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 KevinZai/commander --skill ccc-systematic-debugginggit clone --depth 1 https://github.com/KevinZai/commanderWrote 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/kevinzai/commander/ccc-systematic-debugging)<a href="https://agentmods.dev/skills/kevinzai/commander/ccc-systematic-debugging"><img src="https://agentmods.dev/badge/skills/kevinzai/commander/ccc-systematic-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/kevinzai/commander/ccc-systematic-debugging"><img src="https://agentmods.dev/badge/skills/kevinzai/commander/ccc-systematic-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 | $0.00058 | $0.01927 |
| Opus 5.5 | $0.00023 | $0.00771 |
| Sonnet 5.5 | $0.00012 | $0.00385 |
| Haiku 4.5 | $0.00006 | $0.00193 |
Grade A, and why
ccc-systematic-debugging 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 10d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
$ccc-systematic-debugging — root cause before fix
CC Commander · $ccc-systematic-debugging · a four-phase process that finds the cause, proves it, and fixes it once.
NO FIX WITHOUT CONFIRMED ROOT CAUSE
Guess-and-check fixes waste time and create new bugs; quick patches mask the real issue. Complete each phase before starting the next.
When to use / when not to
- Use for any bug, failing or flaky test, crash, wrong output, or error whose cause is not yet proven.
- Use even when the issue looks simple; the process is fast for simple bugs.
- Do not use for a known, already-diagnosed change (a typo the user pointed at, a requested refactor). Make that edit directly.
- For investigations expected to run past ~20 turns, span several systems, or need hypothesis state held across cycles, delegate (see "Decision points").
What happens
Start from $ARGUMENTS (the symptom, error text or failing test). Keep a short hypothesis log in the conversation: hypothesis, evidence, result, per cycle.
Phase 1 — Root-cause investigation (before any fix)
- Read the error completely: full stack trace, line numbers, error codes. Do not skim.
- Reproduce it. Run the exact failing command and capture the output, for example the single test:
npx vitest run path/to/file.test.ts -t "<name>",pytest path/to/test_x.py::test_name -x, ornode --test path/to/file.test.js. If it does not reproduce reliably, gather more data before going on; for flaky failures, run it in a loop (for i in $(seq 1 20); do <cmd> || break; done) and record the failure rate. - Check recent changes:
git log --oneline -15,git diff,git diff HEAD~5 --stat, new dependencies (git diff HEAD~5 -- package.json package-lock.json pyproject.toml), config changes. When a known-good commit exists,git bisectwith the reproduction command finds the change. - Instrument component boundaries in multi-component systems: log what enters and exits each boundary and confirm config propagates. Run once to find where it breaks before reasoning about why.
- Trace the bad value backward through the call stack to where it originates. The fix belongs at the source, not at the symptom.
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.
- 10d ago Changed · +27 lines · +16 tokens per session debd102ee161
- 1mo ago First seen · 119 lines · 42 tokens per session scan A 442081b0d876
ccc-systematic-debugging is a skill published in the GitHub repository KevinZai/commander (6 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,927 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 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use before proposing a permanent fix for bugs, test failures, or unexpected behavior with an unknown root cause—mandatory when symptoms are far from the cause, a prior fix failed, or the issue crosses components. Reproduce, trace backward to the root cause, fix once at the source, and add a regression test. Do not…
performance-profiler
Tier: POWERFUL Category: Engineering Domain: Performance Engineering.
advisor-strategy
Use when facing complex architectural decisions, debugging dead-ends, high-stakes code changes, security reviews, performance optimization, or when the user explicitly requests advisor consultation. Also use when context is growing large and needs management, or when a second opinion from a more capable model would…
sandbox-unblock
Diagnostic protocol to run before reporting a sandbox blocker or asking for a configuration change. Eleven checks that eliminate false positives, then a report template the person holding the settings can act on. On one measured day, six of eight reported blockers turned out to be false, all from the same handful of…
forge-diagnose
Diagnose runtime infrastructure issues — cold starts, timeouts, scaling problems, network failures. Use when asked about "infra is slow", "cold starts", "network issues", "why is this timing out", "scaling problem", "latency spikes", or "service is down".
fix-bug
Run the Fix Validation pipeline to investigate, fix, and validate a bug. Ensures deterministic pipeline execution with IssueAnalyzer, FixWriter, TestWriter (conditional), TestAudit (conditional), and FixValidator stages.