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 skills add Supersynergy/awesome-agentic-coding --skill debuggit clone --depth 1 https://github.com/Supersynergy/awesome-agentic-codingWrote 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/supersynergy/awesome-agentic-coding/debug)<a href="https://agentmods.dev/skills/supersynergy/awesome-agentic-coding/debug"><img src="https://agentmods.dev/badge/skills/supersynergy/awesome-agentic-coding/debug/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/supersynergy/awesome-agentic-coding/debug"><img src="https://agentmods.dev/badge/skills/supersynergy/awesome-agentic-coding/debug.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 | $0.00019 | $0.00314 |
| Opus 5.5 | $0.00008 | $0.00126 |
| Sonnet 5.5 | $0.00004 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00031 |
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 1mo 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.
What it actually says
Scientific Debugging Protocol
Bug: $ARGUMENTS
Step 1: Observe
- What is the expected behavior?
- What is the actual behavior?
- When did it start? (check
git log --oneline -20) - Can you reproduce it? How?
Step 2: Hypothesize (list 3 possible causes)
Based on the code, list the 3 most likely root causes:
- Hypothesis A: ...
- Hypothesis B: ...
- Hypothesis C: ...
Step 3: Test Each Hypothesis
For each hypothesis:
- Add a targeted log/print statement OR read the relevant code
- Run the reproduction steps
- Confirm or eliminate the hypothesis
- Move to next hypothesis if eliminated
Step 4: Fix
Once root cause is confirmed:
- Make the minimal fix
- Run existing tests
- Add a test that would have caught this bug
Step 5: Verify
- Reproduce the original bug scenario — it should be fixed
- Run full test suite — nothing else should break
Rules
- Never guess the fix without confirming the root cause
- Never fix symptoms (e.g., adding try/catch around the crash)
- Document the root cause in the commit message
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.
- 1mo ago First seen · 45 lines · 19 tokens per session scan A d6b24a87a86c
debug is a skill published in the GitHub repository Supersynergy/awesome-agentic-coding (1 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 314 once invoked, about $0.0001 per session on Opus 5.5. 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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investigate
Systematic root-cause debugging: find the cause before writing any fix.
qa
Systematic QA testing of a web application: diff-aware, tiered, with fix-and-verify loop.
optimize
Analyze and suggest performance improvements for code, queries, or systems.