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/axgord/claude-workflow/debuggingnpx skills add AxGord/claude-workflow --skill debugginggit clone --depth 1 https://github.com/AxGord/claude-workflowWhat 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 | $0.00016 | $0.01944 |
| Opus 5 | $0.00008 | $0.00972 |
| Sonnet 5 | $0.00003 | $0.00389 |
| Haiku 4.5 | $0.00002 | $0.00194 |
Grade A, and why
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 2d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging — Meta-Patterns
Not tooling (that's debug-bridge*) — patterns for the FIX process itself, each distilled from a real multi-round failure.
Repeated Same Symptom After 2+ Fixes = Wrong Subsystem Target
When the user reports the same perceived symptom after two or more rounds of plausible fixes, stop patching that surface. A symptom that survives multiple reasonable fixes is almost always at the seam between two decoupled subsystems — a baked/precomputed path vs. a live event; a cached value vs. its source; a predicted value vs. an independently-computed actual.
DON'T: Keep refining one side each iteration — shapes, easing, constants, thresholds — to match the user's latest wording.
DO: By the 2nd repeat, trace BOTH subsystems end-to-end and ask: "why do these two representations exist, and must they?" The fix is to make them coincide — re-derive one from the other at the authoritative moment — not to tune one side.
Tell: Every "fix" addresses the user's latest description verbatim, yet the user keeps saying "same thing / didn't help." You find yourself adjusting constants repeatedly with no lasting effect.
GOTCHA: The seam is invisible when you look at only one subsystem. You must trace both from their shared input to their diverging output paths to see the gap. (Real case: four rounds of tuning a precomputed trajectory failed because the actual hit was computed by a SEPARATE live simulation — the fix was re-deriving one from the other at start time, which no amount of trajectory tuning could achieve.)
Regressing Feature? Find the Principled Algorithm the Codebase Already Has
When a feature keeps regressing across many patch cycles — tuning constants, reshaping curves, adjusting thresholds — stop inventing ad-hoc logic. Ask: "what does correct behavior fundamentally require, and does this codebase already compute that for some other case?"
DON'T: Keep hand-rolling case-specific heuristics to chase the latest symptom. Each variant is "more specific" than the last and never converges.
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.
- 2d ago First seen · 124 lines · 16 tokens per session scan A a982296c9da0
debugging is a skill published in the GitHub repository AxGord/claude-workflow (5 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 1,944 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 2026-08-31.
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…