Skills
skills/nyosegawa/skills/skill-auditor

skill-auditor

SKILL.md

Skill Auditor

Portfolio-level skill routing analysis and optimization. Analyzes real session transcripts to find routing errors, attention competition, and coverage gaps, then generates an interactive HTML report.

Prerequisites

  • pip install tiktoken (optional — falls back to character-based estimation)
  • No external API keys required. Analysis uses Claude sub-agents.

Workflow

Run all steps sequentially. The coordinator (you) manages data flow between scripts and sub-agents.

Step 0: Initial Questions

Before starting, ask the user two questions using AskUserQuestion:

  1. Report language: "レポートの言語は？ (e.g. 日本語, English, 中文, ...)" — Free text input. Default to the user's conversation language if not specified.
  2. Scope: "分析範囲はどうしますか？" — Cross-project (all projects) / Current project only

Store these choices. Pass the language choice to all sub-agents as an instruction prefix: "Write all output text (health_assessment, detail, reason, suggested_fix, etc.) in [chosen language]."

For cross-project mode, use "all" as the project_path argument in Step 3. For current-project mode, use --cwd "$(pwd)".

Step 1: Detect Project

If cross-project mode was selected:

python3 scripts/collect_transcripts.py all --days 14 \
  --output <workspace>/transcripts.json --verbose

If current-project mode:

python3 scripts/collect_transcripts.py --cwd "$(pwd)" --days 14 \
  --output <workspace>/transcripts.json --verbose

If auto-detection fails, show the list and ask the user which project to audit.

For cross-project mode, base dir: ~/.claude/skill-report/. For current-project mode, base dir: <project>/.claude/skill-report/.

Step 2: Set Up Workspace

Each run gets a timestamped subdirectory so multiple runs never collide:

RUN_ID=$(date +%Y-%m-%dT%H-%M-%S)
WORKSPACE=<base_dir>/${RUN_ID}
mkdir -p ${WORKSPACE}

Use ${WORKSPACE} as <workspace> in all subsequent steps. health-history.json stays at <base_dir>/health-history.json (shared across runs — see Step 8).

Step 3: Collect Data

Run both scripts. They produce the input files for analysis.

# Transcripts already collected in Step 1

python3 scripts/collect_skills.py \
  --output <workspace>/skill-manifest.json --verbose

Report the collection summary to the user: "N sessions, M user turns, K skills found. Attention budget: T tokens total."

Step 4: Routing Audit (Sub-agents)

Spawn one or more routing-analyst sub-agents. Each sub-agent:

  1. Reads agents/routing-analyst.md for its analysis rubric
  2. Reads a filtered skill manifest (only skills visible to that batch)
  3. Reads a batch of transcripts
  4. Writes analysis to a batch JSON file

IMPORTANT — Project-aware batching: Projects with local skills must be batched separately. Projects with only global skills can be pooled together (they see the same skill set). When many projects have unique local skills, batches are capped at MAX_BATCHES (default 12). Excess groups are merged by greedy similarity — the group with the fewest extra skills is merged into the most similar existing batch. This adds a few extra skills to visible_skill_names but keeps sub-agent count bounded.

import json, math
from collections import defaultdict

data = json.load(open("<workspace>/transcripts.json"))
manifest = json.load(open("<workspace>/skill-manifest.json"))
sessions = data["sessions"]

# Identify global skills and project-local skills
global_skills = [s for s in manifest["skills"] if s["scope"] == "global"]
global_names = [s["name"] for s in global_skills]
project_local = defaultdict(list)  # project_path -> [skill dicts]
for s in manifest["skills"]:
    if s["scope"] == "project-local" and s.get("project_path"):
        project_local[s["project_path"]].append(s)

# Helper: does this encoded project_dir match a project_path with locals?
def find_local_skills(project_dir):
    for pp, skills in project_local.items():
        encoded = pp.replace("/", "-").replace(".", "-")
        if encoded.lstrip("-") in project_dir.lstrip("-"):
            return skills
    return []

# Separate sessions: projects with local skills vs global-only
global_only_indices = []            # can be pooled
local_project_groups = defaultdict(list)  # project_dir -> indices

for i, s in enumerate(sessions):
    pdir = s.get("project_dir", "unknown")
    locals = find_local_skills(pdir)
    if locals:
        local_project_groups[pdir].append(i)
    else:
        global_only_indices.append(i)

# Build batches
batch_size = 60
MAX_BATCHES = 12  # Cap total sub-agents to keep cost/time bounded
batches = []

# 1) Pool all global-only sessions together
for chunk_start in range(0, len(global_only_indices), batch_size):
    chunk = global_only_indices[chunk_start:chunk_start + batch_size]
    batches.append({
        "session_indices": chunk,
        "label": "global-only (mixed projects)",
        "visible_skill_names": global_names,
    })

# 2) Group projects with same local skill set, then batch together
by_skill_set = defaultdict(list)  # tuple of local names -> indices
for pdir, indices in local_project_groups.items():
    local_names = tuple(sorted(s["name"] for s in find_local_skills(pdir)))
    by_skill_set[local_names].extend(indices)

local_batches = []
for local_names, indices in by_skill_set.items():
    visible = global_names + list(local_names)
    for chunk_start in range(0, len(indices), batch_size):
        chunk = indices[chunk_start:chunk_start + batch_size]
        local_batches.append({
            "session_indices": chunk,
            "label": f"local skills: {', '.join(local_names[:3])}{'...' if len(local_names) > 3 else ''}",
            "visible_skill_names": visible,
            "_local_set": set(local_names),
        })

# 3) Merge if too many batches — greedily merge smallest into most similar
remaining_budget = MAX_BATCHES - len(batches)
while len(local_batches) > remaining_budget and len(local_batches) > 1:
    # Find the smallest batch
    smallest_idx = min(range(len(local_batches)), key=lambda i: len(local_batches[i]["session_indices"]))
    smallest = local_batches.pop(smallest_idx)
    # Find the most similar batch (fewest extra skills added)
    best_idx, best_extra = 0, float("inf")
    for j, b in enumerate(local_batches):
        extra = len(smallest["_local_set"] - b["_local_set"]) + len(b["_local_set"] - smallest["_local_set"])
        if extra < best_extra:
            best_idx, best_extra = j, extra
    # Merge into best match
    target = local_batches[best_idx]
    target["session_indices"].extend(smallest["session_indices"])
    target["_local_set"] = target["_local_set"] | smallest["_local_set"]
    merged_local = sorted(target["_local_set"])
    target["visible_skill_names"] = global_names + merged_local
    target["label"] = f"merged local skills: {', '.join(merged_local[:3])}{'...' if len(merged_local) > 3 else ''}"

# Clean up internal field and add to batches
for b in local_batches:
    b.pop("_local_set", None)
    batches.append(b)

for i, b in enumerate(batches):
    print(f"Batch {i}: {len(b['session_indices'])} sessions, "
          f"{len(b['visible_skill_names'])} skills — {b['label']}")

Before spawning, build a DMI list per batch from the manifest:

dmi_skills = {s["name"] for s in manifest["skills"] if s.get("disable_model_invocation")}
for b in batches:
    b["dmi_skill_names"] = sorted(set(b["visible_skill_names"]) & dmi_skills)

Spawn sub-agents in parallel — one per batch:

For each batch i:
  Agent tool (general-purpose):
    "Read agents/routing-analyst.md from the skill-auditor skill directory for
     your analysis instructions.
     Read <workspace>/skill-manifest.json for skill definitions.
     Read <workspace>/transcripts.json for session data.
     Only analyze sessions with these indices: [list from batch].
     Only evaluate against these skills: [visible_skill_names from batch].
     Ignore skills not in this list — they are not available in this
     project context.
     These skills have disable-model-invocation: true and NEVER auto-fire:
     [dmi_skill_names from batch]. Do NOT flag them as false_negative.
     Write your analysis as JSON to <workspace>/batch-audit-<i>.json
     following the exact schema in schemas/schemas.md (audit-report.json section)."

After all sub-agents complete, merge batch results:

  • Union all skill_reports (combine incidents, recalculate stats per skill)
  • Union all competition_pairs and coverage_gaps
  • Recalculate meta totals (sum sessions_analyzed, turns_analyzed, etc.)

Write merged result to <workspace>/audit-report.json.

Step 5: Portfolio Analysis (Sub-agent)

Spawn a portfolio-analyst sub-agent:

Agent tool (general-purpose):
  "Read agents/portfolio-analyst.md from the skill-auditor skill directory.
   Read <workspace>/skill-manifest.json for skill definitions and attention budget.
   Read <workspace>/audit-report.json for the routing audit results.
   Write your portfolio analysis as JSON to <workspace>/portfolio-analysis.json."

Step 6: Improvement Plan (Sub-agent)

Spawn an improvement-planner sub-agent:

Agent tool (general-purpose):
  "Read agents/improvement-planner.md from the skill-auditor skill directory.
   Read <workspace>/audit-report.json for routing audit results.
   Read <workspace>/portfolio-analysis.json for portfolio analysis.
   Read <workspace>/skill-manifest.json for current skill definitions.
   IMPORTANT: Write ALL output text in [chosen language] — this includes
   fixes_issues, changes_made, cascade_risk, expected_impact, rationale,
   suggested_description, and every other human-readable string field.
   Write your improvement proposals as JSON to <workspace>/improvement-proposals.json.
   Also write individual patch files to <workspace>/patches/ directory."

Step 7: Generate HTML Report

python3 scripts/generate_report.py \
  --workspace <workspace>

Output: <workspace>/skill-audit-report.html. Open the report in the browser:

open <workspace>/skill-audit-report.html

Step 8: Update Health History

Read <base_dir>/health-history.json (create if doesn't exist — start with empty array []). Append a new entry with the current run's summary:

{
  "timestamp": "<ISO 8601>",
  "sessions_analyzed": <N>,
  "turns_analyzed": <N>,
  "portfolio_health": "<score>",
  "routing_accuracy_avg": <0.0-1.0>,
  "total_description_tokens": <N>,
  "competition_conflicts": <N>,
  "coverage_gaps": <N>,
  "skills_audited": <N>,
  "patches_proposed": <N>
}

If there's a previous entry, show the delta: "Accuracy changed from X to Y."

Step 9: Apply Patches (User Approval)

Show the user a summary from the HTML report. For each patch, show the before/after diff and cascade risk. Let the user approve or reject each.

For approved patches:

python3 scripts/apply_patches.py \
  --patches <workspace>/patches/ --confirm \
  --output <workspace>/changelog.md

Step 10: Summary

Report what was done:

  • How many sessions analyzed
  • How many routing issues found
  • Portfolio health score
  • Patches proposed / approved / applied
  • New skills suggested
  • Link to the HTML report

Analysis Capabilities

Routing Accuracy

Per-skill fire count, accuracy, false positives/negatives, specific incidents with root cause analysis. See agents/routing-analyst.md for the rubric.

Attention Budget

Total description tokens across all skills. Per-skill token cost and efficiency rating. Identifies bloated descriptions that waste attention budget. See agents/portfolio-analyst.md.

Competition Matrix

Classifies skill-pair relationships: orthogonal / adjacent / overlapping / nested. Based on real transcript evidence, not just keyword overlap.

Portfolio-Aware Optimization

Patches consider the full skill set. Cascade checking is mandatory — each patch states what it fixes, what it might break, and the token budget impact. See agents/improvement-planner.md.

Error Taxonomy

Verdict Description
correct Right skill loaded for the intent
false_negative Skill should have loaded but didn't. High bar: task must be meaningfully worse without it
false_positive Skill loaded but was irrelevant
confused Wrong skill loaded instead of the correct one
no_skill_needed No skill was needed for this turn (most common)
explicit_invocation User explicitly called /skill-name — not a routing event, skip from accuracy calc
coverage_gap User intent not covered by any existing skill

Note on disable-model-invocation: true: Skills with this flag never auto-fire by design. They are excluded from false_negative analysis and listed separately in the report as "explicit-only" skills.

Workspace Structure

<base_dir>/                          # e.g. ~/.claude/skill-report/
├── health-history.json              # shared across runs (append-only)
├── 2026-03-04T18-45-23/             # run 1
│   ├── transcripts.json
│   ├── skill-manifest.json
│   ├── batch-audit-*.json
│   ├── audit-report.json
│   ├── portfolio-analysis.json
│   ├── improvement-proposals.json
│   ├── patches/*.patch.json
│   ├── skill-audit-report.html
│   └── changelog.md
└── 2026-03-04T20-12-07/             # run 2
    └── ...

Troubleshooting

  • "No project found": Run with --cwd pointing to the project root, or use --list to see available projects.
  • tiktoken not installed: Token counts will use character-based approximation. Install with pip install tiktoken for accuracy.
  • Large project (100+ sessions): Sessions are batched automatically. Multiple sub-agents run in parallel.
  • Sub-agent produces invalid JSON: Re-run the specific sub-agent step. The rubric in agents/ includes exact schema specifications.
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nyosegawa/skills
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