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/supersynergy/awesome-agentic-coding/debugnpx skills add Supersynergy/awesome-agentic-coding --skill debuggit clone --depth 1 https://github.com/Supersynergy/awesome-agentic-codingWhat 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 | $0.00019 | $0.00314 |
| Opus 5 | $0.00010 | $0.00157 |
| Sonnet 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 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.
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.
- yesterday 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 1mo ago), licensed MIT. It adds 19 tokens to every session and 314 once invoked, about $0.0001 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.
Other skills, from other repositories
aiwiki-research-refresh-privacy
定期为 AiWiki「LLM 隐私保护」主题做一次「高质量一手来源研究 → 去重 → 三道硬闸门把关 → 走隐私条目流水线 → 过闸门」的扩充。当需要发现并新增 LLM 隐私攻防新选题（尤其由月度定时触发器拉起的无人值守会话），且要保证一手出处、不与现有条目重复、不制造假安全时使用。.
aiwiki-privacy-entry-author
为 AiWiki「LLM 隐私保护」主题撰写或修订一条攻防条目（privacy/ 下的 .mdx）。当需要新增/改写隐私条目、并保证第一人称红线、九节结构、三道硬闸门、隐私 frontmatter 与一手出处时使用。.
aiwiki-research-refresh
定期为 AiWiki 做一次「高质量来源研究 → 去重 → 质量把关 → 走条目流水线 → 过闸门」的扩充。当需要发现并新增「AI 使用误区」新选题（尤其由月度定时触发器拉起的无人值守会话），且要保证来源质量、不与现有条目重复时使用。.
aiwiki-entry-author
为 AiWiki 撰写或修订一条「误区」条目（docs/ 下的 .mdx）。当需要新增/改写误区条目、并保证第一人称 AI 声音、七段结构、frontmatter 规范与可核查出处时使用。.
aiwiki-translator
把一条 AiWiki 中文误区条目（docs/ 下的 .mdx）翻译成英文镜像，输出到 i18n/en/ 对应路径。当需要为新增或修订的中文条目生成/更新英文版时使用。.
issue-triage
Issue triage: audit open issues, categorize, detect duplicates, cross-ref PRs, risk assessment, post comments. Args: "all" for deep analysis of all, issue numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.