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 fakoli/fakoli-plugins --skill debugginggit clone --depth 1 https://github.com/fakoli/fakoli-pluginsWrote 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/fakoli/fakoli-plugins/debugging)<a href="https://agentmods.dev/skills/fakoli/fakoli-plugins/debugging"><img src="https://agentmods.dev/badge/skills/fakoli/fakoli-plugins/debugging/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/fakoli/fakoli-plugins/debugging"><img src="https://agentmods.dev/badge/skills/fakoli/fakoli-plugins/debugging.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00102 | $0.00758 |
| Opus 5.5 | $0.00041 | $0.00303 |
| Sonnet 5.5 | $0.00020 | $0.00152 |
| Haiku 4.5 | $0.00010 | $0.00076 |
Grade A, and why
debugging 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 1mo 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
In Codex, first read the native runtime adapter.
Systematic Debugging
A structured approach to debugging that prevents "try random fixes until something works."
When to Use
Activate this skill when any of the following hold:
- A test is failing without an obvious cause.
- A runtime error appears in production.
- A bug report describes unexpected behavior whose source is unclear.
- Two or more fix attempts have already failed.
The 4 Phases
Phase 1: Root Cause Investigation
- Read the error message completely — every line
- Reproduce the failure consistently
- Check recent changes:
git diff HEAD~5andgit log --oneline -10 - Trace data flow backward from the symptom to the source
- Gather evidence: which inputs trigger the bug? Which don't?
Phase 2: Pattern Analysis
- Find a working example of similar code in the codebase
- Compare the broken path against the working path — line by line
- Identify every difference, not just the obvious one
- Check: does the working example handle an edge case the broken code doesn't?
Phase 3: Hypothesis & Test
- Form a single, specific hypothesis: "X fails because Y does Z when W"
- Design a minimal test that confirms or disproves the hypothesis
- Run the test
- If disproved → return to Phase 1 with new information
- If confirmed → proceed to Phase 4
Phase 4: Implementation
- Write a failing test that reproduces the exact bug
- Implement the single, minimal fix
- Run the failing test → should now pass
- Run ALL tests → should still pass
- If new failures appear → the fix is wrong, return to Phase 3
Red Flags (Violations)
Stop immediately if you catch yourself:
- "Let me just try changing X" (no hypothesis)
- Making multiple changes at once (untestable)
- "This fix should work" without running the test
- Fixing a different bug than the one reported
- Deleting or modifying a test to make it pass
Escalation
If 3+ fix attempts have failed:
- Stop fixing
- Question the architecture — the bug may be a design problem
- Write up what you've learned and escalate to the orchestrator
- Include: what you tried, what happened, what you now suspect
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.
- 1mo ago Changed · +3 lines bcdac533c08f
- 1mo ago First seen · 73 lines · 102 tokens per session scan A 0c0d298075db
debugging is a skill published in the GitHub repository fakoli/fakoli-plugins (4 stars, last pushed 24d ago), licensed MIT. It adds 102 tokens to every session and 758 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
diag
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question
Answer a question about how things are right now — this codebase, its history, its behavior, a library, an error. Investigates and answers with evidence. Changes nothing, ever.
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A diagnostic framework that examines a problem across technology, processes, people, and business causes.
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When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
monitoring-observability
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards. Use when instrumenting services, designing logs/traces/metrics, defining SLOs and alerts, or debugging production behavior from telemetry.