autoresearch

autoresearch is a skill for Claude Code from eliransu/digital-brain. It costs 124 tokens per session (1,810 once invoked), scanned A, a copy of autoresearch, MIT.

An automated research workflow that repeatedly searches a topic, gathers sources, combines the findings, and saves structured pages in a wiki.

In plain words
What is it for?
Use it for in-depth topic research when you want sourced findings filed into a knowledge base.
Why use it?
It turns an open-ended research request into a repeatable investigation with configurable objectives and source preferences.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is if [ -x ./scripts/boundary-score.py ] && [ -d ./.vault-meta ] && command -v python3 >/dev/null 2>&1; then.

Part of the digital-brain plugin — 18 skills, 5 commands, 2 agents, 3 hooks shipped together

Good fit Use it for in-depth topic research when you want sourced findings filed into a knowledge base.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/eliransu/digital-brain
agentmods
npx agentmods add skills/eliransu/digital-brain/autoresearch

Made for: Claude Code.

Or install digital-brain, the plugin that ships this one along with the rest of its 18 skills, 5 commands, 2 agents, 3 hooks.

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.

agentmods badge for autoresearch

README.md
[![agentmods](https://agentmods.dev/badge/skills/eliransu/digital-brain/autoresearch/github.svg)](https://agentmods.dev/skills/eliransu/digital-brain/autoresearch)
Your own site
<a href="https://agentmods.dev/skills/eliransu/digital-brain/autoresearch"><img src="https://agentmods.dev/badge/skills/eliransu/digital-brain/autoresearch/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.

agentmods 80×15 button for autoresearch

Your own site · 80×15
<a href="https://agentmods.dev/skills/eliransu/digital-brain/autoresearch"><img src="https://agentmods.dev/badge/skills/eliransu/digital-brain/autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,810 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.01810
Opus 5 $0.00062 $0.00905
Sonnet 5 $0.00025 $0.00362
Haiku 4.5 $0.00012 $0.00181

Measured 9d ago against content hash 46191b1748d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

autoresearch 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 9d 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.

Origin

This is a copy

100% identical to autoresearch — 21 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.

skills/autoresearch/SKILL.md · 229 lines

How it starts

The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.

autoresearch: Autonomous Research Loop

You are a research agent. You take a topic, run iterative web searches, synthesize findings, and file everything into the wiki. The user gets wiki pages, not a chat response.

This is based on Karpathy's autoresearch pattern: a configurable program defines your objectives. You run the loop until depth is reached. Output goes into the knowledge base.


Before Starting

Read references/program.md to load the research objectives and constraints. This file is user-configurable. It defines what sources to prefer, how to score confidence, and any domain-specific constraints.


Topic Selection

Three paths to a topic:

A. Explicit topic (always respected)

When the user says /autoresearch [topic] or "research X", use the given topic verbatim and skip the sections below.

B. Boundary-first selection (agenda control, opt-in)

This is agenda control, not pure memory. DragonScale Memory.md Mechanism 4 labels this mechanism as such because it shapes which direction the research agent moves next. Users who want a strict memory-layer subset should omit this path entirely.

When /autoresearch is invoked WITHOUT a topic AND the vault has adopted DragonScale, default to surfacing the frontier of the vault as a set of candidate topics the user can accept, override, or decline.

Feature detection (shell):

if [ -x ./scripts/boundary-score.py ] && [ -d ./.vault-meta ] && command -v python3 >/dev/null 2>&1; then
  BOUNDARY_MODE=1
else
  BOUNDARY_MODE=0
fi

When BOUNDARY_MODE=1:

  1. Run ./scripts/boundary-score.py --json --top 5. Returns the top 5 frontier pages by boundary_score = (out_degree - in_degree) * recency_weight.
  2. Helper failure handling: if the helper exits non-zero, emits invalid JSON, or returns an empty results array, set BOUNDARY_MODE=0 and fall through to section C below. Do NOT prompt the user with an empty candidate list, and do NOT improvise a topic.
  3. Present the candidate list to the user: "Your top frontier pages are: [list]. Research which one? (1-5, or type a topic to override, or say 'cancel' to be asked normally.)"
  4. If the user picks 1-5, use the selected page's title as the topic.
  5. If the user types free text, use that.
  6. If the user cancels or does not choose, fall through to C.

Read the full file on GitHub · 229 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

  1. 9d ago First seen · 229 lines · 124 tokens per session scan A 46191b1748d2

Subscribe to this mod's changes

autoresearch is a skill published in the GitHub repository eliransu/digital-brain (1 stars, last pushed 3mo ago), licensed MIT. It adds 124 tokens to every session and 1,810 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to autoresearch, differing in 21 lines, and is treated as a copy.

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