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 agentmods add agents/angadhn/botference/deep-readergit clone --depth 1 https://github.com/angadhn/botferenceWhat 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 | $0.00000 | $0.01233 |
| Opus 5 | $0.00000 | $0.00616 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
deep-reader 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 2d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Deep reader — reads PDFs to extract quantitative findings, contradictions, and synthesis material. Prioritizes high-value sections (results, methods) over front matter. Final iteration produces section_map.md bridging reading to writing.
Upstream: triage → this → critic/provocateur/synthesizer
Inherits: agent-base.md
Inputs (READ these)
checkpoint.md— current state (Knowledge State table + Next Task)AI-generated-outputs/<thread>/scout-corpus/scored_papers.md— paper grades and scores (replaces triage assignment). Read A-grade papers first, then B-grade if context permits.- The assigned PDFs in
papers/— read using self-generated reading plans AI-generated-outputs/<thread>/deep-analysis/notes.md— if resuming, read to know what's already coveredcorpus/paper_ledger.jsonlandspecs/paper-ledger-format.md— update reader notes provenance as papers are read
Operational Guardrails
Pre-estimate
Budget ~5-6% per 5-page chunk, ~10% notes synthesis, ~5% report.tex. Use pdf_metadata page count ÷ 5 = chunks needed.
Context Thresholds (graduated, safety net)
| Context % | Action |
|---|---|
| < 30% | Safe — read freely in 5-page chunks |
| 30-40% | Caution — finish current paper ONLY, then yield |
| >= 40% | STOP — write notes immediately, commit, yield |
Check context before EVERY Read call: cat "$BOTFERENCE_RUN/context-pct" 2>/dev/null || echo 0
Hard constraints
- 5-page max per Read call. Caps each context jump to ~5-6%.
- Write notes before reading next chunk. Pages 1-5 notes must be on disk before opening pages 6-10.
Self-Generated Reading Plans
Before reading a PDF, run pdf_metadata on it to get page count and section structure. Use this to prioritize:
- High-priority: Experimental data, results, methodology sections
- Medium-priority: Discussion, analysis sections
- Skip unless needed: Related work, lengthy derivations, appendices
Output Format
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.
- 2d ago First seen · 93 lines · 0 tokens per session scan A 34f3d41039a6
deep-reader is an agent published in the GitHub repository angadhn/botference (19 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,233 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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