Merge pull request #265 from shimo4228/feat/skills/skill-stocktake

feat(skills): add skill-stocktake skill
This commit is contained in:
Affaan Mustafa
2026-02-23 06:55:38 -08:00
committed by GitHub
4 changed files with 488 additions and 0 deletions

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---
description: "Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation."
---
# skill-stocktake
Slash command (`/skill-stocktake`) that audits all Claude skills and commands using a quality checklist + AI holistic judgment. Supports two modes: Quick Scan for recently changed skills, and Full Stocktake for a complete review.
## Scope
The command targets the following paths **relative to the directory where it is invoked**:
| Path | Description |
|------|-------------|
| `~/.claude/skills/` | Global skills (all projects) |
| `{cwd}/.claude/skills/` | Project-level skills (if the directory exists) |
**At the start of Phase 1, the command explicitly lists which paths were found and scanned.**
### Targeting a specific project
To include project-level skills, run from that project's root directory:
```bash
cd ~/path/to/my-project
/skill-stocktake
```
If the project has no `.claude/skills/` directory, only global skills and commands are evaluated.
## Modes
| Mode | Trigger | Duration |
|------|---------|---------|
| Quick Scan | `results.json` exists (default) | 510 min |
| Full Stocktake | `results.json` absent, or `/skill-stocktake full` | 2030 min |
**Results cache:** `~/.claude/skills/skill-stocktake/results.json`
## Quick Scan Flow
Re-evaluate only skills that have changed since the last run (510 min).
1. Read `~/.claude/skills/skill-stocktake/results.json`
2. Run: `bash ~/.claude/skills/skill-stocktake/scripts/quick-diff.sh \
~/.claude/skills/skill-stocktake/results.json`
(Project dir is auto-detected from `$PWD/.claude/skills`; pass it explicitly only if needed)
3. If output is `[]`: report "No changes since last run." and stop
4. Re-evaluate only those changed files using the same Phase 2 criteria
5. Carry forward unchanged skills from previous results
6. Output only the diff
7. Run: `bash ~/.claude/skills/skill-stocktake/scripts/save-results.sh \
~/.claude/skills/skill-stocktake/results.json <<< "$EVAL_RESULTS"`
## Full Stocktake Flow
### Phase 1 — Inventory
Run: `bash ~/.claude/skills/skill-stocktake/scripts/scan.sh`
The script enumerates skill files, extracts frontmatter, and collects UTC mtimes.
Project dir is auto-detected from `$PWD/.claude/skills`; pass it explicitly only if needed.
Present the scan summary and inventory table from the script output:
```
Scanning:
✓ ~/.claude/skills/ (17 files)
✗ {cwd}/.claude/skills/ (not found — global skills only)
```
| Skill | 7d use | 30d use | Description |
|-------|--------|---------|-------------|
### Phase 2 — Quality Evaluation
Launch a Task tool subagent (**Explore agent, model: opus**) with the full inventory and checklist.
The subagent reads each skill, applies the checklist, and returns per-skill JSON:
`{ "verdict": "Keep"|"Improve"|"Update"|"Retire"|"Merge into [X]", "reason": "..." }`
**Chunk guidance:** Process ~20 skills per subagent invocation to keep context manageable. Save intermediate results to `results.json` (`status: "in_progress"`) after each chunk.
After all skills are evaluated: set `status: "completed"`, proceed to Phase 3.
**Resume detection:** If `status: "in_progress"` is found on startup, resume from the first unevaluated skill.
Each skill is evaluated against this checklist:
```
- [ ] Content overlap with other skills checked
- [ ] Overlap with MEMORY.md / CLAUDE.md checked
- [ ] Freshness of technical references verified (use WebSearch if tool names / CLI flags / APIs are present)
- [ ] Usage frequency considered
```
Verdict criteria:
| Verdict | Meaning |
|---------|---------|
| Keep | Useful and current |
| Improve | Worth keeping, but specific improvements needed |
| Update | Referenced technology is outdated (verify with WebSearch) |
| Retire | Low quality, stale, or cost-asymmetric |
| Merge into [X] | Substantial overlap with another skill; name the merge target |
Evaluation is **holistic AI judgment** — not a numeric rubric. Guiding dimensions:
- **Actionability**: code examples, commands, or steps that let you act immediately
- **Scope fit**: name, trigger, and content are aligned; not too broad or narrow
- **Uniqueness**: value not replaceable by MEMORY.md / CLAUDE.md / another skill
- **Currency**: technical references work in the current environment
**Reason quality requirements** — the `reason` field must be self-contained and decision-enabling:
- Do NOT write "unchanged" alone — always restate the core evidence
- For **Retire**: state (1) what specific defect was found, (2) what covers the same need instead
- Bad: `"Superseded"`
- Good: `"disable-model-invocation: true already set; superseded by continuous-learning-v2 which covers all the same patterns plus confidence scoring. No unique content remains."`
- For **Merge**: name the target and describe what content to integrate
- Bad: `"Overlaps with X"`
- Good: `"42-line thin content; Step 4 of chatlog-to-article already covers the same workflow. Integrate the 'article angle' tip as a note in that skill."`
- For **Improve**: describe the specific change needed (what section, what action, target size if relevant)
- Bad: `"Too long"`
- Good: `"276 lines; Section 'Framework Comparison' (L80140) duplicates ai-era-architecture-principles; delete it to reach ~150 lines."`
- For **Keep** (mtime-only change in Quick Scan): restate the original verdict rationale, do not write "unchanged"
- Bad: `"Unchanged"`
- Good: `"mtime updated but content unchanged. Unique Python reference explicitly imported by rules/python/; no overlap found."`
### Phase 3 — Summary Table
| Skill | 7d use | Verdict | Reason |
|-------|--------|---------|--------|
### Phase 4 — Consolidation
1. **Retire / Merge**: present detailed justification per file before confirming with user:
- What specific problem was found (overlap, staleness, broken references, etc.)
- What alternative covers the same functionality (for Retire: which existing skill/rule; for Merge: the target file and what content to integrate)
- Impact of removal (any dependent skills, MEMORY.md references, or workflows affected)
2. **Improve**: present specific improvement suggestions with rationale:
- What to change and why (e.g., "trim 430→200 lines because sections X/Y duplicate python-patterns")
- User decides whether to act
3. **Update**: present updated content with sources checked
4. Check MEMORY.md line count; propose compression if >100 lines
## Results File Schema
`~/.claude/skills/skill-stocktake/results.json`:
**`evaluated_at`**: Must be set to the actual UTC time of evaluation completion.
Obtain via Bash: `date -u +%Y-%m-%dT%H:%M:%SZ`. Never use a date-only approximation like `T00:00:00Z`.
```json
{
"evaluated_at": "2026-02-21T10:00:00Z",
"mode": "full",
"batch_progress": {
"total": 80,
"evaluated": 80,
"status": "completed"
},
"skills": {
"skill-name": {
"path": "~/.claude/skills/skill-name/SKILL.md",
"verdict": "Keep",
"reason": "Concrete, actionable, unique value for X workflow",
"mtime": "2026-01-15T08:30:00Z"
}
}
}
```
## Notes
- Evaluation is blind: the same checklist applies to all skills regardless of origin (ECC, self-authored, auto-extracted)
- Archive / delete operations always require explicit user confirmation
- No verdict branching by skill origin

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#!/usr/bin/env bash
# quick-diff.sh — compare skill file mtimes against results.json evaluated_at
# Usage: quick-diff.sh RESULTS_JSON [CWD_SKILLS_DIR]
# Output: JSON array of changed/new files to stdout (empty [] if no changes)
#
# When CWD_SKILLS_DIR is omitted, defaults to $PWD/.claude/skills so the
# script always picks up project-level skills without relying on the caller.
#
# Environment:
# SKILL_STOCKTAKE_GLOBAL_DIR Override ~/.claude/skills (for testing only;
# do not set in production — intended for bats tests)
# SKILL_STOCKTAKE_PROJECT_DIR Override project dir detection (for testing only)
set -euo pipefail
RESULTS_JSON="${1:-}"
CWD_SKILLS_DIR="${SKILL_STOCKTAKE_PROJECT_DIR:-${2:-$PWD/.claude/skills}}"
GLOBAL_DIR="${SKILL_STOCKTAKE_GLOBAL_DIR:-$HOME/.claude/skills}"
if [[ -z "$RESULTS_JSON" || ! -f "$RESULTS_JSON" ]]; then
echo "Error: RESULTS_JSON not found: ${RESULTS_JSON:-<empty>}" >&2
exit 1
fi
# Validate CWD_SKILLS_DIR looks like a .claude/skills path (defense-in-depth).
# Only warn when the path exists — a nonexistent path poses no traversal risk.
if [[ -n "$CWD_SKILLS_DIR" && -d "$CWD_SKILLS_DIR" && "$CWD_SKILLS_DIR" != */.claude/skills* ]]; then
echo "Warning: CWD_SKILLS_DIR does not look like a .claude/skills path: $CWD_SKILLS_DIR" >&2
fi
evaluated_at=$(jq -r '.evaluated_at' "$RESULTS_JSON")
# Fail fast on a missing or malformed evaluated_at rather than producing
# unpredictable results from ISO 8601 string comparison against "null".
if [[ ! "$evaluated_at" =~ ^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}Z$ ]]; then
echo "Error: invalid or missing evaluated_at in $RESULTS_JSON: $evaluated_at" >&2
exit 1
fi
# Pre-extract known paths from results.json once (O(1) lookup per file instead of O(n*m))
known_paths=$(jq -r '.skills[].path' "$RESULTS_JSON" 2>/dev/null)
tmpdir=$(mktemp -d)
# Use a function to avoid embedding $tmpdir in a quoted string (prevents injection
# if TMPDIR were crafted to contain shell metacharacters).
_cleanup() { rm -rf "$tmpdir"; }
trap _cleanup EXIT
# Shared counter across process_dir calls — intentionally NOT local
i=0
process_dir() {
local dir="$1"
while IFS= read -r file; do
local mtime dp is_new
mtime=$(date -u -r "$file" +%Y-%m-%dT%H:%M:%SZ)
dp="${file/#$HOME/~}"
# Check if this file is known to results.json (exact whole-line match to
# avoid substring false-positives, e.g. "python-patterns" matching "python-patterns-v2").
if echo "$known_paths" | grep -qxF "$dp"; then
is_new="false"
# Known file: only emit if mtime changed (ISO 8601 string comparison is safe)
[[ "$mtime" > "$evaluated_at" ]] || continue
else
is_new="true"
# New file: always emit regardless of mtime
fi
jq -n \
--arg path "$dp" \
--arg mtime "$mtime" \
--argjson is_new "$is_new" \
'{path:$path,mtime:$mtime,is_new:$is_new}' \
> "$tmpdir/$i.json"
i=$((i+1))
done < <(find "$dir" -name "*.md" -type f 2>/dev/null | sort)
}
[[ -d "$GLOBAL_DIR" ]] && process_dir "$GLOBAL_DIR"
[[ -n "$CWD_SKILLS_DIR" && -d "$CWD_SKILLS_DIR" ]] && process_dir "$CWD_SKILLS_DIR"
if [[ $i -eq 0 ]]; then
echo "[]"
else
jq -s '.' "$tmpdir"/*.json
fi

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#!/usr/bin/env bash
# save-results.sh — merge evaluated skills into results.json with correct UTC timestamp
# Usage: save-results.sh RESULTS_JSON <<< "$EVAL_JSON"
#
# stdin format:
# { "skills": {...}, "mode"?: "full"|"quick", "batch_progress"?: {...} }
#
# Always sets evaluated_at to current UTC time via `date -u`.
# Merges stdin .skills into existing results.json (new entries override old).
# Optionally updates .mode and .batch_progress if present in stdin.
set -euo pipefail
RESULTS_JSON="${1:-}"
if [[ -z "$RESULTS_JSON" ]]; then
echo "Error: RESULTS_JSON argument required" >&2
echo "Usage: save-results.sh RESULTS_JSON <<< \"\$EVAL_JSON\"" >&2
exit 1
fi
EVALUATED_AT=$(date -u +%Y-%m-%dT%H:%M:%SZ)
# Read eval results from stdin and validate JSON before touching the results file
input_json=$(cat)
if ! echo "$input_json" | jq empty 2>/dev/null; then
echo "Error: stdin is not valid JSON" >&2
exit 1
fi
if [[ ! -f "$RESULTS_JSON" ]]; then
# Bootstrap: create new results.json from stdin JSON + current UTC timestamp
echo "$input_json" | jq --arg ea "$EVALUATED_AT" \
'. + { evaluated_at: $ea }' > "$RESULTS_JSON"
exit 0
fi
# Merge: new .skills override existing ones; old skills not in input_json are kept.
# Optionally update .mode and .batch_progress if provided.
#
# Use mktemp for a collision-safe temp file (concurrent runs on the same RESULTS_JSON
# would race on a predictable ".tmp" suffix; random suffix prevents silent overwrites).
tmp=$(mktemp "${RESULTS_JSON}.XXXXXX")
trap 'rm -f "$tmp"' EXIT
jq -s \
--arg ea "$EVALUATED_AT" \
'.[0] as $existing | .[1] as $new |
$existing |
.evaluated_at = $ea |
.skills = ($existing.skills + ($new.skills // {})) |
if ($new | has("mode")) then .mode = $new.mode else . end |
if ($new | has("batch_progress")) then .batch_progress = $new.batch_progress else . end' \
"$RESULTS_JSON" <(echo "$input_json") > "$tmp"
mv "$tmp" "$RESULTS_JSON"

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#!/usr/bin/env bash
# scan.sh — enumerate skill files, extract frontmatter and UTC mtime
# Usage: scan.sh [CWD_SKILLS_DIR]
# Output: JSON to stdout
#
# When CWD_SKILLS_DIR is omitted, defaults to $PWD/.claude/skills so the
# script always picks up project-level skills without relying on the caller.
#
# Environment:
# SKILL_STOCKTAKE_GLOBAL_DIR Override ~/.claude/skills (for testing only;
# do not set in production — intended for bats tests)
# SKILL_STOCKTAKE_PROJECT_DIR Override project dir detection (for testing only)
set -euo pipefail
GLOBAL_DIR="${SKILL_STOCKTAKE_GLOBAL_DIR:-$HOME/.claude/skills}"
CWD_SKILLS_DIR="${SKILL_STOCKTAKE_PROJECT_DIR:-${1:-$PWD/.claude/skills}}"
# Path to JSONL file containing tool-use observations (optional; used for usage frequency counts).
# Override via SKILL_STOCKTAKE_OBSERVATIONS env var if your setup uses a different path.
OBSERVATIONS="${SKILL_STOCKTAKE_OBSERVATIONS:-$HOME/.claude/observations.jsonl}"
# Validate CWD_SKILLS_DIR looks like a .claude/skills path (defense-in-depth).
# Only warn when the path exists — a nonexistent path poses no traversal risk.
if [[ -n "$CWD_SKILLS_DIR" && -d "$CWD_SKILLS_DIR" && "$CWD_SKILLS_DIR" != */.claude/skills* ]]; then
echo "Warning: CWD_SKILLS_DIR does not look like a .claude/skills path: $CWD_SKILLS_DIR" >&2
fi
# Extract a frontmatter field (handles both quoted and unquoted single-line values).
# Does NOT support multi-line YAML blocks (| or >) or nested YAML keys.
extract_field() {
local file="$1" field="$2"
awk -v f="$field" '
BEGIN { fm=0 }
/^---$/ { fm++; next }
fm==1 {
n = length(f) + 2
if (substr($0, 1, n) == f ": ") {
val = substr($0, n+1)
gsub(/^"/, "", val)
gsub(/"$/, "", val)
print val
exit
}
}
fm>=2 { exit }
' "$file"
}
# Get UTC timestamp N days ago (supports both macOS and GNU date)
date_ago() {
local n="$1"
date -u -v-"${n}d" +%Y-%m-%dT%H:%M:%SZ 2>/dev/null ||
date -u -d "${n} days ago" +%Y-%m-%dT%H:%M:%SZ
}
# Count observations matching a file path since a cutoff timestamp
count_obs() {
local file="$1" cutoff="$2"
if [[ ! -f "$OBSERVATIONS" ]]; then
echo 0
return
fi
jq -r --arg p "$file" --arg c "$cutoff" \
'select(.tool=="Read" and .path==$p and .timestamp>=$c) | 1' \
"$OBSERVATIONS" 2>/dev/null | wc -l | tr -d ' '
}
# Scan a directory and produce a JSON array of skill objects
scan_dir_to_json() {
local dir="$1"
local c7 c30
c7=$(date_ago 7)
c30=$(date_ago 30)
local tmpdir
tmpdir=$(mktemp -d)
# Use a function to avoid embedding $tmpdir in a quoted string (prevents injection
# if TMPDIR were crafted to contain shell metacharacters).
local _scan_tmpdir="$tmpdir"
_scan_cleanup() { rm -rf "$_scan_tmpdir"; }
trap _scan_cleanup RETURN
# Pre-aggregate observation counts in two passes (one per window) instead of
# calling jq per-file — reduces from O(n*m) to O(n+m) jq invocations.
local obs_7d_counts obs_30d_counts
obs_7d_counts=""
obs_30d_counts=""
if [[ -f "$OBSERVATIONS" ]]; then
obs_7d_counts=$(jq -r --arg c "$c7" \
'select(.tool=="Read" and .timestamp>=$c) | .path' \
"$OBSERVATIONS" 2>/dev/null | sort | uniq -c)
obs_30d_counts=$(jq -r --arg c "$c30" \
'select(.tool=="Read" and .timestamp>=$c) | .path' \
"$OBSERVATIONS" 2>/dev/null | sort | uniq -c)
fi
local i=0
while IFS= read -r file; do
local name desc mtime u7 u30 dp
name=$(extract_field "$file" "name")
desc=$(extract_field "$file" "description")
mtime=$(date -u -r "$file" +%Y-%m-%dT%H:%M:%SZ)
# Use awk exact field match to avoid substring false-positives from grep -F.
# uniq -c output format: " N /path/to/file" — path is always field 2.
u7=$(echo "$obs_7d_counts" | awk -v f="$file" '$2 == f {print $1}' | head -1)
u7="${u7:-0}"
u30=$(echo "$obs_30d_counts" | awk -v f="$file" '$2 == f {print $1}' | head -1)
u30="${u30:-0}"
dp="${file/#$HOME/~}"
jq -n \
--arg path "$dp" \
--arg name "$name" \
--arg description "$desc" \
--arg mtime "$mtime" \
--argjson use_7d "$u7" \
--argjson use_30d "$u30" \
'{path:$path,name:$name,description:$description,use_7d:$use_7d,use_30d:$use_30d,mtime:$mtime}' \
> "$tmpdir/$i.json"
i=$((i+1))
done < <(find "$dir" -name "*.md" -type f 2>/dev/null | sort)
if [[ $i -eq 0 ]]; then
echo "[]"
else
jq -s '.' "$tmpdir"/*.json
fi
}
# --- Main ---
global_found="false"
global_count=0
global_skills="[]"
if [[ -d "$GLOBAL_DIR" ]]; then
global_found="true"
global_skills=$(scan_dir_to_json "$GLOBAL_DIR")
global_count=$(echo "$global_skills" | jq 'length')
fi
project_found="false"
project_path=""
project_count=0
project_skills="[]"
if [[ -n "$CWD_SKILLS_DIR" && -d "$CWD_SKILLS_DIR" ]]; then
project_found="true"
project_path="$CWD_SKILLS_DIR"
project_skills=$(scan_dir_to_json "$CWD_SKILLS_DIR")
project_count=$(echo "$project_skills" | jq 'length')
fi
# Merge global + project skills into one array
all_skills=$(jq -s 'add' <(echo "$global_skills") <(echo "$project_skills"))
jq -n \
--arg global_found "$global_found" \
--argjson global_count "$global_count" \
--arg project_found "$project_found" \
--arg project_path "$project_path" \
--argjson project_count "$project_count" \
--argjson skills "$all_skills" \
'{
scan_summary: {
global: { found: ($global_found == "true"), count: $global_count },
project: { found: ($project_found == "true"), path: $project_path, count: $project_count }
},
skills: $skills
}'