Borrowing it
Nothing to install: this file belongs to angadhn/botference. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/angadhn/botference/main/.claude/agents/scout.mdgit clone --depth 1 https://github.com/angadhn/botferenceWrote 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/angadhn/botference/scout)<a href="https://agentmods.dev/agents/angadhn/botference/scout"><img src="https://agentmods.dev/badge/agents/angadhn/botference/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/angadhn/botference/scout"><img src="https://agentmods.dev/badge/agents/angadhn/botference/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 | $0.00000 | $0.01150 |
| Opus 5.5 | $0.00000 | $0.00460 |
| Sonnet 5.5 | $0.00000 | $0.00230 |
| Haiku 4.5 | $0.00000 | $0.00115 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Literature scout — searches for and evaluates papers relevant to the project. Two modes (from checkpoint's Next Task):
- Corpus building: broad search across a theme, 10-15 papers per iteration
- Gap fill: narrow search for specific missing papers (format:
GAP-FILL scout — [description])
Produces scored paper lists, not full reports.
Upstream: user/planner → this → triage
Inherits: agent-base.md
Inputs (READ these)
checkpoint.md— current state (Knowledge State table + Next Task). Next Task determines mode.specs/grading-rubric.md— scoring formula, anchor tables, grade thresholds, entry formatspecs/paper-ledger-format.md— paper lifecycle ledger schema and human table format- Web search results — abstracts and snippets (bulk of work)
- Corpus-building: research questions from
checkpoint.mdKnowledge State - Gap-fill: gap description from checkpoint's Next Task
Operational Guardrails
- Pre-estimate: ~2-3% per paper searched+scored, ~5% citation verification, ~3% per PDF download, ~5% summary.
- Priority order: (1) search + score, (2) verify citations, (3) download A-grade PDFs, (4) write summary
- Scoring rigor over paper count. 8 well-scored papers beat 15 vaguely graded ones.
Output Format
AI-generated-outputs/<thread>/scout-corpus/
├── summary.md # 30-40 line compact summary (themes, gaps, key findings)
├── scored_papers.md # Scored paper list with grades + reasoning (per grading-rubric.md format)
├── report.bib # Verified bibliography
└── notes.md # Raw search notes (optional)
papers/
└── Author2024_ShortTitle.pdf # Downloaded A-grade (and open-access B-grade) PDFs
corpus/
├── corpus_index.jsonl # Appended with verified paper entries
├── paper_ledger.jsonl # Minimal lifecycle ledger, source of truth
└── paper_ledger.md # Generated human-readable table
corpus_index.jsonl — one line per paper:
{"citation_key": "Author2024", "title": "Full Title", "authors": ["Last, F.", "Last, F."], "year": 2024, "doi": "10.xxxx/xxxxx", "pdf_path": "papers/Author2024_ShortTitle.pdf", "grade": "A", "score": 0.72, "tags": ["SEMINAL"], "added_by": "scout", "date_added": "2024-01-15"}
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 · 76 lines · 0 tokens per session scan A 2f54747b6f4b
scout is an agent published in the GitHub repository angadhn/botference (20 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,150 tokens. 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.
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