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 TechNickAI/ai-coding-config --skill systematic-debugginggit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/systematic-debugging)<a href="https://agentmods.dev/skills/technickai/ai-coding-config/systematic-debugging"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/systematic-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/technickai/ai-coding-config/systematic-debugging"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/systematic-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 | $0.00024 | $0.00802 |
| Opus 5.5 | $0.00010 | $0.00321 |
| Sonnet 5.5 | $0.00005 | $0.00160 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
systematic-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 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.
What it actually says
Core principle: If you can't explain WHY it's broken, you're not ready to fix it. Every fix must address a specific, understood root cause.
Work to reproduce the issue reliably. If you can't trigger it consistently, gather more data before proposing solutions. Document the exact steps that trigger the failure.
Check what changed recently. Review commits, new dependencies, configuration changes, environmental differences. Most bugs correlate with recent changes.
When multiple components interact, add diagnostic output at each boundary to identify which component fails. This narrows the investigation to the specific failing layer.
When implementing a pattern, read reference implementations thoroughly. Understand their dependencies, settings, and environmental requirements.
When a hypothesis proves wrong, form a new one based on what you learned. Don't layer fixes on top of failed attempts.
Apply a single, focused fix that addresses the root cause. Resist bundling other improvements or refactoring.
Verify the fix resolves the issue without breaking other functionality.
These patterns suggest reconsidering the fundamental approach rather than continuing to patch symptoms.
These thoughts signal you're guessing rather than debugging systematically.
Systematic debugging finds and fixes the real problem. Random attempts waste time and create new issues.
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 · 91 lines · 24 tokens per session scan A 5f2194f9e57d
systematic-debugging is a skill published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 26d ago), licensed MIT. It adds 24 tokens to every session and 802 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 2026-09-22.
Other skills, from other repositories
explore
Build an accurate mental model of an unfamiliar codebase, feature, or area before changing it — where it lives, how it connects, what it does, and why. Use when asked how something works, where something is, when onboarding to a repo, or before editing code you don't know. For a cross-cutting question that needs…
debug
Trace a bug, error, or regression to its root cause. Use when investigating a failure, a stack trace, unexpected behavior, or "why does X happen".
github-webhooks
Receive and verify GitHub webhooks. Use when setting up GitHub webhook handlers, debugging signature verification, or handling repository events like push, pullrequest, issues, or release.
cursor-webhooks
Receive and verify Cursor Cloud Agent webhooks. Use when setting up Cursor webhook handlers, debugging signature verification, or handling Cloud Agent status change events (ERROR, FINISHED).
debug-assistant
Systematic debugging assistant that helps identify and fix bugs through structured analysis, hypothesis testing, and root cause identification. Use when facing errors, unexpected behavior, or mysterious bugs.
quick-fix
Apply a minimal, targeted fix to a specific bug or issue. Focused on speed and precision - locate the problem, make the smallest correct change, and verify. Not a planning workflow; use for small fixes, patches, and hotfixes.