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 Weaverse/.agents --skill debuggit clone --depth 1 https://github.com/Weaverse/.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/weaverse/.agents/debug)<a href="https://agentmods.dev/skills/weaverse/.agents/debug"><img src="https://agentmods.dev/badge/skills/weaverse/.agents/debug/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/weaverse/.agents/debug"><img src="https://agentmods.dev/badge/skills/weaverse/.agents/debug.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 | $0.00048 | $0.01689 |
| Opus 5.5 | $0.00019 | $0.00676 |
| Sonnet 5.5 | $0.00010 | $0.00338 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
debug 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 241 lines · 48 tokens per session scan A 8b54eef57b48
debug is a skill published in the GitHub repository Weaverse/.agents (39 stars, last pushed 3mo ago), with no licence file. It adds 48 tokens to every session and 1,689 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
qa-check
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debug-wp
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debug-rules
Diagnose which rules loaded, which didn't, and why. Reads the InstructionsLoaded audit log and compares against expected rules.
debugging
Structured debugging protocol. Use when investigating a bug, failing test, regression, unexpected behavior, or any scenario where code is not doing what it should. Trigger phrases include "broken", "not working", "bug", "error", "it worked before", "still broken", "why is this failing", "trace", or "/trace".
tool-usage
Recovery protocol for tool failures and search budgets. Use when Edit fails repeatedly on the same target, when hooks may have modified file contents between operations, or when WebSearch/WebFetch is not converging on an answer.
anomaly-alert
Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error…