Borrowing it
Nothing to install: this file belongs to ScottDuncanAI/industrial-ai-troubleshooting-agent. 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/ScottDuncanAI/industrial-ai-troubleshooting-agent/main/CLAUDE.mdgit clone --depth 1 https://github.com/ScottDuncanAI/industrial-ai-troubleshooting-agentWrote 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/scottduncanai/industrial-ai-troubleshooting-agent/claude-md)<a href="https://agentmods.dev/instructions/scottduncanai/industrial-ai-troubleshooting-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/scottduncanai/industrial-ai-troubleshooting-agent/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/scottduncanai/industrial-ai-troubleshooting-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/scottduncanai/industrial-ai-troubleshooting-agent/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 | $0.06180 | $0.06180 |
| Opus 5 | $0.03090 | $0.03090 |
| Sonnet 5 | $0.01236 | $0.01236 |
| Haiku 4.5 | $0.00618 | $0.00618 |
Grade A, and why
industrial-ai-troubleshooting-agent 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 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 — 435 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Boiler Historian — System Reference
This system connects an LLM to four data sources for a coal-fired CFB boiler at a chemical plant in Zhejiang, China: a time-series historian (5 days of sensor data), a DCS alarm log (6,005 alarm events derived from the historian), a process knowledge graph (equipment topology and relationships), and a plant document library (62 procedures, datasheets, and guides). Together they enable natural-language operations queries grounded in real data and real documentation.
Critical Rules — Read First
These apply to every response, no exceptions:
-
Never read files directly. Do not use
Read,Grep, orGlobon theRAG docs/folder or any project file. All document access must go through the MCP tools (docs_search,docs_list_documents,docs_get_document). -
Always resolve relative time before any historian query. If the user says "past 3 hours", "last day", or "this morning", call
historian_get_data_rangefirst to get the latest timestamp, then compute the absolute start time. Never guess a time window. -
Always cite sources when quoting procedures or limits. Format: "Per [Document Title], Rev [X.X]: ..." — include document title and revision for every piece of procedural or specification information you present.
-
Do not invent tag names. Only use tags confirmed by
historian_list_tagsorhistorian_search_tags. If a tag name is uncertain, search first. -
Flag anomalies proactively. Whenever
TE_8332Ais in scope and its value is outside 530–545°C, call it out explicitly as outside the normal operating range, even if the user didn't ask about it directly.
The Four Data Sources
1. Historian (DuckDB)
- File:
boiler_historian.duckdb - Data: 86,400 rows × 30 tags at 5-second intervals
- Time range:
2022-03-27 14:28:54→2022-04-01 14:28:49(5 days) - This is a static snapshot. There is no live feed. "Now" = the latest timestamp.
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 · 435 lines · 6,180 tokens per session scan A 0b461397f278
industrial-ai-troubleshooting-agent CLAUDE.md is an instructions file published in the GitHub repository ScottDuncanAI/industrial-ai-troubleshooting-agent (59 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 6,180 tokens to every session, about $0.0309 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 2026-08-30.
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