Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wan-huiyan/claude-ecosystem-hygiene --skill ecosystem-auditgit clone --depth 1 https://github.com/wan-huiyan/claude-ecosystem-hygieneWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wan-huiyan/claude-ecosystem-hygiene/ecosystem-audit)<a href="https://agentmods.dev/skills/wan-huiyan/claude-ecosystem-hygiene/ecosystem-audit"><img src="https://agentmods.dev/badge/skills/wan-huiyan/claude-ecosystem-hygiene/ecosystem-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wan-huiyan/claude-ecosystem-hygiene/ecosystem-audit"><img src="https://agentmods.dev/badge/skills/wan-huiyan/claude-ecosystem-hygiene/ecosystem-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00342 | $0.04228 |
| Opus 5 | $0.00171 | $0.02114 |
| Sonnet 5 | $0.00068 | $0.00846 |
| Haiku 4.5 | $0.00034 | $0.00423 |
Grade A, and why
ecosystem-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ecosystem Audit
A comprehensive audit of the entire ~/.claude/ ecosystem that scans 9 categories of persistent
artifacts, scores each on a health scale, and produces an interactive HTML dashboard with
prioritized cleanup recommendations.
When to Use
- User wants to know what skills are installed and which are actually used
- User wants to clean up their Claude Code environment
- User asks about ecosystem health, dormant artifacts, or storage bloat
- Monthly hygiene check (recommend running every 30 days)
- Before/after installing a batch of new skills
- When memory-hygiene or schliff:doctor findings suggest broader issues
Architecture
This skill orchestrates parallel subagents for speed. The audit runs in 3 phases:
Phase 1: Scan (parallel subagents)
├── Skills: parse JSONL logs for invocations, classify by domain
├── Memory: check all project dirs, apply memory-hygiene thresholds
├── Handoffs: count, classify lifecycle state, find orphans
└── ADRs + Docs: read status fields, check links, classify
Phase 2: Score (sequential)
├── Calculate health % per category
├── Apply lifecycle scoring for worktrees
├── Cross-reference with memory-hygiene thresholds
└── Generate prioritized recommendations (P0/P1/P2)
Phase 3: Report (write files)
├── Markdown summary → docs/handoffs/ecosystem_audit_report.md
└── Interactive HTML → docs/handoffs/ecosystem_audit_report.html
Phase 1: Scan
Launch 4 parallel subagents, each responsible for one scan domain. Each subagent reports structured findings back to the orchestrator.
1A. Skill Usage Scan
Parse JSONL session logs to determine which skills are actually invoked.
Data source: ~/.claude/projects/<dir>/<uuid>.jsonl
What to extract:
- Lines where
message.content[].name == "Skill"— these are invocations - Extract
input.skill(skill name),input.args, andtimestamp - Cross-reference with
toolUseResult.commandNameandtoolUseResult.success
Classification rules:
- Active: invoked in the session log window (default: 30 days)
- Relevant-Dormant: not invoked but domain-aligned with user's work (check user profile in memory files for role/domain context)
- Niche-Dormant: specialized domain unlikely to be needed (bio/chem databases, quantum computing, wet-lab integrations, astrophysics, game dev, embedded systems)
- DevOps-Dormant: IaC/CI/CD tools (terraform, helm, k8s, ansible, dockerfile, jenkins, github-actions generators and validators)
- Not-a-Skill: workspace directories, cloned repos, .DS_Store, README files
- Deprecated: SKILL.md explicitly says "DEPRECATED" or "merged into"
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .claude-plugin/plugin.json 620 B
- assets/report_template.html 61 KB
- CHANGELOG.md 11 KB
- evals/evals.json 5.5 KB
- evals/trigger_eval.json 3.9 KB
- marketplace.json 735 B
- README.md 5.8 KB
- scripts/parse_skill_usage.py 4.4 KB runs code
- scripts/score_health.py 7.1 KB runs code
- scripts/score_trigger_coverage.py 10 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 328 lines · 342 tokens per session scan A 70f35c49dbf5
ecosystem-audit is a skill published in the GitHub repository wan-huiyan/claude-ecosystem-hygiene (1 stars, last pushed 25d ago), licensed MIT. It adds 342 tokens to every session and 4,228 once invoked, about $0.0017 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
session-handoff
End-of-session handoff that captures session knowledge, dispatches output across the canonical 7-bucket docs/ taxonomy (decisions/runbooks/analysis/references/reviews/handoffs/deliverables — aligned with memory-hygiene v3.3), triggers a doc-freshness reverse-lint + skill-freshness audit to catch stale normative…
llm-wiki
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or…
reflect
Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.
remember
Review auto-memory entries and propose promotions to CLAUDE.md, CLAUDE.local.md, or shared memory. Also detects outdated, conflicting, and duplicate entries across memory layers.
ucai-patterns
Use when the user asks about Claude Code best practices, how to write agents, how to use hooks, how to manage context, or how to work effectively with Claude Code's native systems.
dream
Memory consolidation - review, merge, prune, and index memory files. Run periodically to keep memories organized and up-to-date.