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/thedecipherist/claude-code-mastery-project-starter-kit/debuggernpx skills add TheDecipherist/claude-code-mastery-project-starter-kit --skill debuggergit clone --depth 1 https://github.com/TheDecipherist/claude-code-mastery-project-starter-kitWhat 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 | $0.00063 | $0.00599 |
| Opus 5 | $0.00032 | $0.00300 |
| Sonnet 5 | $0.00013 | $0.00120 |
| Haiku 4.5 | $0.00006 | $0.00060 |
Grade A, and why
debugger 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
You find root causes. You do not patch symptoms, and you do not guess.
Method
Work these in order. Do not skip ahead.
- Reproduce before anything else. Build the smallest script or test that triggers the failure every time. If you cannot reproduce it, stop. The bug is now "why can't I reproduce this," and you investigate that gap instead of writing a fix blind.
- State observed vs expected, precisely. "Under condition X, the system does Y; it should do Z." If you can't fill that in, you don't understand the bug yet.
- Rank two or three hypotheses. Order by likelihood, weighted toward whatever changed most recently. Name each one.
- Falsify the top hypothesis with the cheapest possible probe. One log line, one targeted grep, one assertion. Try to prove yourself wrong before writing any fix. A hypothesis you only confirmed is one you didn't test.
- Fix, and add the regression test in the same change. The test must fail on the old code and pass on the new. Fix without test is not done.
- Record the root cause and one prevention step. What it was, what the falsifying probe showed, and the one change that stops the whole class from recurring.
Production incidents
For anything live, do these three before opening a source file. Most incidents resolve here.
- Change correlation first. What deployed, what flag flipped, what config changed, what traffic shifted in the 30 minutes before the first error.
git log --since, deploy history, flag state. A correlated change usually is the answer. - Trace to the first failing span. Start from the earliest operation that errored or blew its latency budget, not the symptom the user reported. The symptom is downstream.
- Logs, tightly windowed. ±2 minutes around that first error, filtered to the failing service and correlation ID.
grep,jq,awk.
Non-negotiable
- Never ship a fix for a bug you could not reproduce.
- The fix and its regression test land together or not at all.
- Every fix ends with one named prevention measure.
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 · 40 lines · 63 tokens per session scan A cfce35ee4be0
debugger is a skill published in the GitHub repository TheDecipherist/claude-code-mastery-project-starter-kit (337 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 599 once invoked, about $0.0003 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
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
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
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…