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 skills add knowns-dev/knowns --skill kn-debuggit clone --depth 1 https://github.com/knowns-dev/knownsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/knowns-dev/knowns/kn-debug)<a href="https://agentmods.dev/skills/knowns-dev/knowns/kn-debug"><img src="https://agentmods.dev/badge/skills/knowns-dev/knowns/kn-debug/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/knowns-dev/knowns/kn-debug"><img src="https://agentmods.dev/badge/skills/knowns-dev/knowns/kn-debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00025 | $0.01943 |
| Opus 5.5 | $0.00010 | $0.00777 |
| Sonnet 5.5 | $0.00005 | $0.00389 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
kn-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 22d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging
Systematic debugging: triage → reproduce → diagnose → fix → learn.
Announce: "Using kn-debug for [error/issue]."
Core principle: CLASSIFY FIRST → REPRODUCE → ROOT CAUSE → FIX → CAPTURE LEARNING.
When to Use
- Build fails (compilation, type error, missing dependency)
- Test fails (assertion mismatch, timeout, flaky)
- Runtime crash or exception
- Integration failure (API mismatch, env config, auth)
- Task blocked with unclear cause
- User says "debug this", "fix this error", "why is this failing"
Inputs
- Error message, stack trace, or failing command
- Optional: task ID (if debugging within a task context)
Step 1: Triage — Classify the Issue
Classify before investigating. Misclassifying wastes time.
| Type | Signals |
|---|---|
| Build failure | Compilation error, type error, missing module, bundler failure |
| Test failure | Assertion mismatch, snapshot diff, timeout, flaky intermittent |
| Runtime error | Crash, uncaught exception, undefined behavior |
| Integration failure | HTTP 4xx/5xx, env variable missing, API schema mismatch |
| Blocked task | Circular dependency, conflicting changes, unclear requirement |
Output: One-line classification: [TYPE] in [component]: [symptom]
Step 2: Check Known Patterns
Before deep investigation, search for known solutions (unified search includes docs, learnings, and memories):
mcp_knowns_search({ "action": "search", "query": "<keywords from classification>", "type": "doc" })
Also check learnings docs:
mcp_knowns_search({ "action": "search", "query": "<error pattern>", "type": "doc", "tag": "learning" })
Search memories for past debug patterns:
mcp_knowns_search({ "action": "search", "query": "<error pattern>", "type": "memory" })
If a known pattern matches → jump to Step 4 (Fix) using the documented resolution.
Step 3: Reproduce & Diagnose
3a. Reproduce
Run the exact failing command verbatim:
# Whatever failed — run it exactly
<failing-command> 2>&1
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.
- 22d ago Changed e23316aed7ac
- 1mo ago First seen · 265 lines · 25 tokens per session scan A df2418f4bee1
kn-debug is a skill published in the GitHub repository knowns-dev/knowns (249 stars, last pushed 22d ago), licensed MIT. It adds 25 tokens to every session and 1,943 once invoked, about $0.0001 per session on Opus 5.5. 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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