Borrowing it
Nothing to install: this file belongs to dungnotnull/workshop-air-pollution-monitoring-agent-skill. 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/dungnotnull/workshop-air-pollution-monitoring-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/workshop-air-pollution-monitoring-agent-skillWrote 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/instructions/dungnotnull/workshop-air-pollution-monitoring-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/workshop-air-pollution-monitoring-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/workshop-air-pollution-monitoring-agent-skill/claude-md/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/instructions/dungnotnull/workshop-air-pollution-monitoring-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/workshop-air-pollution-monitoring-agent-skill/claude-md.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 | $0.01679 | $0.01679 |
| Opus 5 | $0.00839 | $0.00839 |
| Sonnet 5 | $0.00336 | $0.00336 |
| Haiku 4.5 | $0.00168 | $0.00168 |
Grade A, and why
workshop-air-pollution-monitoring-agent-skill CLAUDE.md 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Skill 284: workshop-air-pollution-monitoring
Skill Identity
- Skill Name:
workshop-air-pollution-monitoring - Tagline: Workshop Indoor Air Pollution Monitoring & Alert — Industrial Workshop Indoor Air Quality & Safety evidence-backed analysis harness.
- Current Phase: Phase 5 — Integration & Polish (PRODUCTION READY v2.0.0)
- Version: 2.0.0
- Folder:
D:\972026\284-workshop-air-pollution-monitoring\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Industrial Workshop Indoor Air Quality & Safety. It gathers authoritative real-time and reference data, applies recognized domain methods, cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources — continuously self-improving through an automated knowledge crawl pipeline.
Harness Flow Summary
/workshop-air-pollution-monitoring invoked
│
├─ PRE-FLIGHT HOOKS (language · context budget · registry validate)
│
├─ Step 1: sub-router → chain-of-thought plan selection
├─ Step 2: sub-gather-requirements → structured requirements
├─ Step 3: sub-evidence-collector → tiered evidence bundle + degradation level
├─ Step 4: sub-core-analysis → pollutants/limits, ventilation/LEV, sensors, controls
├─ Step 5: sub-knowledge-updater → knowledge-base citations + gap flagging
├─ Step 6: sub-advisor → risk-disclosed conclusion + evidence chain
│
├─ PRE-DELIVERY HOOK → enforce U1–U6 + G1–G4 (auto-fix x2)
└─ POST-DELIVERY HOOK → queue knowledge gaps, metrics, structured log
The router enables non-linear execution: comparison runs core-analysis
twice; risk_assessment forces multi-scenario output; educational deepens
knowledge-updater; degraded short-circuits retrieval.
Sub-Skills
| File | Purpose |
|---|---|
skills/main.md |
Orchestrator + quality gate + output template. |
skills/sub-router.md |
Chain-of-thought router → execution plan. |
skills/sub-gather-requirements.md |
Intake: object, scope, timeframe, inputs, audience, language. |
skills/sub-evidence-collector.md |
Authoritative real-time + reference data; tiered bundle. |
skills/sub-core-analysis.md |
Pollutants vs limits, ventilation/LEV, sensors/alerts, control hierarchy. |
skills/sub-knowledge-updater.md |
Knowledge-base citations + gap flagging for the crawl pipeline. |
skills/sub-advisor.md |
Risk-disclosed synthesis: verdict, scenarios, risks, evidence chain. |
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 · 158 lines · 1,679 tokens per session scan A 5a9a785f6c3f
workshop-air-pollution-monitoring-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/workshop-air-pollution-monitoring-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,679 tokens to every session, about $0.0084 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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