Skill Creator

Skill Creator

A skill for creating new skills and iteratively improving them.

At a high level, the process of creating a skill goes like this:

• Decide what you want the skill to do and roughly how it should do it
• Write a draft of the skill
• Create a few test prompts and run claude-with-access-to-the-skill on them
• Help the user evaluate the results both qualitatively and quantitatively
• While the runs happen in the background, draft some quantitative evals if there aren't any (if there are some, you can either use as is or modify if you feel something needs to change about them). Then explain them to the user (or if they already existed, explain the ones that already exist)
• Use the eval-viewer/generate_review.py script to show the user the results for them to look at, and also let them look at the quantitative metrics
• Rewrite the skill based on feedback from the user's evaluation of the results (and also if there are any glaring flaws that become apparent from the quantitative benchmarks)
• Repeat until you're satisfied
• Expand the test set and try again at larger scale

Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress through these stages. So for instance, maybe they're like "I want to make a skill for X". You can help narrow down what they mean, write a draft, write the test cases, figure out how they want to evaluate, run all the prompts, and repeat.

Six standing disciplines apply throughout, because these failure modes ship convincing-looking skills that are wrong:

• Verify before you write. Every technical assertion that enters the skill (endpoint, parameter, command, version, behavior) must trace to something you executed and observed — in this session or an explicitly approved mined one. Can't verify it right now? Either go verify it, or mark it explicitly ("unverified — from memory"). A skill multiplies whatever it contains: verified knowledge compounds, and so do confidently-stated errors. For knowledge skills (content is mostly facts about an external system — API endpoints, parameters, fields, platform behavior), read references/knowledge-skill-grounding.md for the operational version: the authority ladder (observed behavior > machine-readable contract > exercised production code > official docs > memory), evidence-scope annotation, pre-ship doc-example smoke runs, and the audience/Windows portability checklist. A source-grounding audit once found multiple confident contract claims that contradicted evidence already available to the author (methodology Case 9).
• Treat "impossible / not supported" as a hypothesis, not a conclusion. When a capability seems blocked (an API error wall, a tool that won't connect, a format that won't open), exhaust the observation paths — the UI's own network traffic, an alternative channel, a different documented identifier — before writing "the platform doesn't support this" into a skill. Observed behavior outranks speculative request shapes.
• Stand on the field's shoulders — retrieve the domain's established best-practices into context BY DEFAULT, before authoring or optimizing a skill's methodology. A skill's methodology is only as good as the knowledge in your context window, not the knowledge latent in your weights: pretraining is lossy, goes stale, and often is not even activated unless the canonical sources are actually pulled in. So the quality ceiling of what you write is your training data + the user's input — *unless* you deliberately retrieve the subject domain's real prior art. Do it: WebSearch the field's canonical theory / standards / methods, and read any bundled or installed skill in that domain, then fold the load-bearing principles into the skill with attribution. This is a different axis from "Prior Art Research" below — that finds *tools/infrastructure* to reuse; this grounds the *quality of the methodology itself* in the discipline's accumulated science. Make it the default action, not something you wait to be asked for: briefly tell the user which field you're pulling from and let them say "skip," but never ship a methodology capped by your memory plus their prompt when 40 years of the field's public work is one search away. Examples: a data-visualization skill must absorb Cleveland & McGill's graphical-perception ranking and Bertin's visual variables (position/length beat color beat text — measured, not aesthetic); a date/time skill must surface the mature libraries and their canonical pitfalls; a persuasion/negotiation skill must retrieve the established frameworks rather than reinvent them from memory. If the canonical knowledge lives only in your weights and never enters context, you are guessing where you could be citing.
• Preserve before you compress an existing skill. Updating an existing skill is a migration, not a blank-page rewrite. Before the first edit, capture the complete old bundle with the audit tool's snapshot command, or reconstruct it from an explicit Git ref; an arbitrary copy plus a provenance label is not a baseline. Inventory runtime capabilities, trigger contexts, interfaces, references, and eval coverage. Progressive disclosure and concision authorize moving or deduplicating content; they do not authorize silently deleting behavior. After editing, run scripts/audit_skill_regression.py and classify every unmatched old unit. A runtime contract that survives only in evals/, tests, or an unlinked reference is still lost. Do not call the update complete while any candidate is unclassified or any true gap remains unfixed. The same logic governs *reversals*, not just deletions, and covers any prior commitment — not only the ones carrying a date and a name: overturning a decision already made is a proposal, never a side effect. Say it out loud and get it accepted. A silent rewrite is worse than a silent deletion, because it destroys the artifact and the evidence that could have caught it in one move — and it blinds every downstream reviewer (see #5).

• Nothing ships on self-review alone — an independent, fresh-context adversarial pass is a standing step, not a special case. Your "it's good now" judgment runs on the same model that produced the artifact, so it shares the exact blind spots that produced the defect. Measured, not folklore: intrinsic self-correction without external feedback does not reliably improve output and sometimes degrades it (Huang et al., ICLR 2024), and when generator and evaluator share error modes, iterating raises confidence without adding information. More self-checks cannot escape that; only an outside view can. Full procedure — prompt templates, anchor selection, the findings table, worked cases — in references/independent-review-protocol.md; read it before your first pass. The load-bearing rules:
• Independence has two faces, and the second one is the one authors miss. The *reviewer's context* must not be a fork of yours — a fork inherits your blind spots and hands back a "reviewed" stamp. But the *evidence it measures against* must also sit outside the change's blast radius, or it inherits your conclusion: it reads an artifact and a spec that already agree, and reports agreement. Note "not edited this session" is too weak a line — in the reference's controlled case the poisoned record was edited the *previous* day, and three reviewers went blind to a defect they caught in its untouched twin. Rank anchors by how hard they are for *you* to have touched: the user's own transcript words > a git ref predating the work (git log --oneline <ref>..HEAD -- <path>; any commit of yours disqualifies it) > an append-only log (a convention, not an enforcement). Greenfield has no anchor — say so and ask for one rather than reporting a pass you did not run.
• Give it exactly two things: the artifact, and the reader spec. Nothing else — no design rationale, no project background, no "just confirm X is fine," which converts an independent reviewer into a rubber stamp. The reader spec is a *specification*, not rationale, so it cannot rubber-stamp anything; omitting it wastes half the pass, because the reviewer then measures against itself. For a SKILL.md the reader is another agent executing it, and its failure mode is "I don't know which tool to call," not "I don't know this word" — ask which instructions it could not act on. State the spec before the run and never use it afterwards to explain findings away.
• It is ground truth for comprehensibility and for completeness-against-a-corpus; it has no authority over taste. Apply those two directly. Treat "this might be a bug / I'd suggest Y" as a hypothesis and reproduce it yourself first. Never delegate AI-slop or aesthetic judgment — same-model blind spot.
• Enumerate failure *axes*, not content areas — that also decides how many reviewers you run. The axis is *the question you ask*; the area is *the material you read*. Three reviewers covering scenarios, arithmetic, and the diff but all asking "is this coherent?" is one reviewer billed three times. One is the default and frequently sufficient; add one only for an additional axis. The axis self-review is worst at is fidelity — "is this still faithful to commitments already made?" — because the author is the one who moved the commitment, and coherence and fidelity are orthogonal: an artifact can be flawlessly self-consistent while being completely unfaithful to what was decided.
• Leave an artifact, or this is just a warning. Discipline #6 says a check yielding an opinion loses to one yielding an artifact — so this one produces a file too, or it loses to completion-drive exactly when it matters. Write independent-review.md under skill-reviews/<skill-name>/ in your private, git-tracked knowledge repo: the reviewer prompt verbatim (so a later reader can see whether it was leading), the findings with a disposition and reason each (which is what separates legitimate filtering from discarding what hurts), and what could not be checked. If you don't know which repo is your private knowledge repo (or don't have one), say so and ask the user — do not guess a location that lands in either forbidden zone. Two forbidden locations: NOT in <skill-name>-workspace/ (gitignored scratch dirs that get wiped — this file is cross-session review evidence and must survive them) and NOT in any repo that is or may become public or distributed — which normally rules out the reviewed skill's own repo (review content inherently quotes private paths, real names, and project details). Re-review with a *new* agent after a substantive edit — a rule, contract, or number changed, not a typo. From Step 5 onward this file is the evidence the pass happened; its absence means it did not.
• Discipline #4's regression gate is mechanical and complementary: it proves you did not *delete* behavior. It says nothing about whether what you wrote can actually be followed.

• Design the checks you write so they cannot self-certify green. Skills are largely made of checks — gates, checklists, "before you ship" steps — and a check that the executing context can pass *while violating the very rule it encodes* is worse than no check, because it manufactures confidence. Four rules, borrowed from fields that solved this before software:
• The verification must cover every clause of its rule. If the rule says "A + B + C," the evidence must demand proof of A, *and* B, *and* C separately. One confirm line bolted onto a three-clause rule gets satisfied by whichever clause the author already did; the others are invisible. Real case: a report-authoring skill carried a delivery gate whose rule read "options as side-by-side chips + recommendation highlighted + background written as complete, self-sufficient sentences a stranger could follow" — but the evidence line under it asked only for "N decision items, all rendered as chips." The author ran the gate, wrote that evidence, self-certified green, and shipped a page whose labels were single characters with all the context deleted. The rule sat in the file the entire time; the check simply never measured that clause.
• A check that misfires on healthy input is worse than no check. The failure above is a check that passes when it should fail; this is its mirror — a check that fails when it should pass. It is the more expensive one, because it teaches the operator to bypass reflexively (--no-verify, SKIP=1, --force), and once that reflex exists the gate is off for *every* input, including the ones it was built for. So when authoring a fail-closed check, false positives outrank false negatives: missing one real problem costs you that instance, while killing one healthy input costs you the entire gate.

Watch for the tell: the frustration of having hit the same trap repeatedly is itself the risk signal — it is exactly the state in which an author ships a defense that was never calibrated against healthy input. Real case: after stepping on one formatting trap three times in a day, the author added a regex check to a linter; it killed 33 healthy inputs on the project's own corpus and was reverted the same hour. Calibrate before you arm it — run any fail-closed check across real, known-good material and confirm zero false positives; prefer loosening it until it occasionally misses over letting it ever misfire.

• Make each item a falsifiable observation, not a self-assessment. "Background is self-sufficient" cannot be failed by the person who wrote it; "cover the rest of the page, read one card alone, and state what it is deciding" can. Prefer checks that yield an artifact — a command's output, a quoted line, a screenshot — over checks that yield an opinion.
• The same suspicion applies to the checks you *run*, not just the ones you write. The four rules above govern checks that ship inside a skill. But the greps, finds and one-off scripts you use to verify your own work are instruments too, and a wrong instrument reports a clean result just as confidently as a right one. In one 2026-07 session five separate verification commands lied in both directions: a find without -L reported an installed skill's files missing (they were behind a symlink); a grep --exclude-dir=<name> hid a second copy of the very thing being audited; an inverted shell condition raised a false alarm that a removal had not happened; a regex spanning newlines invented 55 "lost quotations"; and a search over two of five files reported two rules missing that were present in the third. Every one of them was believed at first, and every one was caught only by re-running a differently-shaped check.

The fix is the oldest one in experimental practice: run the instrument on a case whose answer you already know before trusting it on the case you don't. Grepping for a string you expect to be absent? First grep for one you know is present, in the same command shape — if that returns 0 too, the command is broken, not the file. This costs one line and converts "I checked" into "I checked with an instrument I calibrated."

Two specific shapes worth memorizing, because both appeared above and both fail *silently*: find does not follow symlinks without -L (and skill installs are frequently symlinks into a source repo), and --exclude-dir matches by basename everywhere in the tree, not just at the path you had in mind.

And there is a second half to this rule that only bites when the check SHIPS: calibrate against the *standard* implementation, not the one on your machine. The instrument rule above keeps *your* conclusion honest; this keeps the *reader's* working. A tool-behavior claim written into a skill — a flag, a recursion mode, an option that "follows symlinks" — is executed on machines whose binaries you have never seen, and the divergence is silent on both ends: it works when you test it, and it quietly does nothing for them. Two mechanisms produce this, and both are invisible from inside a session: the same command name resolves to a different program (a shell alias or function shadowing the binary — note \tool only escapes an *alias*, so command tool or an absolute path is the only deterministic form), and the same program behaves differently across implementations (BSD vs GNU vs a drop-in replacement). Real case (2026-07): an author verified that grep -R follows symlinks, wrote it into a skill as the fix for a symlink trap, and shipped it to a 1200-star public repo — their grep was ugrep via a shell function; on macOS's own /usr/bin/grep the same -R matches nothing (it needs -RS), so the prescribed fix failed silently for most readers, inside the very section warning that validators fail silently.

So: before a tool-behavior assertion enters a shipped artifact, re-run it against the standard binary (/usr/bin/<tool>), not the one your shell hands you. If it does not survive that, do not write the flag — prefer the implementation-independent formulation: resolve the path yourself (readlink -f) instead of betting on a recursion flag, do a substring test in a script instead of a line-oriented match, name the *behavior* you need instead of the option you happen to know. A prescription that only works in your environment is worse than no prescription, because the reader has no way to discover that it silently did nothing.

• Use what mature checklist practice already settled. Decide whether a list is READ-DO (execute while reading — for low-frequency or unfamiliar procedures) or DO-CONFIRM (work from expertise, then stop at a defined pause point and confirm — for experienced operators under time pressure), and anchor it at a real pause point rather than "somewhere in the workflow." Keep it to the killer items — critical *and* commonly missed under pressure, roughly five to nine; everything beyond that dilutes compliance (Gawande, *The Checklist Manifesto*). And prefer Shingo's control over warning (poka-yoke): a prose reminder depends on vigilance and loses to completion-drive, while a step that blocks progress or forces an artifact needs no vigilance at all. Where a skill can only warn, at least put the warning where the decision gets made — a rule filed in a reference the executing context never opens is not, in practice, a rule.

On the other hand, maybe they already have a draft of the skill. In this case you can go straight to the eval/iterate part of the loop.

Of course, you should always be flexible and if the user is like "I don't need to run a bunch of evaluations, just vibe with me", you can do that instead.

Then after the skill is done (but again, the order is flexible), you can also run the skill description improver, which we have a whole separate script for, to optimize the triggering of the skill.

Cool? Cool.

First: coexistence check (official skill-creator plugin)

Before anything else, run one quick check (a single grep, no output needed on the common path): does ${CLAUDE_CONFIG_DIR:-~/.claude}/plugins/installed_plugins.json contain "skill-creator@claude-plugins-official"?

• Not present (the common case): do nothing — do not install anything, do not mention this section to the user. Proceed with the engagement.
• Present: the official plugin's skill-creator and this edition now sit in the skill list with near-identical descriptions, so future sessions will route between them at random. Tell the user this in one or two sentences, then offer (never act without their consent):
• Recommended — run scripts/setup_supersede_hook.sh install. It copies a small self-checking SessionStart hook into their Claude config and registers it in settings.json (with a backup), so every future session deterministically routes skill work to this edition. Reversible with scripts/setup_supersede_hook.sh uninstall; the official plugin stays fully usable when asked for by name. On machines without the official plugin the installer refuses to install anything, so it can never leave a useless hook behind.
• Alternative — claude plugin disable skill-creator@claude-plugins-official (reversible with enable), which removes the ambiguity by taking the official entry out of the skill list entirely.

If the hook is already installed (scripts/setup_supersede_hook.sh status shows the SessionStart entry as present), skip all of this silently.

The same machinery is available for skills the user creates: when their skill deliberately overlaps an installed one, generate them a kit with scripts/generate_supersede_kit.py — see "Coexistence & Precedence" under Prior Art Research and references/skill-precedence-and-coexistence.md.

Communicating with the user

The skill creator is liable to be used by people across a wide range of familiarity with coding jargon. If you haven't heard (and how could you, it's only very recently that it started), there's a trend now where the power of Claude is inspiring plumbers to open up their terminals, parents and grandparents to google "how to install npm". On the other hand, the bulk of users are probably fairly computer-literate.

So please pay attention to context cues to understand how to phrase your communication! In the default case, just to give you some idea:

• "evaluation" and "benchmark" are borderline, but OK
• for "JSON" and "assertion" you want to see serious cues from the user that they know what those things are before using them without explaining them

It's OK to briefly explain terms if you're in doubt, and feel free to clarify terms with a short definition if you're unsure if the user will get it.

Using AskUserQuestion (Critical — Read This)

Use the AskUserQuestion tool aggressively at every decision point. Do not ask open-ended text questions in conversation when structured choices exist. This is the single biggest UX improvement you can make — users juggle multiple windows and may not have looked at this conversation in 20 minutes.

Every AskUserQuestion MUST follow this structure:

• Re-ground: State the skill name, current phase, and what just happened (1-2 sentences). The user may have context-switched away.
• Simplify: Explain the decision in plain language. No function names or internal jargon. Say what it DOES, not what it's called.
• Recommend: Lead with your recommendation and a one-line reason why. If options involve effort, show both scales: (human: ~X min / Claude: ~Y min).
• Options: Provide 2-4 concrete, lettered choices. Each option should be a clear action, not an abstract concept.

Rules:

• One decision per question — never batch unrelated choices
• Provide an escape hatch ("Other" is always implicit in AskUserQuestion)
• Accept the user's choice — nudge on tradeoffs but never refuse to proceed
• Skip the question if there's an obvious answer with no tradeoffs (just state what you'll do)

Creating a skill

Capture Intent

Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step.

Source inventory — always before drafting, with consent boundaries. Inventory the live conversation and existing docs/skills that overlap (see Prior Art Research below). Earlier local session JSONL files are a separate private source: do not open or parse them unless the user explicitly asks to mine history or affirmatively approves that source after you explain what will be read. If approved, fold only relevant prior sessions in through the conversation-mining workflow's redacted extraction; never load raw transcripts into your own context. If not approved, continue from the live conversation and existing project sources without treating the missing history as a blocker.

When mining a conversation (or session transcripts), inventory two kinds of assets — they land in different places. *Knowledge* — endpoints, parameters, pitfalls, decision rules — becomes SKILL.md guidance or references/. *Code the session had to write* — helper scripts, injected snippets, renderers, one-off templates — is a scripts/ candidate: if this session wrote it, the next invocation will have to rewrite it, so parameterize it, sanitize it, and bundle it. A prior distillation captured polished prose but omitted the reusable helpers; the general lesson is to keep both knowledge→references and code→scripts channels in frame.

When the source material is *past* session transcripts (the JSONL files under the Claude Code projects directory) rather than the live conversation, do not load them into your own context — a large transcript can exhaust the window and lose the session. Delegate extraction to subagents instead, with explicit instructions to parse line-by-line with a script, truncate every extracted field, and return only a distilled lessons list — the raw transcript never enters the main context.

First, resolve which DIRECTION this is — before the four questions below. The request may be one of several *opposite* things: build a NEW skill / edit an EXISTING skill / optimize skill-creator itself / or it's not-a-skill-at-all (a one-off task). Guessing wrong wastes the whole session — the research you'd do for "new skill" is the wrong research for "optimize the meta-tool." When the phrasing is ambiguous (e.g. "make me a skill" while pointing at skill-creator's own path), one AskUserQuestion here costs 30 seconds. The wrapper-skill fork below is one special case of this; the direction check is general.

• What should this skill enable Claude to do?
• When should this skill trigger? (what user phrases/contexts)
• What's the expected output format?
• Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art, taste-calibrated reports) often can't use assertions — but "no assertions" is not "no verification". Their verification paths, in order of cost:
• Historical-task replay: re-run one real prompt the skill has served before, old vs new skill, and compare outputs against the specific rules that changed ("does the new output actually follow the tokens / title grammar this update introduced?"). Cheap, catches "the rule was written but nothing reads it".
• Production-as-eval: acknowledge that the real test is the user's next actual use — then make the loop explicit: every user correction afterward is an incident to fold back (the skill's own "迭代/活文档" section), every approval is corpus material. A taste skill that ships without this write-back habit doesn't improve; one that has it converges without ever running a formal eval.
• Render + human review for visual outputs (the skill's own visual-QA gates), never a grep assertion pretending to measure aesthetics. And the renderer you verify with must be the same engine the deliverable will be consumed in — whatever previewer is conveniently installed is not a substitute. A thumbnailer whose layout engine differs from the target application will silently *hide* the exact defects you are looking for, and a green verification on the wrong engine is worse than no verification, because it buys false confidence. Real case (2026-07): a .docx was "visually verified" through macOS Quick Look thumbnails, which do not reproduce justified-text stretching; Word showed the document's info blocks blown apart the moment the user opened it. The fix was to install the Word-compatible engine (LibreOffice), convert to PDF, rasterize per page, and read every page. Match the engine, or the verification is theater. This generalizes past renderers to every verification tool — parser, linter, validator: it must share an implementation with production, or its green is meaningless. Second case, same shape: an author tried to catch a markup pattern that corrupts the final document by checking at the source stage with a *different* markdown implementation than the production toolchain used — it parsed all three known-bad inputs as perfectly fine, so any pre-check built on it would have silently passed everything. The honest conclusion was that this particular defect is only detectable after the production tool has run, and the check belongs there. When no available tool shares the production implementation, say the check cannot be done at that stage — do not build the one that can only produce false green.

Suggest the appropriate default based on the skill type, but let the user decide.

After extracting answers from conversation history (or asking questions 1-3), use AskUserQuestion to confirm the skill type and testing strategy:

Creating skill "[name]" — here's what I understand so far:
- Purpose: [1-sentence summary]
- Triggers on: [key phrases]
- Output: [format]

RECOMMENDATION: [Objective/Subjective/Hybrid] skill → [suggested testing approach]

Options:
A) Objective output (files, code, data) — set up automated test cases (Recommended if output is verifiable)
B) Subjective output (writing, design) — qualitative human review only
C) Hybrid — automated checks for structure, human review for quality
D) Skip testing for now — just build the skill and iterate by feel

This upfront classification drives the entire evaluation strategy downstream. Get it right here to avoid wasted effort later.

The extend-vs-create check — runs BEFORE any specialized branch

Each of the three specialized workflows below ends with "do not continue reading the sections below", and *Prior Art Research* happens to sit after them. That ordering is layout, not execution order. The extend-vs-create judgment applies to every branch, and skipping it is exactly how a session ships a skill that duplicates one already installed.

So before routing into wrapper-skill / conversation-mining / artifact-corpus, answer one question: does a skill already exist that this capability belongs to?

Discover the roots, don't recall them. A hand-maintained list of install locations is exactly the artifact that goes stale, and the root you forget is the one that bites.

Search for the file, not for a directory named skills. Skill directories are named after the *skill* (skill-creator/, <suite>/<skill>/), so a source repo, a marketplace clone and a plugin cache contain no directory called skills at all — searching for that name silently skips them while appearing to work. Every skill has a SKILL.md; that is the layout-agnostic handle.

# 1) discover
find ~ -type f -name SKILL.md -not -path '*/node_modules/*' -not -path '*/.git/*' > /tmp/all-skills.txt

# 2) VERIFY COVERAGE BEFORE TRUSTING IT — `2>/dev/null` and permission denials hide gaps
#    silently, which is exactly how a sweep reports "nothing found" from a root it never
#    entered. A 0 on any line you expect means the search did not go there:
for r in '/.claude/skills/' '/plugins/marketplaces/' '/plugins/cache/' '/.claude-profiles/'; do
  printf '%6s  %s\n' "$(grep -c "$r" /tmp/all-skills.txt)" "$r"
done
# ...and grep for your own skill source repos by path; they must appear too.

# 3) filter by capability VOCABULARY, not by skill name — in every language the target
#    skill might be written in (a skill whose body is Chinese will not match English terms):
xargs grep -li -e '<domain-term>' -e '<域内术语>' < /tmp/all-skills.txt

Expect step 3 to take a few seconds and to still return more than you want; narrow with terms specific to the capability rather than generic ones (chart matches everything, stacked bar does not).

The roots this reaches — and that a from-memory list usually misses: the skill source repos (a claude-code-skills checkout and any -pro sibling), ~/.claude/plugins/marketplaces/ and ~/.claude/plugins/cache/ (marketplace-installed suites — nothing in the source repos hints they are there), ~/.claude/skills/, ~/.codex/skills, ~/.agents/skills, per-profile config homes (~/.claude-profiles/<name>/), and — the one with no signposts at all — every project's own .claude/skills/. Step 2 is what makes that a claim you verified rather than one you inherited.

Per-project skills are structurally invisible. They live inside an unrelated project's working tree, so they appear in no marketplace, no global skill list, and no source-repo listing; nothing you would normally open while planning a new skill mentions them. Real case (2026-07): a session built a global skill for a domain, swept the source repos, the global dirs and the other-agent dirs, found nothing, and shipped. A later conversation-history search turned up a mature project-level skill for that exact domain, a month old, sitting in one project's .claude/skills/ — carrying eight rules the new skill lacked, including one the user had personally dictated. Every root had been checked except the per-project one, and the sweep reported "no prior art" with complete confidence.

What to do when the overlap *is* a project-level skill in an unrelated project — the case that war story lands you in, and the one the three bullets below do not cover: you cannot add a sibling to a suite it has none of, and "extend it" would mean editing an unrelated project's working tree. The move that worked: harvest its rules into the skill you are building, then retire the project-local one with the owner's consent — it was written against real work, so treat it as the more mature source and reconcile *toward* it. Retiring someone's working skill is the owner's decision, not a side effect of your build.

Four things that sentence leaves out, each of which will stop you:

• "Reconcile toward it" is a rebuttable presumption, not a rule. Two standard exits: the project skill may be *stale* (rules written months ago against a system that moved), and it may be *project-specific* (rules that only hold under that project's constraints — importing them wholesale makes your skill narrow, which this file elsewhere tells you not to do). Harvested rules are another author's memory, so re-verify each one the way discipline #1 requires of anything you write into a skill.
• "Retire" needs a mechanism, and its first step is not the one you reach for. In order:

• find the skill's *bodies*, before grepping for its *references*. A skill routinely has more than one copy in the same repo — .claude/skills/<name>/ and .agents/skills/<name>/ are both loaded, by different tools, from the same working tree. Grep answers "who mentions it"; only find answers "how many of it are there".

     find <project> -type d -name '<skill-name>' -not -path '*/.git/*'

Do not put the skill's own name in --exclude-dir (it matches by basename, so it hides every same-named directory including the copy you have not found — see the instrument rule in discipline #6). Real case (2026-07): a retirement did exactly that, fixed all five references it found, and left a second full copy under .agents/skills/ — git-tracked, no retirement marker, a stale snapshot missing the newest rule — which the other tool would still load as live.

• Verify the new home is actually reachable from where the old one was, *before* deleting anything — a marketplace skill you just pushed is not installed until the marketplace is updated and the plugin installed, and retiring first leaves a window with neither:

     claude plugin marketplace update <marketplace>   # your push is not their cache
     claude plugin install <skill>@<marketplace>
     find -L ~/.claude/plugins/cache -path '*<skill>*' -name '*.md'   # -L: installs are often symlinks

The -L is not optional — plugin caches frequently symlink into a source repo, and a bare find reports the files missing (see the instrument rule in discipline #6).

• Then grep for references and repoint the live ones. Distinguish live instructions (a skill list, a cross-reference, a handoff doc telling the next agent what to use) from historical records (a decision log entry saying "on date X we shipped this") — rewriting the second destroys an audit trail to fix a problem it does not have.
• Then replace each body with a superseded by <skill> stub rather than a bare deletion, and make every copy's stub byte-identical. Keep the YAML frontmatter — a SKILL.md without it may fail to load rather than fail informatively — but rewrite the description so the stub announces its own retirement instead of advertising the old triggers; otherwise it keeps winning the routing it no longer serves. The body needs only: where the capability went, and one line on why it moved.
• Identify the owner with the ownership test below (it applies here too: a project-level skill has no marketplace.json, but the project's git remote still tells you whose it is). When the owner is the person you are talking to, "consent" is one AskUserQuestion. When the owner is unreachable, harvest only — do not retire (which leaves both skills live and competing for the trigger, same end state as a declined retirement — see Coexistence & Precedence below).
• If the owner declines to retire it, you now have two skills competing for the same trigger; that is the Coexistence & Precedence problem below, not a failure.

Search by capability vocabulary, not by skill name. That project skill would not have matched a name search for the new skill's title; it matched on the domain terms inside its body. Grep the candidate roots for the *concepts* the new skill will handle.

If something overlaps:

Deciding which bullet applies — whose skill is it? A filesystem hit does not carry ownership. Read the marketplace's .claude-plugin/marketplace.json owner field, or git remote -v in the containing repo; a hit under ~/.claude/plugins/marketplaces/ can just as easily be your *own* marketplace installed back onto your machine. A project-level skill has no marketplace.json, but its project's git remote answers the same question.

Two cases the probes get wrong or cannot answer, so check for them before trusting the result: a fork shows your own remote while the content is someone else's — treat it as third-party, because their upstream improvements still stop reaching you. And when there is no marketplace.json and no remote (a local-only project, a skill hand-copied into a global skills dir), the probes are silent rather than negative: ask the owner instead of guessing.

• The overlap is a third party's skill (a marketplace suite, an official plugin): do not re-implement its capability. Write a *thin increment* that drives it correctly — the pitfalls you hit, the correct invocation, the verified helper script — and reference it by namespaced name. Cloning someone else's engine into your bundle is the expensive mistake: their upgrades stop reaching you, and the two copies drift apart silently.
• The overlap is your own skill: extend it, or add a sibling inside its existing suite. A standalone that competes for the same triggers helps nobody. Exception: if it lives inside an *unrelated project's* working tree, neither move applies — see the project-level case above.
• Some related skill already points at the gap you're filling (e.g. its description says "for X, use Y"): after you build, close the loop — update that pointer, or you have left a dangling reference behind.

Only when nothing overlaps do you build standalone.

Why this check earns its place at the top: a real 2026-07 session spent a day getting a third-party docx engine to produce correct Chinese business documents, then reached for the wrapper-skill branch — which skips straight past Prior Art Research. The shape it was about to ship was a fresh skill re-carrying that engine's capability. The correct shape was a three-layer reference chain: third-party engine untouched → a thin increment skill holding the correct usage plus the verified generator script → the domain-workflow skill calling that increment. The user had to catch it twice before it landed, with the second correction being the sharper one: *"don't copy an extra one — write the correct usage on top of theirs, and reference their skill; that's what skill-as-code means."*

Specialized Workflow: Wrapper Skills for Third-Party CLI Tools

Before committing to the generic skill-creation flow, check whether the session that led up to this point actually calls for the wrapper skill workflow instead. A wrapper skill is a companion that installs, configures, diagnoses, and repairs a pre-existing third-party CLI tool or skill package — code that someone else wrote and that the user has just spent a session getting to work on their machine.

Signals this applies (any two together are enough):

• The user has been installing a tool in the current conversation — downloading a .zip, running npx / pip install / brew install, dealing with an official installer.
• The session has produced real, concrete error messages and the user and Claude have worked out concrete fixes for them (edited files, added flags, bypassed aliases).
• The user says something like "wrap this up as a skill", "save this as a wrapper skill", "so other people don't have to go through this again", "把这次 session 做成一个 skill".
• The user explicitly mentions a third-party tool by name and wants other agents or other people to be able to use it without the learning curve they just paid.

Signals it does not apply (use the generic workflow above instead):

• The user wants a skill for something they're going to write from scratch.
• The session was smooth — no real friction to capture.
• The skill would wrap a service the user owns or controls (it's their code; edit the source instead of wrapping it).
• The "tool" is actually a methodology or workflow that doesn't involve installing any binary or package.

When the wrapper skill workflow applies, do not continue reading the sections below. Jump to workflows/wrapper-skill/workflow.md and follow that workflow end-to-end. It is a retrospective distillation workflow — its job is to mine the current conversation for the install flow, the bugs that were fixed, and the design decisions that were made, and to turn that mining output into a complete, self-contained wrapper skill that another user can install and benefit from without reliving the debugging session.

The wrapper skill workflow has its own architecture contract, code templates, and verification protocol — it does not share test-case infrastructure with the generic workflow, because its output is a user's install state rather than a file that can be easily asserted on. The canonical reference implementation is the ima-copilot skill (at the root of the daymade/claude-code-skills repository — a bare relative link here already broke once when this skill moved into a suite, exactly as the cross-skill-reference rule below warns), a wrapper around the Tencent IMA skill distilled from a real session using this exact workflow.

Specialized Workflow: Enrich a Skill from Conversation History

Before committing to the generic skill-creation flow, check whether the session is actually asking to distill past conversations into a skill. This is useful when the user has been debugging, designing, or exploring a topic over multiple Claude Code / Codex sessions and wants to turn the accumulated know-how into reusable references/.

Signals this applies (any one is enough):

• The user says something like "mine my chat history for patterns", "turn this conversation into a skill reference", "distill what we learned into the skill", "enrich this skill from my conversations", or "把这次对话沉淀到 skill 里".
• The session is explicitly about extracting lessons from a recent multi-turn debugging or design session.
• The user wants to add a references/ file to an existing skill based on real conversations they have already had.
• The target skill already exists, and the goal is to enrich it with conversation-mined knowledge rather than build it from scratch.

Signals it does not apply (use the generic workflow above instead):

• The user is creating a brand-new skill from a single prompt or idea.
• The user wants a wrapper around a third-party CLI tool they just installed (use the wrapper-skill workflow above).
• There is no local conversation history to mine and no transcript exports to process.
• The mined content is one-time personal notes that should live in memory/ rather than a reusable reference file.
• The source material is a batch of finished artifacts the user has endorsed, rather than dialogue — use the artifact-corpus-distillation workflow below.

When the conversation-mining workflow applies, do not continue reading the generic sections below. Jump to workflows/conversation-mining/workflow.md and follow that workflow end-to-end. It is a retrospective distillation workflow: it discovers local Claude Code project sessions, Codex transcripts, and command histories, redacts them, partitions them into agent-sized chunks, runs mining agents, and promotes the resulting candidate references into the target skill's references/ after validation.

The conversation-mining workflow has its own architecture contract, agent prompts, templates, and verification protocol. It is the canonical way to turn real conversation history into a skill's reusable knowledge base.

Specialized Workflow: Distill User Preferences from an Approved-Artifact Corpus

Before committing to the generic flow, check whether the session is asking to extract the user's real preferences from a batch of finished artifacts they have endorsed — approved HTML report pages, generated documents, designs. This is the third distillation source, distinct from the two above: the material is products, not conversations, and the output is taste made executable (explicit principles, quantified parameters, vocabulary), not knowledge or install fixes.

Signals this applies (any one is enough):

• The user lists finished artifacts and says "这些都是我认可的样例" / "你来学到底什么是我想要的" / "extract my preferences from these approved examples".
• A taste-calibration skill (report generator, doc styler, deck builder) has an approved-sample corpus that keeps growing, and the user asks to make the skill *learn* from it rather than just index it.
• The user complains that a previous update "只加了示例" — only cataloged samples without changing skill behavior.

Signals it does not apply: the source material is dialogue/corrections rather than endorsed products (use conversation-mining); the samples are not personally approved by the user (approval is the admission gate — ask first).

When it applies, jump to workflows/artifact-corpus-distillation/workflow.md. Its core discipline, which also applies any time you add material to an existing skill: cataloging ≠ distillation — registering a sample in a corpus table changes nothing about the skill's next run; ask of every addition "*does this change a decision rule?*", and do not declare a distillation session done while the answer is no for everything written (methodology Case 15). The workflow's spine: script-extracted quantitative comparison across ALL artifacts (≥3-artifact threshold per pattern, checked exception lists per claimed constant) → layered induction with evidence anchors → write to the decision-rule layer (separating invariants from register-dependent variables) → independent completeness audit (standing discipline #5) → regression audit.

Prior Art Research (Do Not Skip)

The user's private methodology — their domain rules, workflow decisions, competitive edge — is what makes a skill valuable. No public repo can provide that. But the user shouldn't waste time reinventing infrastructure (API clients, auth flows, rate limiting) when mature tools exist. Prior art research finds building blocks for the infrastructure layer so the skill can focus on encoding the user's unique methodology.

Two axes, don't conflate them. This section sources the *infrastructure* layer (tools / MCPs / libraries / existing skills to reuse). The *methodology* layer has two inputs of its own: the user's private edge (theirs alone, un-retrievable) and the domain's established best-practices / science, which you retrieve into context by default per standing discipline #3. Finding the right tool does not discharge the second — a viz skill that adopts a charting library but never absorbs Cleveland/Bertin is still capped at your pretraining. Do both.

Search these channels in order (use subagents for 4-8 in parallel):

| Priority | Channel | What to search | How |

|----------|---------|---------------|-----|

| 1 | Conversation history | User's proven workflows, verified API patterns, corrections made during debugging | Grep recent conversations for the service/API name |

| 2 | Local documents & SOPs | User's private methodology, runbooks, existing skills | Search project directory, ~/.claude/CLAUDE.md, ~/.claude/references/ |

| 3 | Installed plugins & MCPs | Already-integrated tools | Check ~/.claude/plugins/, parse installed_plugins.json; check ~/.claude.json for configured MCP servers |

| 4 | skills.sh | Community skills | WebFetch https://skills.sh/?q=<keyword> |

| 5 | Anthropic official plugins | Official/partner plugins | WebFetch https://github.com/anthropics/claude-plugins-official/tree/main/plugins and external_plugins directory |

| 6 | MCP servers on GitHub | Existing MCP servers for the same API | WebSearch "<service-name> MCP server site:github.com" |

| 7 | Official API docs | The target service's own documentation | WebSearch "<service-name> API documentation" or WebFetch the docs URL |

| 8 | npm / PyPI | SDK or CLI packages | npm search <keyword> or curl https://pypi.org/pypi/<name>/json |

Channels 1-3 surface the user's own proven patterns and existing integrations. Channels 4-8 find public infrastructure. The user's private SOP always takes precedence — public tools are building blocks, not replacements. In competitive domains (finance, trading, proprietary operations), the valuable methodology will never be public.

Bias toward merge/extend over create-new, and sweep EVERY skill root — not just ~/.claude. When channels 1-3 turn up an existing skill that overlaps the requested domain, the usual right move is to extend or merge into it — except when it lives in an unrelated project's working tree, where the direction reverses: harvest *from* it into the skill you are building rather than merging *into* it (see the project-level case in the extend-vs-create check above) (one real "new skill" task became "make the existing extractor the extract-phase of the new archiver"), not to ship a parallel skill that competes for the same triggers — two overlapping skills fight over triggering and confuse users. When searching, discover the install roots rather than recalling a list — use the SKILL.md sweep and its coverage self-check from the extend-vs-create section above (searching for a directory named skills misses source repos, marketplace clones and plugin caches entirely, because their skill directories are named after the skill). Run the coverage check rather than trusting this sentence: every project's own .claude/skills/ is the root a from-memory list reliably drops, because nothing outside that project references it. A skill the user already installed *anywhere* is the strongest prior art there is, and a project-local one is often the most mature: it was written against real work.

If a public MCP server or skill is found, clone it and verify — don't trust the README:

• Read the actual source code — many projects have polished READMEs on hollow codebases
• Verify auth method — does it match how the API actually authenticates? (X-Api-Key headers vs Bearer vs OAuth — many get this wrong)
• Check test coverage — zero tests = prototype, not production-grade
• Check maintenance — last commit date, open issue count, response to bug reports
• Check environment compatibility — proxy/network assumptions, hardcoded DNS/IPs, region locks
• Check license — MIT/Apache is fine; GPL/SSPL may conflict with proprietary use
• Check dependency weight — huge dependency trees create conflict and security surface

Decision matrix:

| Finding | Action |

|---------|--------|

| Mature MCP/SDK handles the infrastructure | Adopt it, build on top — install the MCP, then build the skill as a workflow layer encoding the user's methodology |

| Partial MCP or SDK exists | Extend — use for infrastructure, fill gaps in the skill |

| Public skill covers the same domain | Use for structural inspiration only — public skills in competitive domains are generic by definition. The user's edge is their private SOP |

| Complementary skill exists that provides a sub-capability of what you're building | Bundle it — copy the complementary skill's self-contained assets into your bundle and wire them up. Do NOT rely on the user having it pre-installed. See "Complementary Skills" below |

| Nothing public exists | Build from scratch — validate API access patterns work (auth, endpoints, proxy) before writing the full skill |

| Integration cost > build cost | Build it — a 2-hour custom implementation you own beats a "mature" tool with integration friction and upstream risk |

| User deliberately supersedes an installed skill (fork, hardened edition) | Ship it with a supersede kit — see "Coexistence & Precedence" below |

Coexistence & Precedence (deliberate overlap)

Merging into the existing skill is the default fix for overlap (above). But when the user *deliberately* ships a skill that overlaps an installed one — a fork of an official plugin, a hardened in-house edition — the two entries will sit in the skill list with similar descriptions and Claude will route between them at random. Resolve it, in escalating order: rename if the overlap is accidental; add a description tiebreaker ("supersedes X — when both appear, always use this one"); and for distributed forks, stamp a conditional supersede kit into the skill with scripts/generate_supersede_kit.py — a consent-based SessionStart routing hook that only ever installs on machines where the competitor is actually present, refuses to install elsewhere, and self-disables if either side disappears. Mechanics, decision table, SKILL.md sample wording, and sandbox verification: references/skill-precedence-and-coexistence.md. This skill dogfoods the same kit against the official skill-creator plugin (see "First: coexistence check" at the top).

The more common case: your new skill silently loses the trigger to the *installed population*, without any deliberate fork. A skill's domain (image generation, PDF handling, dashboards) is often already crowded with several installed skills, and a fresh skill can lose auto-routing to all of them. So verify triggering early — the build isn't done when the content is good. After a draft exists, fire a few realistic queries through claude -p and check the new skill actually WINS; if it doesn't, name the specific competitor it lost to (different queries often lose to different skills). Then know two things: (1) prose can't always win a crowded slot — the resolution ladder is rename → description tiebreaker/SUPERSEDES → manual invocation → SessionStart routing hook (structural; modifies global config, so requires the user's explicit consent, same discipline as --no-verify); and (2) the fix depends on who authored the competitor — competitors that are *third-party* → accept manual invocation or a routing hook; competitors that are *your own* → merge/consolidate them into one, don't keep two of your skills fighting for the same trigger. (The full resolution ladder lives in references/skill-precedence-and-coexistence.md — that file is the SSOT; the summaries here and above are pointers, don't extend them independently.) Documenting the chosen path (e.g. an "Activation" note saying "invoke manually, competitors are third-party") stops the next session from re-litigating it. (methodology Case 13)

Complementary Skills (bundle, don't depend)

When building a skill that touches a domain with an existing complementary skill, you have two choices:

• Depend on it being installed: fragile — the user may not have it, or may have a different version. Every missing-dependency failure traces back to this choice.
• Bundle it: copy the complementary skill's self-contained assets (scripts, templates, reference docs) into your own bundle, and wire them up so your skill works standalone.

Rule: if a sub-capability your skill needs is provided by another installable skill, bundle it. This is especially important for:

• Statusline / UI rendering scripts (e.g., statusline-generator's generate_statusline.sh)
• Shared validation / sanitization scripts
• Common data transformation utilities

Example: claude-switch-models-setup manages multiple Claude Code profiles. Each profile needs a statusline. The statusline-generator skill provides generate_statusline.sh. Rather than depending on the user running statusline-generator first, the profile setup skill bundles statusline.sh and wires it into each new profile during claude-profiles-init. The two skills remain independently useful, but the wrapper skill works standalone.

Anti-pattern: writing "run other-skill's installer first" in your SKILL.md. That pushes the dependency to the user and creates a fragile install order. Bundle instead.

After research completes, present findings via AskUserQuestion:

Research complete for "[skill-name]". Here's what I found:

[1-2 sentence summary of what exists publicly]

RECOMMENDATION: [ADOPT / EXTEND / BUILD] because [one-line reason]

Options:
A) Adopt [tool/MCP X] for infrastructure, build methodology layer on top (Recommended)
B) Extend [partial tool Y] — use what works, fill gaps in the skill
C) Build from scratch — nothing found matches well enough
D) Show me the detailed findings before I decide

When in doubt, bias toward adopting mature infrastructure for the plumbing layer and building custom logic for the methodology layer — that's where the value lives.

Interview and Research

Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.

Check available MCPs - if useful for research (searching docs, finding similar skills, looking up best practices), research in parallel via subagents if available, otherwise inline. Come prepared with context to reduce burden on the user.

Write the SKILL.md

Based on the user interview, fill in these components:

• name: Skill identifier
• description: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. All "when to use" info goes here, not in the body. Note: currently Claude has a tendency to "undertrigger" skills -- to not use them when they'd be useful. To combat this, please make the skill descriptions a little bit "pushy". So for instance, instead of "How to build a simple fast dashboard to display internal Anthropic data.", you might write "How to build a simple fast dashboard to display internal Anthropic data. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'"
• compatibility: Required tools, dependencies (optional, rarely needed)