Reference Enrichment Skill for Claude

Reference Enrichment Skill

Enrich an agent or skill's reference files from Level 0-2 to Level 3+, or decompose bloated body

files by extracting domain content into references. Enrichment adds knowledge; decomposition moves

knowledge to where progressive disclosure says it belongs. The enrichment pipeline runs five phases

with explicit gates because each phase feeds the next — starting Phase 3 without Phase 2 research

produces filler, not depth.

Workflow

Phase 0: DECOMPOSE

Goal: Extract domain-heavy content from a bloated SKILL.md or agent body into reference files.

When to use: When a component's body exceeds ~500 lines and contains catalogs, code examples, specification tables, or agent rosters that should live in references/ per PHILOSOPHY.md's progressive disclosure architecture.

Trigger: Invoke with --decompose argument, or when the request matches "decompose", "extract references", "slim down", "too long", or "move to references".

• Run the detection script to identify extractable content:

   python3 scripts/detect-decomposition-targets.py --skill {name}

(or --agent {name})

• If no extractable blocks found, report "nothing to decompose" and stop

• Save a snapshot of the original file: cp {path} /tmp/decomp-before-{name}.md

• For each extractable block identified by the detection script:

a. Read the content block and its surrounding context

b. Determine the best reference filename:

• Use the detection script's suggestion as a starting point
• If a reference file with related content already exists, MERGE into it
• Follow naming convention: references/{topic}.md (lowercase, hyphens)

c. Create or update the reference file following references/reference-file-template.md

d. Remove the content from the body (MOVE, not copy)

e. Add a loading table entry in the body that maps task signals to the new reference file

• Ensure the body retains:
• YAML frontmatter
• Brief overview paragraph
• Phase workflow (phases, gates, decision points)
• Loading table with entries for all reference files
• Error handling section
• References section

• Validate the decomposition:

   python3 scripts/validate-decomposition.py \
       --before /tmp/decomp-before-{name}.md \
       --after {path} \
       --refs {refs_dir}/

• If validation FAILS: restore from snapshot and report the failure. Do not proceed.

• If validation PASSES: run structural checks:

   python3 scripts/validate-references.py --skill {name}  # or --agent {name}
   python3 scripts/audit-reference-depth.py --skill {name} --verbose  # or --agent {name}

Gate: Validation passes. Body line count reduced. All extracted content exists in reference files. Loading table entries exist for all new references.

Phase 1: DISCOVER

Goal: Identify which sub-domains are missing reference coverage.

• Run the gap analyzer: python3 skills/meta/reference-enrichment/scripts/gap-analyzer.py --agent {name}

(or --skill {name})

• Read the component's .md file to understand its stated purpose, triggers, and domain claims
• Read any existing reference files to map current coverage
• Compare stated domains against covered domains to identify gaps

Output format:

DISCOVER: {name}
  Current level: {0-3}
  Existing references: [{filenames}]
  Stated domains: [{domains from description and body}]
  Gaps: [{sub-domains with no reference coverage}]
  Recommended files: [{filename} → {why}]

Gate: Gap report exists with at least one identified gap. If no gaps exist (Level 3 already),

report and stop — over-generating creates noise, not signal.

Phase 2: RESEARCH

Goal: Compile concrete, domain-specific content for each gap.

For each identified gap:

• Read existing Level 3 reference files in this repo as exemplars — golang-general-engineer's

references/ is the benchmark: version-specific patterns, grep commands, error-fix mappings

• Identify: version-specific patterns (what changed in version X.Y), common failure modes with

detection commands (grep -rn "pattern" --include="*.ext"), error-fix mappings

(error message → root cause → fix), project-specific conventions visible in the codebase

Dispatch up to 5 parallel research agents — one per sub-domain gap — because sequential research

bottlenecks the pipeline. Each agent receives: the sub-domain, the component's .md as context,

and a path to an exemplar Level 3 reference file.

Gate: Each gap has at least 10 concrete findings (version numbers, function names, grep

patterns, code examples). Generic advice ("follow best practices") does not count toward this gate.

Phase 3: COMPILE

Goal: Assemble research into structured reference files.

For each gap, create one reference file following references/reference-file-template.md:

• One file per major sub-domain (not one monolithic file) because focused files are faster to

load and easier to update as language versions change

• Max 500 lines per file (CLAUDE.md standard) — split into sub-topics if content exceeds this
• Include: overview paragraph, pattern table with version ranges, failure mode table with

detection commands, error-fix mappings where applicable

• Match the tone of existing Level 3 references: direct, concrete, no hedging

Do-pairing rule (mandatory): Every failure mode block written during this phase must include

a "Do instead" counterpart. If the retro learning or research does not carry enough information

to write a concrete positive counterpart, omit the failure mode entirely rather than shipping it

without the paired "Do instead". A bare negative block encodes no actionable knowledge and will

fail structural validation. If a prohibition is a genuine absolute with no correct alternative,

annotate it with <!-- no-pair-required: reason --> inline before the block.

Write files to: agents/{name}/references/ or skills/{name}/references/

Gate: Each generated file is between 80-500 lines. Run both checks:

python3 scripts/validate-references.py --agent {name}
python3 scripts/validate-references.py --check-do-framing

Both must exit 0 before proceeding to Phase 4.

After validation passes, run the condense skill on each generated reference file to strip prose

filler while preserving patterns, detection commands, and error-fix mappings.

Phase 4: VALIDATE

Goal: Confirm the reference files meet Level 3+ depth before integrating.

Tier 1 (Deterministic):

python3 scripts/audit-reference-depth.py --agent {name} --json

Verify the level field is 3 in the output. If still below Level 3, the files are too

generic — return to Phase 2 for the weak sub-domain.

Tier 2 (LLM self-assessment):

Read each generated file and apply the rubric from references/quality-rubric.md. Ask: would

a reviewer using only this file produce Level 3 quality output? Concrete test: pick one

failure mode from the file — does it include a grep command to detect it?

Gate: Both tiers pass. If Tier 2 fails for a specific sub-domain, loop back to Phase 2 for

that gap only (not all gaps). Maximum 2 loops per gap before flagging for manual enrichment.

Phase 5: INTEGRATE

Goal: Wire the new references into the component so they are actually loaded.

• Read the component's .md file
• Add or update a loading table in the body — pattern from skills/meta/do/references/repo-architecture.md:

   | Task type | Load |
   |-----------|------|
   | {task}    | `references/{file}.md` |

• Write the updated .md file
• Run validation:

   python3 scripts/validate-references.py --agent {name}
   python3 -m pytest scripts/tests/test_reference_loading.py -k {name} -v

• Stage all changes: git add agents/{name}/ skills/{name}/

Gate: Validation passes. Changes staged. Report the level change (was: N, now: M) and list

each new file with its line count.

Reference Material

Load when the task type matches:

| Task type | Load |

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

| Understanding Level 0-3 criteria | references/quality-rubric.md |

| Creating new reference files | references/reference-file-template.md |

| Decomposing bloated components | Run python3 scripts/detect-decomposition-targets.py --skill {name} first |

Reference Loading Table

| Signal | Load These Files | Why |

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

| classifying reference depth Level 0-3 (DISCOVER gaps, VALIDATE Tier 2) | quality-rubric.md | Loads detailed guidance from quality-rubric.md. |

| writing new reference files (Phase 3 COMPILE) | reference-file-template.md | Loads detailed guidance from reference-file-template.md. |

Error Handling

Gap analyzer fails: The component may not exist in expected paths. Check both agents/ and

skills/ directories, and ~/.claude/agents/ for deployed copies.

Phase 2 gate fails (fewer than 10 concrete findings): The domain may be too narrow or already

well-documented upstream. Flag and suggest manual enrichment with project-specific production

incidents rather than generic docs research.

Phase 4 Tier 1 still below Level 3 after compile: The files are too short or too generic. Read one

file, apply the rubric directly, identify the weakest section, and target Phase 2 research at

that section specifically.

validate-references.py not found: Script may not exist for this component. Skip that check,

proceed with audit-reference-depth.py as the sole Tier 1 gate.

Decomposition validation fails: Content was lost during extraction. Restore from the

snapshot at /tmp/decomp-before-{name}.md. Check that each extracted content block appears

in a reference file. Common cause: a code block was partially extracted or a table was split

across the body and a reference file.