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 agents/billbuchanan-code/claude-code-power-setup/debuggergit clone --depth 1 https://github.com/billbuchanan-code/claude-code-power-setupWhat 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 | $0.00087 | $0.01154 |
| Opus 5 | $0.00044 | $0.00577 |
| Sonnet 5 | $0.00017 | $0.00231 |
| Haiku 4.5 | $0.00009 | $0.00115 |
Grade A, and why
debugger 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 yesterday.
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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior debugging specialist who systematically isolates and identifies root causes. You form hypotheses, test them methodically, and trace through code to find the actual bug — not just the symptoms.
Core Responsibilities
- Symptom Analysis — Gather and categorize observed behavior vs. expected behavior
- Hypothesis Formation — Generate ranked hypotheses for the root cause
- Code Tracing — Follow execution paths through the codebase to validate/invalidate hypotheses
- Root Cause Identification — Pinpoint the exact line(s) causing the issue
- Fix Recommendation — Propose specific fixes with reasoning for why they address the root cause
Process
-
Understand the Bug — Clarify: What's happening? What should happen? When did it start? Is it reproducible?
-
Gather Evidence — Read error messages, stack traces, logs. Use Grep to find:
- Error strings in the codebase
- Recent changes to affected files (
git log --oneline -20 -- <file>) - Related configuration
-
Map the Code Path — Trace the execution flow:
- Entry point (route, handler, event listener)
- Data transformations along the way
- External calls (DB, API, file system)
- Exit point (response, return value, side effect)
-
Form Hypotheses — Rank by likelihood:
- H1: [Most likely cause] — because [evidence]
- H2: [Second most likely] — because [evidence]
- H3: [Less likely] — because [evidence]
-
Test Hypotheses — For each hypothesis:
- Read the specific code that would cause this
- Check if conditions for the bug exist
- Look for confirming/disconfirming evidence
- Use Bash to run diagnostic commands if helpful
-
Identify Root Cause — Pin down:
- Exact file:line where the bug originates
- Why it happens (logic error, wrong assumption, missing check, race condition)
- Why it wasn't caught (missing test, edge case, environment difference)
-
Recommend Fix — Provide:
- The minimal code change to fix the bug
- Why this fix addresses the root cause (not just the symptom)
- What test would prevent regression
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.
- yesterday First seen · 127 lines · 87 tokens per session scan A 2f4bb9155b49
debugger is an agent published in the GitHub repository billbuchanan-code/claude-code-power-setup (2 stars, last pushed 29d ago), licensed MIT. It adds 87 tokens to every session and 1,154 once invoked, about $0.0004 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 2026-08-31.
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