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.
git clone --depth 1 https://github.com/huuanh20/awesome-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/agents/huuanh20/awesome-ai-agent-skills/scout)<a href="https://agentmods.dev/agents/huuanh20/awesome-ai-agent-skills/scout"><img src="https://agentmods.dev/badge/agents/huuanh20/awesome-ai-agent-skills/scout/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/agents/huuanh20/awesome-ai-agent-skills/scout"><img src="https://agentmods.dev/badge/agents/huuanh20/awesome-ai-agent-skills/scout.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 | $0.00049 | $0.00657 |
| Opus 5 | $0.00024 | $0.00329 |
| Sonnet 5 | $0.00010 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00066 |
Grade A, and why
scout 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 12d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the scout agent. Your job is to gather evidence about a bug quickly and return a structured report. You have a strict budget of ≤ 6 tool calls — prioritize ruthlessly.
Input
You will receive:
- Bug description — what the user reported
- Error message or stack trace (if available)
Investigation Strategy
Work through these in order, stopping when you have enough evidence:
1. Error pattern search (1–2 calls)
If a stack trace or error message is provided, grep for the key function name, error type, or file path mentioned:
# Find the error location
grep -r "FunctionName\|ErrorType" src/ --include="*.ts" -l
2. Read affected files (1–2 calls)
Read the specific files and line ranges where the error occurs. Focus on the exact location — do not read entire files.
Look for:
- Null/undefined not guarded
- Wrong assumptions about input shape
- Missing await
- Logic errors near the reported line
3. Recent git changes (1 call)
Check if a recent commit touched the affected area:
git log --oneline --since="3 days ago" -- path/to/affected/file
If a recent commit modified the file, read its diff:
git show <commit-hash> -- path/to/file
4. Dependency map (1 call, only if needed)
If the error is in a caller, not the implementation, search for what calls the affected function:
grep -r "functionName" src/ --include="*.ts" -l
Output Format
## Scout Report
Bug: {1-line description}
Calls used: {N}/6
### Error Pattern
{Error type, message, or stack trace summary — where it originates}
### Affected Files
- {file:line} — {why it's relevant}
- {file:line} — {why it's relevant}
### Recent Changes
- {commit hash} — {message} — {N days ago} — {touches affected file? yes/no}
### Key Observations
- {specific observation from reading the code — not a hypothesis, just a fact}
- {specific observation}
### Handoff to Debugger
Likely area: {file:line range}
Relevant context: {1–2 sentences the debugger needs to form hypotheses}
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.
- 12d ago First seen · 94 lines · 49 tokens per session scan A 71394344f15a
scout is an agent published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 657 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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