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 kevinnft/ai-agent-skills --skill debugging-and-error-recoverygit clone --depth 1 https://github.com/kevinnft/ai-agent-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/kevinnft/ai-agent-skills/debugging-and-error-recovery)<a href="https://agentmods.dev/skills/kevinnft/ai-agent-skills/debugging-and-error-recovery"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/debugging-and-error-recovery/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/kevinnft/ai-agent-skills/debugging-and-error-recovery"><img src="https://agentmods.dev/badge/skills/kevinnft/ai-agent-skills/debugging-and-error-recovery.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 | $0.00053 | $0.02430 |
| Opus 5 | $0.00026 | $0.01215 |
| Sonnet 5 | $0.00011 | $0.00486 |
| Haiku 4.5 | $0.00005 | $0.00243 |
Grade A, and why
debugging-and-error-recovery 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 11d 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.
This is a copy
94% identical to debugging-and-error-recovery — 15 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging and Error Recovery
Overview
Systematic debugging with structured triage. When something breaks, stop adding features, preserve evidence, and follow a structured process to find and fix the root cause. Guessing wastes time. The triage checklist works for test failures, build errors, runtime bugs, and production incidents.
When to Use
- Tests fail after a code change
- The build breaks
- Runtime behavior doesn't match expectations
- A bug report arrives
- An error appears in logs or console
- Something worked before and stopped working
The Stop-the-Line Rule
When anything unexpected happens:
1. STOP adding features or making changes
2. PRESERVE evidence (error output, logs, repro steps)
3. DIAGNOSE using the triage checklist
4. FIX the root cause
5. GUARD against recurrence
6. RESUME only after verification passes
Don't push past a failing test or broken build to work on the next feature. Errors compound. A bug in Step 3 that goes unfixed makes Steps 4-10 wrong.
The Triage Checklist
Work through these steps in order. Do not skip steps.
Step 1: Reproduce
Make the failure happen reliably. If you can't reproduce it, you can't fix it with confidence.
Can you reproduce the failure?
├── YES → Proceed to Step 2
└── NO
├── Gather more context (logs, environment details)
├── Try reproducing in a minimal environment
└── If truly non-reproducible, document conditions and monitor
When a bug is non-reproducible:
Cannot reproduce on demand:
├── Timing-dependent?
│ ├── Add timestamps to logs around the suspected area
│ ├── Try with artificial delays (setTimeout, sleep) to widen race windows
│ └── Run under load or concurrency to increase collision probability
├── Environment-dependent?
│ ├── Compare Node/browser versions, OS, environment variables
│ ├── Check for differences in data (empty vs populated database)
│ └── Try reproducing in CI where the environment is clean
├── State-dependent?
│ ├── Check for leaked state between tests or requests
│ ├── Look for global variables, singletons, or shared caches
│ └── Run the failing scenario in isolation vs after other operations
└── Truly random?
├── Add defensive logging at the suspected location
├── Set up an alert for the specific error signature
└── Document the conditions observed and revisit when it recurs
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.
- 11d ago First seen · 306 lines · 53 tokens per session scan A bf05e65528db
debugging-and-error-recovery is a skill published in the GitHub repository kevinnft/ai-agent-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 2,430 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to debugging-and-error-recovery, differing in 15 lines, and is treated as a copy.
Other skills, from other repositories
kubectl-investigator
Investigate a live or recent incident in a Kubernetes cluster. Anchor the window, bisect the change surface (rollouts, ConfigMaps/Secrets, RBAC, HPA/cluster changes, CronJobs), classify against four reference failure paths (OOM, DNS, cascading-failure, deploy-correlator), confirm the hypothesis with three independent…
frontend-bugfix-debugger
Diagnose and fix frontend defects from evidence, including unclear UI/runtime errors, broken routes, styling regressions, and hydration or client issues. Reproduce before editing; exclude planned refactors and backend-only debugging.
gdpr-dsgvo-expert-neekware
GDPR and German DSGVO compliance automation. Scans codebases for privacy risks, generates DPIA documentation, tracks data subject rights requests. Use for GDPR compliance assessments, privacy audits, data protection planning, DPIA generation, and data subject rights management.
plumb-line-remediate
Use when applying findings from a plumb-line audit report — the builder has a report (or pasted findings) and wants the fixes made. Opt-in and separate from the audit, which is read-only and never fixes.
stage-review
Reviews a completed pipeline stage before advancing to the next one. Verifies all declared outputs exist, runs the stage's Review Checkpoint criteria, checks quality against the Process intent, and confirms downstream readiness. Use when: reviewing stage output, checking if ready to advance, verifying stage completion.
validate-pipeline
Validates an ICM pipeline's contract chain — checks that each stage's outputs match the next stage's expected inputs, verifies factory/product separation, and flags structural anti-patterns. Use when: checking pipeline integrity, verifying handoffs, debugging broken stage connections.