Working With Skills

Working with PostHog skills

This skill teaches agents how to use the llma-skill-* MCP tools well — minimum

context, minimum round-trips, minimum mistakes. If you are not yet familiar with

the tool surface itself, read the skills-store skill first for the catalog.

This document is about _how to choose between the tools_ and _how to scale the

workflow_ when skills get big.

Operating principles

• Progressive disclosure is non-negotiable. Lists return descriptions, get

returns body + manifest, file-get returns one file. Never preload bundled

files "just in case" — every preloaded script is wasted context for the

actual task.

• Pick the smallest write primitive that does the job. A targeted edits

or file_edits is cheaper, safer, and clearer in version history than a

full body or full bundle replacement.

• Reads are cheap; concurrent overwrites are not. Always have a recent

version from llma-skill-get (or from the response of the previous write)

before calling any write tool, and pass it as base_version.

• Authoring follows the Agent Skills spec.

Keep name kebab-case, descriptions trigger-rich, body short, bulky

material in bundled files.

Decision tree: which tool do I call?

Need to know what's available?
  └─► llma-skill-list                    (names + descriptions only)

Need to use / inspect a specific skill?
  └─► llma-skill-get                     (body + file manifest, NO file contents)
        └─► llma-skill-file-get          (one file, on demand, only as referenced)

Authoring a brand new skill?
  └─► llma-skill-create                  (body + all initial files in one call)

Editing an existing skill?
  ├─ Body change?
  │    ├─ Substantial rewrite ............. update(body=...)
  │    └─ Surgical tweak .................. update(edits=[{old, new}, ...])
  ├─ Bundled file content change?
  │    └─ update(file_edits=[{path, edits:[...]}, ...])
  ├─ Add / remove / rename a file?
  │    ├─ Add ............................. llma-skill-file-create
  │    ├─ Delete .......................... llma-skill-file-delete
  │    └─ Rename .......................... llma-skill-file-rename
  └─ Wholesale bundle reset (rare!) ....... update(files=[...])  # replaces ALL files

Want a fork as the starting point?
  └─► llma-skill-duplicate               (then update the copy)

Done with a skill entirely?
  └─► llma-skill-archive                 (hides ALL versions; cannot be undone)

If you find yourself reaching for update(body=...) plus a sprawling files=[...]

to change one paragraph and one script, stop — that's two narrower calls

(update(edits=[...]) plus update(file_edits=[...])) or even a single

update carrying both edits and file_edits.

Discover before you fetch

posthog:llma-skill-list
{ "search": "fractal" }

llma-skill-list is the right tool to "find a skill" — it returns names and

descriptions only. Reading the descriptions is the entire point: pick the right

skill before pulling any body. If search doesn't narrow it enough, list

without it and scan, but do not start fetching candidate bodies blindly.

llma-skill-get should be called once per skill per task, not per question.

Cache the body in your working memory; fetch again only if you suspect the

skill changed under you (e.g. a 409 on write — see "Concurrency" below).

Reading a large skill efficiently

Big skills (long body, many bundled files) are the case where lazy loading

matters most.

• llma-skill-get(skill_name=...) — read body + files[] manifest.
• Scan the body's table of contents / headings. The body should already tell

you which file goes with which task — that's why bodies stay short and

reference files by path.

• For each file the body explicitly points at for _the current task_, call

llma-skill-file-get(file_path=...). Skip everything else.

• If the body references "see scripts/X for the rare case Y" and you are not

in case Y, do not fetch scripts/X.

When in doubt, fewer files. You can always fetch one more on the next turn.

Authoring a new skill

Use a single llma-skill-create call with body and initial files — the

skill lands at version: 1 complete. Do not create the skill empty and then

make N follow-up llma-skill-file-create calls; that's N extra versions and N

extra round-trips for no benefit.

posthog:llma-skill-create
{
  "name": "my-skill",
  "description": "What it does AND when to use it. Include trigger keywords.",
  "body": "# my-skill\n\n## When to use\n...\n## Workflow\n...",
  "license": "MIT",
  "compatibility": "Requires Python 3.10+",
  "allowed_tools": ["Bash", "Write"],
  "metadata": { "author": "me", "category": "..." },
  "files": [
    { "path": "scripts/foo.py", "content": "...", "content_type": "text/x-python" },
    { "path": "references/primer.md", "content": "...", "content_type": "text/markdown" }
  ]
}

Authoring rules of thumb

• description is the discovery surface. It is the only thing

llma-skill-list returns. Make it trigger-rich (what the user might say) and

scope-honest (what the skill does and does not do).

• name — kebab-case, max 64 chars, no leading/trailing/consecutive

hyphens. The spec validator rejects anything else.

• Body ≤ ~500 lines. Long preambles, exhaustive SQL, full example payloads,

and runnable code belong in references/, assets/, or scripts/. The body

should _route_ to those files, not inline them.

• File layout convention — scripts/ for executable code, references/

for prose docs and examples, assets/ for templates / data. Agents can rely

on this for orientation when they only have the manifest.

• allowed_tools lists the MCP / built-in tools the skill expects to be

callable. Be honest — under-declaring causes silent failures, over-declaring

is a security smell.

Updating an existing skill

The single most common mistake is using update(body=..., files=[...]) for a

small change. That works, but it round-trips the entire skill, makes the diff

unreadable in version history, and risks dropping files if files was

incomplete. Use the smallest primitive instead.

Always read first, capture version

posthog:llma-skill-get
{ "skill_name": "my-skill" }

Note the returned version — pass it as base_version on every write. After a

successful write, the response contains the new version; chain further writes

with that.

Body: full replacement vs incremental edits

Full replacement when you are restructuring the body:

posthog:llma-skill-update
{ "skill_name": "my-skill", "body": "# my-skill\n\nNew body...", "base_version": 7 }

Incremental edits when you are tweaking a few lines (preferred for small

changes — easier to review, lower error surface):

posthog:llma-skill-update
{
  "skill_name": "my-skill",
  "edits": [
    { "old": "Use Pillow for rendering.", "new": "Use Pillow ≥10.0 for rendering." },
    { "old": "## Old section title", "new": "## New section title" }
  ],
  "base_version": 7
}

Each edits[].old must match exactly once in the current body, and body and

edits are mutually exclusive in one call.

Bundled file content edits

file_edits patches one or more existing files in place — non-targeted files

carry forward unchanged. This is the right primitive when you are tweaking

script logic or fixing a typo in a reference doc:

posthog:llma-skill-update
{
  "skill_name": "my-skill",
  "file_edits": [
    {
      "path": "scripts/foo.py",
      "edits": [{ "old": "ITERATIONS = 100", "new": "ITERATIONS = 250" }]
    },
    {
      "path": "references/primer.md",
      "edits": [{ "old": "## Outdated header", "new": "## Updated header" }]
    }
  ],
  "base_version": 7
}

file_edits cannot add, remove, or rename files — only patch

existing ones. For structural changes, use the per-file tools.

Combining edits in a single call

You can combine edits (body) and file_edits (existing files) in one

llma-skill-update call to publish a single coherent version when a change

spans both:

posthog:llma-skill-update
{
  "skill_name": "my-skill",
  "edits": [{ "old": "## Configuration", "new": "## Setup" }],
  "file_edits": [
    { "path": "scripts/run.py", "edits": [{ "old": "DEBUG = False", "new": "DEBUG = True" }] }
  ],
  "base_version": 7
}

File-path parameter naming (read this before guessing)

The same concept — a bundled file's path — is named differently depending on

where it travels in the request, and this trips up agents working from

memory. There is one rule:

• file_path — when the path is part of the URL (llma-skill-file-get,

llma-skill-file-delete). These read/delete one file addressed by its path.

• path — when the path is a body field: llma-skill-file-create, the

files=[{path, content, content_type}] array, and file_edits=[{path, edits}].

• old_path / new_path — body fields on llma-skill-file-rename.

Mnemonic: path is the field name on a file _object_ (it sits next to

content), so everything that carries a file object uses path; the two

tools that address a file by URL use file_path. When unsure, check the

tool's input schema rather than guessing — passing path to file-get yields a

/files/undefined/ 404.

Adding, removing, renaming files

Each is its own call, each publishes a new version:

posthog:llma-skill-file-create
{ "skill_name": "my-skill", "path": "scripts/julia.py", "content": "...", "base_version": 7 }

posthog:llma-skill-file-delete
{ "skill_name": "my-skill", "file_path": "scripts/old.py", "base_version": 8 }

posthog:llma-skill-file-rename
{ "skill_name": "my-skill", "old_path": "scripts/julia.py", "new_path": "scripts/julia_set.py", "base_version": 9 }

llma-skill-file-rename is a true move — it carries the existing content

forward without resending it. Always prefer it over delete + create when the

content is unchanged.

When to use update(files=[...]) (rare)

Passing files to llma-skill-update replaces the entire bundle —

anything not in the array is dropped. This is the right tool only when you are

intentionally wiping and reseeding the bundle (e.g. importing a fresh local

SKILL.md tree). For almost every other case, prefer file_edits plus per-file

CRUD.

Working with large multi-file skills

Skills with many files (10+) require extra discipline:

• Treat the manifest as the index. llma-skill-get's files[] is your map.

Match each task step to one file and fetch only that one.

• Group structural changes into a sequence, not a fork. If you are renaming

three files, do them sequentially: rename → rename → rename, each chained

via the previous response's version. That gives you three small reviewable

versions instead of one giant update(files=[...]) blob.

• Keep edits localised. A single llma-skill-update with file_edits

targeting five files is fine. A single update(files=[...]) carrying ten

full file bodies is almost always a sign you should have used file_edits.

• Refactor the body itself first. If the body has grown past ~500 lines,

the right next step is usually to split content into new bundled files

before adding more material.

Concurrency: base_version

Every write tool accepts base_version. Always pass it.

• The server compares base_version to the current latest version. If they

match, the write succeeds and the new version is base_version + 1.

• If they differ, the write is rejected (someone else updated the skill).

Re-run llma-skill-get, reconcile your changes against the new body, and

retry with the fresh version.

• After a successful write, the response includes the new version. Chain

further edits with that — do not re-get between back-to-back writes you

control.

Skipping base_version does _not_ speed things up — it just turns a clean

"someone else won the race" error into a silent overwrite of their work.

Common pitfalls

• Calling llma-skill-list with no search and then fetching every body —

defeats progressive disclosure. Read the descriptions first.

• Pre-fetching every bundled file after llma-skill-get — same mistake on

the inner level. Fetch on demand from the body's directives.

• Using update(body=..., files=[...]) for a one-line fix — round-trips

the entire skill, makes diffs unreadable, and risks dropping files. Use

edits / file_edits.

• Using update(files=[...]) when you meant to add one file — drops every

file you didn't include. Use llma-skill-file-create instead.

• Delete + create instead of rename — loses content history and costs an

extra version bump.

• Stale base_version after chained writes — read the version from the

previous write's response, not from your initial get.

• Leaving base_version off — accepts a silent overwrite. Always include

it once you've done a get.

• Empty / vague description — the skill becomes effectively undiscoverable

via llma-skill-list search. Treat the description as the trigger contract.

• Long body + no bundled files — when a body crosses ~500 lines, refactor

into references/ and scripts/ rather than letting it grow.

• Mixing body and edits in one update call — they're mutually exclusive.

Pick one.

• Guessing path vs file_path — file-get and file-delete take file_path

(it's in the URL); create, rename (old_path/new_path), files, and

file_edits take path (it's a body field). See "File-path parameter

naming" above.

Archiving a skill

llma-skill-archive hides every active version of a skill by name. It is

not version-scoped and cannot be undone — the skill drops out of

llma-skill-list and llma-skill-get for the whole team.

posthog:llma-skill-archive
{ "skill_name": "my-skill" }

Before archiving, llma-skill-get the skill if you need to inspect or copy it

first. Archiving is the right tool for retiring a skill entirely; to remove a

single bundled file use llma-skill-file-delete, and to roll back content

publish a new version rather than archiving.

Porting a local SKILL.md tree into PostHog

When migrating a local skill folder (e.g. my-skill/SKILL.md plus

scripts/, references/, assets/):

• Read the local SKILL.md. Its frontmatter maps to name, description,

license, compatibility, allowed_tools, metadata. The body after the

frontmatter becomes body.

• Walk the bundled subdirs and gather every file as

{ path, content, content_type }.

• Call posthog:llma-skill-create once with everything — the skill lands at

version: 1 complete. Do not split this into a create + N file-create

calls.

After the create, the skill is live for everyone via llma-skill-get.

When a skill is the wrong answer

Not every persistent prompt belongs in the skills store:

• One-off task instructions belong in the conversation, not in a skill.
• Personal scratchpads belong in agent memory or local files.
• Code is not a skill — if it's something a service runs, it belongs in the

repo.

A good skill is reusable, discoverable by description, and worth the cost of

keeping it correct over time.