What is autoresearch_learn?

A codebase documentation generator and checker. It can create or refresh Markdown, JSON, or reStructuredText documents, validate them, and build a navigable wiki, meaning linked pages that explain different parts of a project.

Use it to document selected files or modules, write overviews, check documentation, and create a project knowledge base. It supports starting documentation from scratch, updating it, or summarising it.

It reduces the manual work of explaining unfamiliar code and helps keep existing documentation current. Validation and optional fixes can find documentation problems after generation.

autoresearch_learn — Command for Claude Code and OpenCode by WdBlink

Install

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.

Clone the repo

git clone --depth 1 https://github.com/WdBlink/autoresearch-paper

Made for: Claude Code, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/wdblink/autoresearch-paper/autoresearch_learn/github.svg)](https://agentmods.dev/commands/wdblink/autoresearch-paper/autoresearch_learn)

Your own site

<a href="https://agentmods.dev/commands/wdblink/autoresearch-paper/autoresearch_learn"><img src="https://agentmods.dev/badge/commands/wdblink/autoresearch-paper/autoresearch_learn/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/wdblink/autoresearch-paper/autoresearch_learn"><img src="https://agentmods.dev/badge/commands/wdblink/autoresearch-paper/autoresearch_learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 2,113 The whole file, excluding the scripts and references it only reads on demand.

Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin 97% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.00027	$0.02113
Opus 5.5	$0.00011	$0.00845
Sonnet 5.5	$0.00005	$0.00423
Haiku 4.5	$0.00003	$0.00211

Measured 3d ago against content hash 320c5d519783, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade C, and why

autoresearch_learn scanned grade C with 1 finding 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 3d 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.

Harvests environment variableshighData exfiltration

Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.

- **Secrets (2-layer):** (1) prompt instructs "summarize config, never include verbatim values from .env/credentials or strings matching key/secret/token/password; extract env var *names* not *values*"; (2) post-gen, `gr

Origin

This is a copy

97% identical to autoresearch:learn — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.opencode/commands/autoresearch_learn.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

EXECUTE IMMEDIATELY.

Parse Arguments

Extract from $ARGUMENTS:

Mode: or --mode — init (create from scratch), update (refresh existing), check (validate), summarize (brief overview), wiki (navigable knowledge base)
Scope: or --scope — file globs to document
Depth: or --depth — overview, standard, comprehensive
--file <path> — specific file to document
--scan — force fresh codebase scout
--topics — comma-separated focus topics
--modules <list> — wiki mode: comma-separated module names/paths overriding auto-detection
--force — wiki mode: regenerate all pages from scratch, ignore existing manifest
--no-fix — validate only, don't auto-fix issues
--format — markdown (default), json, rst
Iterations: or --iterations — default 10. "unlimited" for unbounded.
--evals, --evals-interval N, --chain, --<subcommand>

Setup (if Mode or Scope missing)

question (single batch): Q1 (Mode): "What to do?" — init (generate docs), update (refresh), check (validate), summarize (overview), wiki (knowledge base) Q2 (Scope): "Which files?" — suggested globs + entire codebase Q3 (Depth): "How detailed?" — overview only, standard, comprehensive Q4 (Topics): "Focus on?" — architecture, API, database, testing, all If all provided → skip.

Establish Baseline

Scout codebase: file tree, imports/exports, existing docs
Identify documentation gaps (undocumented files, outdated docs, missing READMEs)
Create output directory: autoresearch/learn-{YYMMDD}-{HHMM}/
TSV header: # metric_direction: higher_is_better\niteration\ttimestamp\tfile_documented\tvalidation_status\tissues_found\tissues_fixed\tdescription
Metric = files with valid documentation (higher is better)

Summarize Mode (no loop)

If mode == summarize:

One-shot: scan codebase → produce structured summary
Write summary.md to output directory
Skip iteration loop entirely

Wiki Mode (no per-file loop)

If mode == wiki: reuse Scout (Phase 1) + Analyze output, then generate a navigable wiki/ knowledge base. Skip the init/update/check loop. Metric = pages_generated / pages_planned × 100 (from manifest); size target 300 lines/page.

Read the full file on GitHub · 138 lines

Changes

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.

3d ago First seen · 138 lines · 27 tokens per session scan C 320c5d519783

Subscribe to this mod's changes

autoresearch_learn is a command published in the GitHub repository WdBlink/autoresearch-paper (2 stars, last pushed 4d ago), licensed MIT. It adds 27 tokens to every session and 2,113 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (harvests environment variables). It is 97% identical to autoresearch:learn, differing in 4 lines, and is treated as a copy.

Documents Writing and docs academic-writing agent-skills ai ai-agents autonomous-research

autoresearch_learn next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
autoresearch_learnWdBlink/autoresearch-paper	Command	2	4d ago	MIT	C	27	Claude Code, OpenCode
ars-citation-checkImbad0202/academic-research-skills	Command	56k	yesterday	none	A	17	Claude Code
ars-format-convertImbad0202/academic-research-skills	Command	56k	yesterday	none	A	27	Claude Code
ars-format-convertImbad0202/academic-research-skills-codex	Command	14k	5d ago	none	A	27	Claude Code
export-latexTobiasBlask/open-paper-machine	Command	20	7mo ago	MIT	A	55	Claude Code
address-commentsStill-chowline394/MagicTeX-mcp	Command	0	1mo ago	AGPL-3.0	A	9	Claude Code
render_previewStill-chowline394/MagicTeX-mcp	Command	0	1mo ago	AGPL-3.0	A	12	Claude Code

autoresearch_learn