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/ariaxhan/kernel-claude/debugnpx skills add ariaxhan/kernel-claude --skill debuggit clone --depth 1 https://github.com/ariaxhan/kernel-claudeWhat 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 | $0.00052 | $0.01096 |
| Opus 5 | $0.00026 | $0.00548 |
| Sonnet 5 | $0.00010 | $0.00219 |
| Haiku 4.5 | $0.00005 | $0.00110 |
Grade A, and why
debug 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run agentdb recall with the exact error text, subsystem/library, failing
test, and known files/symbols. Recall again when the hypothesis changes or a new failure
appears; that is a new retrieval question. Reference on demand:
skills/debug/reference/debug-research.md.
-
HYPOTHESIZE: list 3 causes before pursuing any.
- Read ALL error output first (anchoring bias mitigation).
- Write each hypothesis to AgentDB. Prevents circular re-investigation.
- (gate: 3 candidate hypotheses written; none pursued yet)
-
ISOLATE: binary search, O(log n) not O(n).
- Code: call chain A→B→C→D→E fails → check midpoint C → recurse into failing half.
- Time:
git bisectbetween known-good and known-bad commit. ~10 tests for 1000 commits. - Input: large failing input → split in half → recurse to minimal reproduction case.
- Instrument at boundaries: log inputs/outputs at each layer boundary.
- Mock external dependencies to isolate which one causes failure.
- (gate: failure localized to a specific function/commit/input subset)
-
ROOT CAUSE: the error line is the FAILURE. The DEFECT is upstream.
- Ask: what assumption was violated? What invariant broke?
- If you can't explain WHY it broke, you haven't found root cause.
- Top causes by frequency: wrong input shape/type · off-by-one · missing null check · race condition · shared-state mutation · wrong comparison operator · variable scope · swallowed error · API contract mismatch · environment difference.
- (gate: can state root cause in one sentence explaining the violated invariant)
-
FIX: root cause, not symptom.
- Fix the DEFECT, not the FAILURE site. (Null check at crash site = symptom fix.)
- Write regression test that fails before fix, passes after.
- Run: original failing case + edge cases + full regression suite.
- Commit fix + test together.
- (gate: regression test green; original failing case passes)
<anti_patterns> Shotgun (random changes until it works) · fix-and-pray (never re-run the original case) · symptom fixing (null check at the crash site) · printf flooding (binary search first, then targeted logging) · blame-the-framework (it's almost never the library) · unscoped "investigate" (scope narrowly or use a subagent so the file reads don't fill context). </anti_patterns>
<when_stuck> Explain the problem in writing · re-read the error message (the answer is there most of the time) · reduce to a minimal reproduction · ask "what changed?" (git log/diff, deps, env) · search the exact error message in quotes · step away, bias accumulates. Re-run the EXACT original failing case before declaring victory; "seems to work" is not evidence. </when_stuck>
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.
- yesterday First seen · 88 lines · 52 tokens per session scan A 18939e83b03c
debug is a skill published in the GitHub repository ariaxhan/kernel-claude (12 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,096 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.
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