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/abuango/pos-ai/debuggingnpx skills add abuango/pos-ai --skill debugginggit clone --depth 1 https://github.com/abuango/pos-aiWhat 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 | $0.00056 | $0.01302 |
| Opus 5 | $0.00028 | $0.00651 |
| Sonnet 5 | $0.00011 | $0.00260 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade C, and why
debugging scanned grade C 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 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Check `.next/` cache — `rm -rf .next` if stale How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Skill
You are systematically debugging an issue. Follow this structured approach — never brute-force retry the same thing. Every step narrows the search space.
Setup
Before starting: check .handoff/sessions/ for active sessions, read context status.yaml, run git status. Follow .rules/universal.md (Plan -> Approve -> Execute).
Process
Step 1: Reproduce
Before anything else, confirm the bug exists and is reproducible.
- Get the error — Exact error message, stack trace, or unexpected behavior description
- Get the context — When did it start? What changed recently? Which environment?
- Reproduce locally — Run the failing command/test/request and capture the full output
- If not reproducible — Check environment differences, data differences, timing/race conditions
Output: "I can reproduce this: {exact steps and output}" — OR — "Cannot reproduce. Need: {what additional info is required}"
Step 2: Isolate
Narrow down where the bug lives.
- Check recent changes —
git log --oneline -20,git difffor uncommitted work - Trace the execution path — Follow the request/data from entry point to failure
- Binary search — If the codebase is large, bisect: does the bug exist in module A or B?
- Check boundaries — Is the bug in our code, a dependency, the database, or the infrastructure?
Key questions:
- Does it fail with the simplest possible input?
- Does it fail in a different environment?
- Does reverting the last change fix it?
- Is it data-dependent?
Output: "The bug is in {file}:{line_range} because {evidence}"
Step 3: Diagnose
Understand the root cause, not just the symptom.
- Read the code — Read the actual implementation, don't assume
- Check assumptions — What does the code assume about inputs, state, ordering?
- Look for common patterns:
- Null/undefined access — Missing null checks, optional chaining needed
- Race condition — Async operations completing in unexpected order
- State mutation — Shared state modified unexpectedly
- Type mismatch — String vs number, date formats, encoding
- Off-by-one — Array bounds, pagination, date ranges
- Missing migration — Database schema doesn't match model
- Cache stale — OPcache, view cache, query cache serving old data
- Environment mismatch — .env values, Docker config, service URLs
- Dependency version — Breaking change in updated package
What ships with it
1 file 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.
- 2d ago First seen · 142 lines · 56 tokens per session scan C 60e72b0f3088
debugging is a skill published in the GitHub repository abuango/pos-ai (2 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 1,302 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). 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…