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 romiluz13/cc10x --skill debugginggit clone --depth 1 https://github.com/romiluz13/cc10xWrote 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/romiluz13/cc10x/debugging)<a href="https://agentmods.dev/skills/romiluz13/cc10x/debugging"><img src="https://agentmods.dev/badge/skills/romiluz13/cc10x/debugging/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/romiluz13/cc10x/debugging"><img src="https://agentmods.dev/badge/skills/romiluz13/cc10x/debugging.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 | $0.00049 | $0.02728 |
| Opus 5.5 | $0.00020 | $0.01091 |
| Sonnet 5.5 | $0.00010 | $0.00546 |
| Haiku 4.5 | $0.00005 | $0.00273 |
Grade A, and why
debugging scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. `curl`/HTTP request with asserted response How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging
Feedback Loop FIRST: No hypothesis without a repro loop. No fix without root cause. No fix without blast radius scan.
Reference Files
references/investigation-hygiene.md— investigation discipline, evidence handlingreferences/root-cause-playbooks.md— scenario-specific debugging playbooks
Feedback Loop FIRST (Before Any Hypothesis)
A hypothesis without a repro loop is a guess. Before H1, build a fast, deterministic, agent-runnable signal that turns red on the bug.
Construction Ladder (try in rank order, stop at first that works — ordered by loop tightness: earlier rungs are faster and more deterministic)
- Failing automated test (unit/integration) — best: lives at a seam, reusable as RED
curl/HTTP request with asserted response- CLI snapshot diff (run command, diff stdout/stderr/exit)
- Headless browser script (real DOM/runtime crash)
- Trace replay (recorded request/log/event re-run)
- Throwaway harness (tiny script calling the suspect function)
- Property/fuzz check (when failing input is unknown)
git bisect run(regression with existing test)- Differential old-vs-new (last-good vs HEAD behavior diff)
- Human-in-the-loop (LAST resort: scripted manual steps)
Tighten the loop — treat it as a product. Once you have a loop, keep tightening:
- Faster? Cache setup, skip unrelated init, narrow the test scope — sub-second beats sub-minute.
- Sharper signal? Assert the exact failing fact, not a noisy superset — never just "didn't crash".
- More deterministic? Pin time, seed RNG, isolate filesystem, freeze network — same input → same red, no drift. A 30-second flaky loop is barely better than none; a 2-second deterministic one is a debugging superpower.
Red-capable completion criteria — the loop is done when you can name one command (a script path, a test invocation, a curl) that you have already run at least once (paste the invocation and its output), and it is:
- Red-capable — drives the actual bug code path and asserts the user's exact symptom (can go red on this bug, green once fixed). Not "runs without erroring".
- Deterministic — same verdict every run (flaky bugs: a pinned, high reproduction rate).
- Fast — seconds, not minutes.
- Agent-runnable — you can run it unattended. No red-capable command, no hypothesis phase. If you catch yourself reading code to build a theory before this command exists, STOP.
What ships with it
5 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.
- 9d ago Changed · -3 tokens per session 05cc9c98ef48
- 1mo ago Changed · +1 lines 1e32fe960246
- 1mo ago First seen · 187 lines · 52 tokens per session scan A acda1cbfb648
debugging is a skill published in the GitHub repository romiluz13/cc10x (164 stars, last pushed 9d ago), licensed MIT. It adds 49 tokens to every session and 2,728 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
issue-analyze
GitHub Issue and PR review thread deep analysis with Codex blind verdict. Use when: analyzing issue root cause, classifying problems, investigation planning, triaging PR review comments for actionability. Not for: fixing bugs (use bug-fix), code exploration (use code-explore). Output: classified analysis + verdict…
code-explore
Pure Claude code investigation. Use when: tracing execution paths, understanding architecture, diagnosing issues. Not for: dual-perspective review (use code-investigate), code review (use codex-code-review). Output: analysis report with findings.
debug
Interactive debugging workflow with hypothesis-driven probe loop. Use when: unknown bugs, script errors, silent failures, troubleshooting. Not for: known bugs (use bug-fix), GitHub issue analysis (use issue-analyze), code understanding (use code-explore). Output: debug report with probe journal + root cause + fix.
code-investigate
Dual-perspective code investigation. Use when: deep code analysis needing both Claude and Codex perspectives. Not for: quick exploration (use code-explore), code review (use codex-code-review). Output: integrated findings from dual analysis.
git-investigate
Git history investigation. Use when: tracking code changes, finding where bugs were introduced, root cause analysis. Not for: code exploration (use code-explore), issue analysis (use issue-analyze). Output: history trace + root cause report.
simplify
Wrap-up refactoring — simplify code, eliminate duplication, preserve behavior.