auto-research

auto-research is a skill for Claude Code from yqi96/warranted. It costs 70 tokens per session (2,623 once invoked), scanned A, original, MIT.

A process for carrying out open-ended research from supplied data and reference material. It creates research questions and testable claims when the goal does not already specify them.

In plain words
What is it for?
Use it to frame a research project, define questions, gather observed and published evidence, test claims, address counterarguments, and compile the result.
Why use it?
It prevents conclusions from being accepted merely because an analysis appears plausible by requiring evidence, challenges, and verification.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the warranted plugin — 7 skills, 8 agents, 1 hook, 1 MCP server shipped together

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.

agentmods
npx agentmods add skills/yqi96/warranted/auto-research
Any agent
npx skills add yqi96/warranted --skill auto-research
Clone the repo
git clone --depth 1 https://github.com/yqi96/warranted

Made for: Claude Code.

Or install warranted, the plugin that ships this one along with the rest of its 7 skills, 8 agents, 1 hook, 1 MCP server.

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 auto-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/yqi96/warranted/auto-research.svg)](https://agentmods.dev/skills/yqi96/warranted/auto-research)
Your own site
<a href="https://agentmods.dev/skills/yqi96/warranted/auto-research"><img src="https://agentmods.dev/badge/skills/yqi96/warranted/auto-research.svg" alt="Measured on agentmods" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,623 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00070 $0.02623
Opus 5 $0.00035 $0.01311
Sonnet 5 $0.00014 $0.00525
Haiku 4.5 $0.00007 $0.00262

Measured 6d ago against content hash 41cb82b5a5a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

auto-research 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 6d 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.

drafts/auto-research/SKILL.md · 126 lines

How it starts

The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Channel

You are handed a research goal plus reference material and data, and no fixed argument to extract. The graph is the counterweight. Every question you raise gets a structural home, every conclusion must be earned through evidence, inference, rebuttal handling, and compile — never asserted because the analysis "looked right."

Framing Router

The first decision is where the Claims come from. Test each input proposition: could it be shown false?

  • Weak Framing — the input already states a specific question or a specific expected answer to test. Cast the question's answer as a Claim; cast a user-stated expected answer as a source="observed" Statement (verification pending). Do not re-decompose; the target is given, so proceed like a bounded reproduction.
  • Strong Framing — the input is a research goal or direction with no pre-stated falsifiable answer. Decompose it into a small set of answerable research questions, then create_claim (status starts proposed) — one Claim per question whose verdict matters. Because nothing external fixes the target, the falsification obligation below is mandatory before any evidence run.
  • Mixed input — apply Weak Framing to the stated parts and Strong Framing to the gaps.

Keep the initial Claim set small and load-bearing. Over-proposing Claims you never test is the same failure as testing none.

Graph Mapping

research goal / question                     -> one or more research questions
a falsifiable answer to a question           -> Claim (status=proposed)
a proposed or expected answer                -> Statement(source="observed", verification="pending")
a result computed from the given data        -> Statement(source="observed") as Ground
a citable proposition from a reference       -> Statement(source="literature") as Ground or Backing
"what result would show this Claim false"    -> a pre-registered refutation condition, tested as a Rebuttal candidate
why the evidence authorizes the conclusion   -> Warrant
the method or standard behind the inference  -> Statement as Backing
a confirmed conflict against Claim or Warrant-> Statement(rebuttal_for={target_id, target_type})

Read the full file on GitHub · 126 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.

  1. 6d ago First seen · 126 lines · 70 tokens per session scan A 41cb82b5a5a4

Subscribe to this mod's changes

auto-research is a skill published in the GitHub repository yqi96/warranted (2 stars, last pushed 4d ago), licensed MIT. It adds 70 tokens to every session and 2,623 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.

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