Borrowing it
Nothing to install: this file belongs to sommio/qwen-omni-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sommio/qwen-omni-mcp/develop/.agents/skills/health/SKILL.mdgit clone --depth 1 https://github.com/sommio/qwen-omni-mcpWrote 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/sommio/qwen-omni-mcp/health)<a href="https://agentmods.dev/skills/sommio/qwen-omni-mcp/health"><img src="https://agentmods.dev/badge/skills/sommio/qwen-omni-mcp/health/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/sommio/qwen-omni-mcp/health"><img src="https://agentmods.dev/badge/skills/sommio/qwen-omni-mcp/health.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 | $0.00071 | $0.03882 |
| Opus 5 | $0.00036 | $0.01941 |
| Sonnet 5 | $0.00014 | $0.00776 |
| Haiku 4.5 | $0.00007 | $0.00388 |
Grade A, and why
health 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 12d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Health: Agent-Assisted Engineering Health
Prefix your first line with 🥷 inline, not as its own paragraph.
Audit the current project's agent setup and AI coding maintainability against this framework:
agent config → instruction surfaces → tools/runtime → verifiers → maintainability
Find violations. Identify the misaligned layer. Calibrate to project complexity only.
Outcome Contract
- Outcome: a budget-aware health report that separates agent configuration risk from AI maintainability risk.
- Done when: each finding names the misaligned layer, the concrete evidence, and a copy-pasteable action or diagnostic command.
- Evidence: collected health script output, tracked project instructions, runtime config summaries, verifier logs, hooks/MCP surfaces, and read-only live probes when needed.
- Output: prioritized findings with status, impact, and next action, or a clear clean bill with residual risk.
Two lanes share one report:
- Agent config health: Codex/Claude/Pi instruction drift, permissions, hooks, MCP, skills, and memory supply chain.
- AI maintainability health: project context surface, verifier wrapper, generated-artifact checks, hotspot ownership, and stale or misleading durable docs.
Output language: Check in order: (1) project agent instructions (AGENTS.md before runtime-specific files); (2) global agent instructions; (3) user's recent language; (4) English.
Budget posture: Start with the summary audit. Escalate automatically when the user asks for a deep, full, complete, thorough, "深入", "完整", "彻底", or "继续跑完" audit, when the user explicitly mentions AI coding code rot, Codex/Claude config drift, unclear context, missing verification, verifier output that points at stale paths, or "代码变烂", when current project instructions or remembered user preference says to run deep health checks by default, when the project is Complex, or when the summary pass exposes a critical ambiguity that cannot be resolved locally. Otherwise do not read full conversation extracts or launch inspector subagents. Tell the user before escalating because deep health audits can consume significant token quota.
What ships with it
16 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.
- agents/inspector-context.md 8.1 KB
- agents/inspector-control.md 5.4 KB
- agents/inspector-maintainability.md 2.8 KB
- references/durable-context.md 1.6 KB
- references/long-running-agents.md 1.8 KB
- references/maintainability-findings.md 8.0 KB
- scripts/check_agent_context.py 16 KB runs code
- scripts/check_doc_refs.py 3.3 KB runs code
- scripts/check_maintainability.py 25 KB runs code
- scripts/check_verifier_output.py 3.5 KB runs code
- scripts/check-agent-context.sh 273 B runs code
- scripts/check-doc-refs.sh 318 B runs code
- scripts/check-maintainability.sh 488 B runs code
- scripts/check-verifier-output.sh 275 B runs code
- scripts/collect-data.sh 22 KB runs code
- scripts/run-health.ps1 4.3 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.
- 12d ago First seen · 217 lines · 71 tokens per session scan A 9579f3689147
health is a skill published in the GitHub repository sommio/qwen-omni-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 3,882 once invoked, about $0.0004 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.
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