Borrowing it
Nothing to install: this file belongs to AlexisBalayre/claude-code-power-config. 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/AlexisBalayre/claude-code-power-config/main/.claude/skills/diagnose/SKILL.mdgit clone --depth 1 https://github.com/AlexisBalayre/claude-code-power-configWrote 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/alexisbalayre/claude-code-power-config/diagnose)<a href="https://agentmods.dev/skills/alexisbalayre/claude-code-power-config/diagnose"><img src="https://agentmods.dev/badge/skills/alexisbalayre/claude-code-power-config/diagnose/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/alexisbalayre/claude-code-power-config/diagnose"><img src="https://agentmods.dev/badge/skills/alexisbalayre/claude-code-power-config/diagnose.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 | $0.00038 | $0.01453 |
| Opus 5 | $0.00019 | $0.00727 |
| Sonnet 5 | $0.00008 | $0.00291 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
diagnose scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
See [FEEDBACK-LOOP.md](FEEDBACK-LOOP.md) for the ten recipes (failing test, curl, replay, fuzz, bisect, etc.), how to tighten the loop itself, how to attack non-deterministic bugs, and what to do when you genuinely canno How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.
Phase 1 — Build a feedback loop
This is the skill. Everything else is mechanical. If you have a tight pass/fail signal for the bug — one that goes red on this bug — you will find the cause: bisection, hypothesis-testing, and instrumentation all just consume that signal. Without one, no amount of staring at code will save you.
Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.
See FEEDBACK-LOOP.md for the ten recipes (failing test, curl, replay, fuzz, bisect, etc.), how to tighten the loop itself, how to attack non-deterministic bugs, and what to do when you genuinely cannot build one.
Completion criterion — a tight loop that goes red
Phase 1 is done when the loop is tight and red-capable: you can name one command — a script path, a test invocation, a curl — that you have already run at least once (paste the invocation and its output), and that is:
- Red-capable — it drives the actual bug code path and asserts the user's exact symptom, so it can go red on this bug and green once fixed. Not "runs without erroring" — it must be able to catch this specific bug.
- Deterministic — same verdict every run (flaky bugs: a pinned, high reproduction rate, per FEEDBACK-LOOP.md).
- Fast — seconds, not minutes.
- Agent-runnable — you can run it unattended; a human in the loop only via
scripts/hitl-loop.template.sh.
If you catch yourself reading code to build a theory before this command exists, stop — jumping straight to a hypothesis is the exact failure this skill prevents. No red-capable command, no Phase 2.
Phase 2 — Reproduce + minimise
Run the loop. Watch it go red — the bug appears.
What ships with it
2 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.
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.
- 10d ago First seen · 103 lines · 38 tokens per session scan A a05112918dc7
diagnose is a skill published in the GitHub repository AlexisBalayre/claude-code-power-config (2 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,453 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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systematic-debugging
Use when debugging, diagnosing, or investigating any bug, test failure, flaky test, race condition, unexpected behavior, build failure, production incident, third-party breakage, root cause analysis, or performance regression before proposing fixes.