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/evist0/okf-matt-skills/debugnpx skills add evist0/okf-matt-skills --skill debuggit clone --depth 1 https://github.com/evist0/okf-matt-skillsWrote 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/evist0/okf-matt-skills/debug)<a href="https://agentmods.dev/skills/evist0/okf-matt-skills/debug"><img src="https://agentmods.dev/badge/skills/evist0/okf-matt-skills/debug.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.02058 |
| Opus 5 | $0.00018 | $0.01029 |
| Sonnet 5 | $0.00007 | $0.00412 |
| Haiku 4.5 | $0.00004 | $0.00206 |
Grade A, and why
debug scanned grade A 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Phase 1 is done when the loop is **tight** and **red-capable**: you can name **one command** — a script path, a test invocation, a curl — that you have **already run at least once** (paste the invocation and its output), This is a copy
89% identical to diagnosing-bugs — 72 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, read the project's OKF knowledge bundle if it exists (knowledge/glossary/ for a clear mental model of the relevant modules, knowledge/adr/ for decisions), and check ADRs in the area you're touching.
Phase 1 — Build a feedback loop
This is the skill. Everything else is mechanical. If you have a tight pass/fail signal for the bug — one that goes red on this bug — you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.
Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.
Ways to construct one — try them in roughly this order
- Failing test at whatever seam reaches the bug — unit, integration, e2e.
- Curl / HTTP script against a running dev server.
- CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
- Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
- Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
- Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
- Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
- Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can
git bisect runit. - Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
- HITL bash script. Last resort. If a human must click, drive them with
scripts/hitl-loop.template.shso the loop is still structured. Captured output feeds back to you.
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.
- 6d ago First seen · 137 lines · 37 tokens per session scan A 5f4c59b1eda5
debug is a skill published in the GitHub repository evist0/okf-matt-skills (13 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 2,058 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 89% identical to diagnosing-bugs, differing in 72 lines, and is treated as a copy.
Other skills, from other repositories
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-analyze-codebase
Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.
codegraph-ast-grep
Set up, update, or diagnose CodeGraph and ast-grep so coding agents can use semantic repository scope and structural syntax evidence automatically. Use when a repository needs an idempotent CodeGraph/ast-grep installation, stable tool and index migrations, MCP reconnection, persisted agent guidance, or a read-only…
debugging-diagnosis
Diagnose and fix bugs through reproduction, minimization, hypotheses, instrumentation, targeted fixes, and regression coverage. Use when the user reports failing behavior, a broken test, runtime error, flaky workflow, or asks for debugging before implementation.
ci-debugger
Diagnose failed CI jobs and build pipelines using small-log, evidence-first debugging. Use when the user shares a failed GitHub Actions, GitLab CI, Vercel, or package-manager build and wants root cause, minimal fix, and regression test guidance.
refresh-okf-mcp-token
Fetch or refresh an OAuth2 access token for the OKF Data wiki consumption MCP server. Use when the OKF MCP server returns 401/403/unauthorized/expired-token errors, when the user asks to refresh, renew, or get a new OKF MCP token, or when setting up authentication to call the okf-mcp / okfconsumption MCP runtime.