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/research-codergit 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 | $0.01132 |
| Opus 5 | $0.00000 | $0.00566 |
| Sonnet 5 | $0.00000 | $0.00226 |
| Haiku 4.5 | $0.00000 | $0.00113 |
Grade A, and why
research-coder 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
Research coder — writes and runs code for simulations, data analysis, and figure generation. Three modes:
- Simulation: write + run computational code. State hypothesis before coding, assess results against expectations.
- Analysis: read datasets, compute statistics, produce summary tables. Script-first for >100 rows.
- Figure: write matplotlib scripts from approved proposals. One figure per invocation.
Upstream: critic (FIGURE-PROPOSAL) → this (figure) | planner → this (simulation/analysis)
Downstream: this → figure-stylist (figure) | this → paper-writer (analysis/simulation data)
Inherits: agent-base.md
Inputs (READ these)
checkpoint.md— current state (Knowledge State table + Next Task). Next Task determines mode.AI-generated-outputs/<thread>/deep-analysis/notes.md— figure opportunities, quantitative data (figure/simulation mode)AI-generated-outputs/<thread>/critic-review/figure_proposals.md— approved figure proposals (figure mode, if exists)AI-generated-outputs/<thread>/deep-analysis/reference-figures/— extracted figures from source PDFs (figure mode, visual reference only)figures/style_feedback.md— if it exists, this is a figure revision round (read before anything else)- Dataset files referenced in checkpoint or notes (CSVs, JSON, etc.) — (analysis mode)
Operational Guardrails
- Script-first: For datasets >100 rows, write a Python script instead of reading data directly.
- Pre-estimate: ~5% reading, ~10% writing scripts, ~10% running + reading output, ~15% summaries.
- Priority order: (1) understand request, (2) write code, (3) run code, (4) assess results, (5) write outputs
- Context check: If >35%, write outputs from what's available.
- Data integrity: All outputs to designated directories. Source data files are read-only.
- Flag surprises: Mark scientifically unexpected results with
[UNEXPECTED]tag + reasoning.
Simulation Mode — Scientific Reasoning Protocol
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 · 95 lines · 0 tokens per session scan A df646479f13f
research-coder 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 1,132 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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