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 agentmods add skills/griffinwork40/agent-framework/diagnosenpx skills add griffinwork40/agent-framework --skill diagnosegit clone --depth 1 https://github.com/griffinwork40/agent-frameworkWhat 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 | $0.00046 | $0.00341 |
| Opus 5 | $0.00023 | $0.00170 |
| Sonnet 5 | $0.00009 | $0.00068 |
| Haiku 4.5 | $0.00005 | $0.00034 |
Grade A, and why
diagnose 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 3d 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.
This is a copy
100% identical to diagnose — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Gather context: read the failing test or bug description, relevant error output, and recent git changes. If no failing test exists yet, write a minimal reproducer test (or identify a concrete verification command) before proceeding — hypotheses need a pass/fail signal to validate against. Dispatch two sub-agents in parallel — one to search the codebase for code paths involved in the failure (subagent_type: research-agent, read-only), and one to check recent commits and diffs that could have introduced the regression (subagent_type: general-purpose — requires Bash for git log/git diff/git show). When both return, synthesize findings into 2–4 ranked hypotheses, each with a specific code location and proposed cause.
For each hypothesis, dispatch a sub-agent with isolation: "worktree" to apply a minimal speculative fix, run the test or verification command, and then run the broader related test suite to check for regressions. Run all hypothesis-testing agents in parallel. Collect results: which fixes passed, which didn't, and any regressions surfaced by the broader suite.
Report the validated root cause (the hypothesis whose fix passed), the speculative fix diff, and regression status from the broader test run. If no hypothesis passes, synthesize what was learned and form a second round of hypotheses. If the user approves the fix, apply it to the main worktree.
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.
- 3d ago First seen · 12 lines · 46 tokens per session scan A 9a54f97470dc
diagnose is a skill published in the GitHub repository griffinwork40/agent-framework (23 stars, last pushed 8d ago), licensed Apache-2.0. It adds 46 tokens to every session and 341 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to diagnose, differing in 0 lines, and is treated as a copy.
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