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 d-o-hub/github-template-ai-agents --skill debuggergit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote 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/d-o-hub/github-template-ai-agents/debugger)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/debugger"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/debugger/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/d-o-hub/github-template-ai-agents/debugger"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/debugger.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 | $0.00088 | $0.00609 |
| Opus 5.5 | $0.00035 | $0.00244 |
| Sonnet 5.5 | $0.00018 | $0.00122 |
| Haiku 4.5 | $0.00009 | $0.00061 |
Grade A, and why
debugger 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 25d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
Root-cause diagnosis for failing builds and runtime errors. Symptoms first, theories second, fixes last.
When to Use
- Build, deploy, or CI pipeline fails
- Runtime crash, panic, exception, or hang
- Error output that is cryptic or misleading
- Even if they just say "debug this crash" or "the build is red"
Steps
- Read the error first — full output, not the last line. Note exit codes, file:line locations, and the first failure (later ones cascade).
- Reproduce minimally — smallest command or input that triggers it. If it only fails in CI, diff the environments (versions, env vars, cwd).
- Form one hypothesis — state the suspected cause before changing code.
- Bisect — halve the search space (recent commits, config halves, feature flags) until the trigger commit or line is isolated.
- Fix at the root — patch the cause, not the symptom; add the minimal reproducer as a regression test.
- Verify — rerun the reproducer plus the surrounding suite green.
Common Traps
- Fixing the second error in the log while the first (causal) one remains.
- "Works on my machine" without diffing dependency or environment versions.
- Treating flaky as fixed after one green run — rerun at least 3 times.
Rationalizations
| Rationalization | Reality |
|---|---|
| "The error message says what's wrong, just fix that line" | Error text reports where it surfaced, not where it started; trace to the cause. |
| "I'll just retry, it was probably flaky" | Uninvestigated flakes become incidents; reproduce or explicitly classify with evidence. |
Red Flags
- Changing code before stating a hypothesis
- Closing as flaky without rerun evidence or a tracking issue
- Fix without a regression test
See Also
test-runner— Execute tests and diagnose failures inside a test suiteiterative-refinement— Repeat-until-pass validation loops once the cause is knownsecurity-code-auditor— Vulnerability analysis, not failure diagnosis
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.
- 25d ago First seen · 62 lines · 88 tokens per session scan A af1b4ddc07e2
debugger is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 609 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 2026-09-28.
Other skills, from other repositories
slopmop
Trigger when you would normally reach for pytest, gh, mypy, black, or other raw repo tooling. Redirect to sm swab, sm scour, sm buff, sm sail, sm refit, or sm doctor so remediation follows the established rails. Also trigger when asked to "install" or "set up" slop-mop in a repo, or when you find a repo with sm…
performance-patterns
Use when user asks about N+1 queries, performance optimization, query optimization, reduce API calls, improve render performance, fix slow code, optimize database, or reduce bundle size. Provides guidance on identifying and fixing performance anti-patterns across database, backend, frontend, and API layers.
codebase-refactoring
Three-part workflow to improve overall codebase health with jscpd — find and fix duplicated code, find and remove or refactor dead code, then find and simplify the largest/most complex files. Starts from --dashboard/--health to prioritize and ends by re-measuring the score.
report-issue-local
File a bug or feature request against this MCP server's own repo. Use for server-specific issues — tool logic, service integrations, config problems, or domain bugs that aren't caused by the framework.
dorodango
Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.
ring:searching-code
Forensic code search and analysis with optional Chain of Draft (CoD) ultra-concise mode. Five-phase methodology (clarification, planning, execution, analysis, synthesis) with severity assessment. Use for targeted investigation of specific patterns, bugs, or vulnerabilities. Skip for broad architecture mapping (use…