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/baphuongna/pi-crew/systematic-debuggingnpx skills add baphuongna/pi-crew --skill systematic-debugginggit clone --depth 1 https://github.com/baphuongna/pi-crewWhat 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 | $0.00013 | $0.01081 |
| Opus 5 | $0.00006 | $0.00541 |
| Sonnet 5 | $0.00003 | $0.00216 |
| Haiku 4.5 | $0.00001 | $0.00108 |
Grade A, and why
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 3d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
systematic-debugging
Core principle: no fixes without root-cause investigation first. Symptom patches create new bugs and hide the real failure.
Distilled from detailed reads of systematic-debugging, root-cause tracing, TDD, and error-analysis skill patterns.
Invocation — Read Before Debugging
Before beginning any debug session, recite these four steps:
1. First is reproducibility. Can the issue be reproduced reliably? 2. Know the fail path. Where does the code break and what stops it from breaking? 3. Question your hypothesis. What would disprove it? 4. Every run is a breadcrumb. Cross-reference all of them.
If the user says "skip the ritual" → skip the recitation but still apply the four phases silently.
Refuse Gate — Do NOT Proceed Without These
Before proposing ANY fix:
- Can you reproduce the issue reliably? (deterministic or >50% flake rate)
- Do you know the root cause? (confirmed mechanism, not a hypothesis)
- Have you tried to FALSIFY your hypothesis first? (disproof before proof)
If ANY answer is NO: → Stop. → State what's missing. → Do not propose a fix.
Exception: if the user explicitly says "just patch the symptom" — proceed but flag it as a symptom patch, not a root-cause fix.
Four Phases
1. Root Cause Investigation
Before any fix:
- read error messages, stack traces, failing assertions, task status, and logs completely;
- reproduce narrowly and record the exact command/steps;
- check recent diffs, commits, config changes, dependency changes, and environment differences;
- trace data/control flow across component boundaries;
- add temporary diagnostics only when they answer a specific question.
For pi-crew, trace:
user/tool params → config resolution → team/workflow/agent discovery → model/runtime routing → child args/env → state/events/artifacts → status/UI
2. Pattern Analysis
- Find a similar working path in the codebase.
- Compare working vs broken behavior field-by-field.
- Identify dependencies: config home, project root markers, env vars, locks, stale caches, provider model capabilities.
- Do not assume small differences are irrelevant.
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.
- 3d ago First seen · 126 lines · 13 tokens per session scan A 6ffffe4d7771
systematic-debugging is a skill published in the GitHub repository baphuongna/pi-crew (50 stars, last pushed 3d ago), licensed MIT. It adds 13 tokens to every session and 1,081 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
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brainstorming
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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