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 thienanblog/awesome-ai-agent-skills --skill debugging-workflowgit clone --depth 1 https://github.com/thienanblog/awesome-ai-agent-skillsWrote 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/thienanblog/awesome-ai-agent-skills/debugging-workflow)<a href="https://agentmods.dev/skills/thienanblog/awesome-ai-agent-skills/debugging-workflow"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/debugging-workflow/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/thienanblog/awesome-ai-agent-skills/debugging-workflow"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/debugging-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.00419 |
| Opus 5.5 | $0.00017 | $0.00168 |
| Sonnet 5.5 | $0.00009 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
debugging-workflow 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 27d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Workflow
Use evidence to locate the failing boundary and fix the cause while preserving business intent.
Delegation
Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.
Investigate
- Capture the failing command or flow, input, expected behavior, actual result, and relevant environment. Read the error and nearby source before expanding the search.
- Reproduce the smallest useful case when practical. If reproduction is unavailable, distinguish what logs or source prove from what remains a hypothesis.
- Trace the symptom through the affected boundaries. Choose checks that distinguish plausible causes; independent observations can run together when safe. Avoid stacking speculative fixes.
- For a UI failure, inspect the supplied image or rendered state. Annotate a copy only if that makes a material ambiguity easier to resolve.
Read debugging-playbook.md for deeper isolation, flaky failures, or temporary instrumentation. Keep sensitive data out of logs and remove temporary debug code.
Fix and verify
Make the smallest coherent fix, preserving unusual business rules unless evidence shows they are the defect. Avoid unrelated refactors. Add a regression test when it provides durable protection, ideally demonstrating failure before the fix.
Rerun the original failure and affected checks. Complete repository-required checks; broaden only when a remaining risk warrants it and authorization permits. Reuse results that remain valid.
If measurement or test design becomes the main work, consult the relevant specialist guidance directly when available.
Report
Explain the cause, fix, and evidence that the failure is resolved. Include reproduction limits or remaining uncertainty. Record a difficult bug in existing durable docs only when future work would benefit.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 27d ago Changed · -2 lines 495fdf5a4b5c
- 1mo ago Changed · -56 lines · -28 tokens per session 64e4b150ed77
- 1mo ago First seen · 92 lines · 71 tokens per session scan A f88158cf5b6f
debugging-workflow is a skill published in the GitHub repository thienanblog/awesome-ai-agent-skills (70 stars, last pushed 27d ago), licensed Apache-2.0. It adds 43 tokens to every session and 419 once invoked, about $0.0002 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 2026-08-30.
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