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/brain-bootstrap/claude-code-brain-bootstrap/debugnpx skills add brain-bootstrap/claude-code-brain-bootstrap --skill debuggit clone --depth 1 https://github.com/brain-bootstrap/claude-code-brain-bootstrapWhat 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 | $0.00043 | $0.00526 |
| Opus 5 | $0.00022 | $0.00263 |
| Sonnet 5 | $0.00009 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
debug 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 2d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Skill
Systematic root cause analysis.
When to use
- An error is happening and the cause is not obvious
- A test is failing unexpectedly
- Behavior changed after a refactor
- Production incident requiring root cause identification
5-Step Investigation Method
Step 1 — OBSERVE: Gather exact evidence
Do NOT guess. Collect all available evidence first:
- Exact error message (full stack trace, not summary)
- Exact input that triggers the failure
- Exact expected vs. actual output
- When it started failing (which commit? which change?)
- Environment where it fails (dev? CI? prod? all?)
Step 2 — HYPOTHESIZE: Form concrete hypotheses
Based on the evidence, generate 2-3 specific hypotheses:
- Each hypothesis must be falsifiable (testable)
- Rank by probability based on evidence
- Do NOT assume the most complex explanation — prefer simple causes
Step 3 — TRACE BACKWARDS: Follow the execution path
From the failure point, trace backwards:
grep -rn --color=never '<error_symbol_or_message>' . | head -20
- Find the function that threw the error
- Find its caller
- Find the caller's caller
- Keep tracing until you reach user input or a boundary
Step 4 — BINARY SEARCH: Narrow the scope
If the trace is long, use bisection:
- Does it fail with the same input after reverting X? (git bisect or manual)
- Does it fail with minimal input? (reduce the test case)
- Does it fail in isolation? (extract the failing unit)
Step 5 — PROVE: Verify the root cause
Before fixing, prove you found the right root cause:
- Write a test that reproduces the bug exactly
- Verify the test fails (red) with the current code
- Apply the fix
- Verify the test passes (green)
- Run the full test suite — no regressions
Rules
- NEVER fix before proving the root cause
- NEVER submit a fix without a reproducing test
- If two hypotheses remain after Step 4, test the simpler one first
- Document the root cause in
claude/tasks/lessons.mdif it's a non-obvious pattern
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.
- 2d ago First seen · 61 lines · 43 tokens per session scan A f93eccae9309
debug is a skill published in the GitHub repository brain-bootstrap/claude-code-brain-bootstrap (11 stars, last pushed 4mo ago), licensed MIT. It adds 43 tokens to every session and 526 once invoked, about $0.0002 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.
Other skills, from other repositories
implement
TRIGGER when: user asks to implement, fix, build, or work on something — whether from a docs/wip plan OR a standalone task (bug fix, GitHub issue, one-off change). Examples: "work on task 1", "fix this bug", "implement feature X from the issue". Provides structured execution with profile detection, dependency…
review-spec
Use after implementing tasks or mid-feature to verify code matches design docs and ensure they are in sync. Detects spec deviations, missing implementations, doc inconsistencies, and outdated docs in design and implementation documentation.
chain-of-verification
Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use when complex questions require fact-checking, technical accuracy, or multi-step reasoning.
review-design
Review design, implementation, and task documents produced by design. Evaluates document quality, internal consistency, and technical soundness. Use after design completes and before starting implement.
review-code
Code review of current git changes with an expert senior-engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements. Use when performing code reviews.
dependency-handling
TRIGGER when: adding or upgrading any dependency — library, SDK, framework, API, IaC API version (K8s/Terraform/Helm), CRD, or container image. Use BEFORE writing the call. Forces context7/capy lookup instead of guessing.