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/kraitdev/skill.md/debugging-strategiesnpx skills add KraitDev/skiLL.Md --skill debugging-strategiesgit clone --depth 1 https://github.com/KraitDev/skiLL.MdWhat 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 | $0.00017 | $0.01577 |
| Opus 5 | $0.00009 | $0.00788 |
| Sonnet 5 | $0.00003 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
debugging-strategies 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 yesterday.
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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Strategies
Purpose
"Guess and check" programming wastes hours. This skill replaces randomness with the scientific method: reproduce the bug reliably, isolate the failing component via binary search, formulate testable hypotheses, and apply minimal fixes. The goal is deterministic bug resolution, not random fixes that seem to work.
When to use
- Investigating a defect reported in production
- Facing a failing test with no obvious root cause
- Understanding unfamiliar or undocumented legacy code
- Debugging intermittent failures (flaky tests)
When NOT to use
- Performance optimization (use Caching Strategies or Database Query Optimization)
- Code review (use Code Review Guidelines)
- Architecture decisions (different concern)
Inputs required
- Failing code, test, or end-to-end scenario
- Ability to reproduce the failure
- Source code and execution environment
Workflow
- Reproduce the Bug: Consistently replicate the failure state. If you CANNOT reproduce it, STOP (get more details). Write a failing test.
- Isolate the Subsystem: Use binary search (comment out code halves, use
git bisect, add logging) to locate the exact module causing the issue - Formulate Hypothesis: Propose WHY the failure is happening based on logs, stack traces, and code inspection
- Test Hypothesis: Add targeted logging or step through debugger to verify assumptions about state/variables at runtime
- Apply Minimal Fix: Change ONLY what's necessary to make the test pass (single line if possible)
- Verify Fix: Confirm the failing test now passes, and no other tests break
- Add Regression Test: Ensure this bug never reoccurs
Rules
- MUST establish a reproducible test BEFORE changing code
- MUST only change one variable or line at a time during hypothesis testing
- MUST NOT apply multiple changes simultaneously (makes diagnosis impossible)
- MUST use binary search for isolation (comment out halves, not random guessing)
- MUST formulate a hypothesis before adding logging or changes
- MUST keep the fix minimal (change only what caused the bug)
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.
- yesterday First seen · 178 lines · 17 tokens per session scan A c637c5fc7141
debugging-strategies is a skill published in the GitHub repository KraitDev/skiLL.Md (7 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 1,577 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.
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