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 millionco/debug-agent --skill skillgit clone --depth 1 https://github.com/millionco/debug-agentWrote 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/millionco/debug-agent/skill)<a href="https://agentmods.dev/skills/millionco/debug-agent/skill"><img src="https://agentmods.dev/badge/skills/millionco/debug-agent/skill/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/millionco/debug-agent/skill"><img src="https://agentmods.dev/badge/skills/millionco/debug-agent/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 223 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 232 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium MCP Rug Pull · line 55 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 84 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00060 | $0.03018 |
| Opus 5.5 | $0.00024 | $0.01207 |
| Sonnet 5.5 | $0.00012 | $0.00604 |
| Haiku 4.5 | $0.00006 | $0.00302 |
Grade A, and why
debug-agent 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **If reproduction is straightforward** (e.g., a single CLI command, a curl request, a simple script): write and run an ad hoc reproduction script yourself. Tailor it to the runtime — Playwright/Puppeteer for browser bu How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Mode
You are now in DEBUG MODE. You must debug with runtime evidence.
Why this approach: Traditional AI agents jump to fixes claiming 100% confidence, but fail due to lacking runtime information. They guess based on code alone. You cannot and must NOT fix bugs this way — you need actual runtime data.
Your systematic workflow:
- Generate 3-5 precise hypotheses about WHY the bug occurs (be detailed, aim for MORE not fewer)
- Instrument code with logs (see Logging section) to test all hypotheses in parallel
- Reproduce the bug.
- If a failing test already exists: run it directly.
- If reproduction is straightforward (e.g., a single CLI command, a curl request, a simple script): write and run an ad hoc reproduction script yourself. Tailor it to the runtime — Playwright/Puppeteer for browser bugs, a Node/Python/shell script for backend bugs, etc.
- Otherwise: ask the user to reproduce it. Provide clear, numbered steps. Remind them to restart apps/services if instrumented files are cached or bundled. Offer: "If you'd like me to write a reproduction script instead, let me know."
- Once the user confirms a reproduction pathway (manual or automated), reuse it for all subsequent iterations without re-asking.
- Analyze logs: evaluate each hypothesis (CONFIRMED/REJECTED/INCONCLUSIVE) with cited log line evidence
- Fix only with 100% confidence and log proof; do NOT remove instrumentation yet
- Verify with logs: ask user to run again, compare before/after logs with cited entries
- If logs prove success and user confirms: remove all instrumentation by searching for
#region debug log/#endregionmarkers and deleting those blocks (see Cleanup section). If failed: FIRST remove any code changes from rejected hypotheses (keep only instrumentation and proven fixes), THEN generate NEW hypotheses from different subsystems and add more instrumentation - After confirmed success: explain the problem and provide a concise summary of the fix (1-2 lines)
Critical constraints:
- NEVER fix without runtime evidence first
- ALWAYS rely on runtime information + code (never code alone)
- Do NOT remove instrumentation before post-fix verification logs prove success and user confirms that there are no more issues
- Fixes often fail; iteration is expected and preferred. Taking longer with more data yields better, more precise fixes
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 First seen · 233 lines · 60 tokens per session scan A 86cdbb0fea28
debug-agent is a skill published in the GitHub repository millionco/debug-agent (351 stars, last pushed 4mo ago), licensed MIT. It adds 60 tokens to every session and 3,018 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
rubber-duck
Adversarial "rubber duck" review that turns explaining-out-loud into a hallucination check. The main session is the PRESENTER (it did the work — a design doc, investigation, or analysis — and holds the real reasoning) and reconstructs the topic to a LISTENER — a spawned subagent pinned to a DIFFERENT-vendor model that…
mindos
MindOS: local knowledge assistant & shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, preferences across sessions/agents. Core: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, distill lessons. NOT for app source or paths outside KB. Triggers…
superlint
This skill describes the mandatory standard operating procedure for using our internal SuperLint tool. Use this when tasks require fixing code quality issues according to corporate standards.
lastlight-debug
Debug a running Last Light instance over its admin API with the lastlight CLI — no SSH. Use when the user wants to "look at / debug a failed or stuck workflow", "why didn't the bot do X", "check a bad agent run / phase error on ", "tail a session", "search the logs", "read the server/docker logs", "list/trigger a…
a0-debug-plugin
Diagnose and fix Agent Zero plugin problems. Covers plugin not appearing, won't enable, API endpoints not responding, frontend store errors, extension point injection, settings resolution, hooks.py issues, and log inspection. Use when a plugin is not working, not loading, crashing, missing from the list, or behaving…
mindos-max-zh
A Chinese-language skill for MindOS, a shared local knowledge base used by connected agents to store decisions, meeting notes, procedures, debugging lessons, and other context.