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/toffyui/ccteams/debug-playbooknpx skills add toffyui/ccteams --skill debug-playbookgit clone --depth 1 https://github.com/toffyui/ccteamsWrote 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/toffyui/ccteams/debug-playbook)<a href="https://agentmods.dev/skills/toffyui/ccteams/debug-playbook"><img src="https://agentmods.dev/badge/skills/toffyui/ccteams/debug-playbook.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.02103 |
| Opus 5 | $0.00019 | $0.01052 |
| Sonnet 5 | $0.00008 | $0.00421 |
| Haiku 4.5 | $0.00004 | $0.00210 |
Grade A, and why
debug-playbook 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 4d 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.
test (`<runner> <path>::<test>`), the one request (`curl` the endpoint), How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Playbook
This is the literal procedure a frontier model follows when debugging. Follow it step by step; the order is the point. Skipping ahead to "the obvious fix" is the single most expensive mistake in debugging.
Operating loop
- Capture the failure verbatim. Copy the exact error text, stack trace, failing assertion, or wrong output into your notes before touching anything. You will compare against this later — after a change, "a different error" and "the same error" are opposite results, and memory will lie to you about which one you're seeing.
- Reproduce before reasoning. Run the smallest thing that fails: the one
test (
<runner> <path>::<test>), the one request (curlthe endpoint), the one function (a REPL call). If you cannot reproduce, STOP — collect environment facts (versions, env vars, data state) and report what's missing. Never debug from a description of a failure you haven't seen.- Suspected flaky? Run it 10× in a loop and record the failure rate before anything else. A 3/10 failure is a different class of bug (ordering, time, shared state) than a 10/10 failure.
- Read the trace bottom-up, and find YOUR frame. The deepest frame in code you own is where evidence starts. The reported line is where the error surfaced, not where the bug lives — treat it as the end of a thread to pull, not the answer.
- Write a hypothesis ledger. List 2–3 candidate causes explicitly (in a comment, scratch file, or your working notes). For each: what it predicts you'd observe, and the cheapest check that would kill it. Rank by (prior probability × cheapness of the check).
- Run the discriminating experiment. Pick the check that produces a
DIFFERENT result under hypothesis A vs hypothesis B — not the one that
merely confirms your favorite. Targeted print/log at a boundary, a
narrowed input, a bisected commit range (
git bisect), or commenting out one layer are all discriminators. One change at a time; revert each probe before the next. - State the mechanism sentence. Before writing any fix, you must be able to say: "X causes Y because Z" — where you have OBSERVED X and Y, and Z explains why this exact symptom appears (not merely "could appear"). If you cannot fill in Z, you have a correlation, not a cause. Return to step 4.
- Fix at the cause site, minimally. The fix belongs where the mechanism sentence says the defect is — not where the error surfaced. If the fix feels large, re-check: you may be redesigning around the bug instead of fixing it.
- Regression test that fails first. Write the test, run it against the
UNFIXED code (stash the fix:
git stash, run,git stash pop), confirm it fails with the captured failure — then confirm it passes with the fix. A regression test that never failed proves nothing. - Hunt the siblings. The same defect pattern usually exists elsewhere:
grepfor the same call, the same copy-pasted block, the same misused API. Report siblings even if fixing them is out of scope. - Full suite + cleanup. Run the whole test suite, remove every probe and debug print, and re-read your diff — the diff should contain the fix and the test, nothing else.
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.
- 4d ago First seen · 154 lines · 39 tokens per session scan A 2c4a65c537ab
debug-playbook is a skill published in the GitHub repository toffyui/ccteams (47 stars, last pushed 4d ago), licensed MIT. It adds 39 tokens to every session and 2,103 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 2026-08-30.
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