What is autoresearch:improve?

A product-improvement research command that studies the challenges faced by a target customer group and turns findings into product requirement documents. A product requirement document, or PRD, describes proposed features and what they should achieve.

Use it to research customer pain points, identify improvement opportunities, select features, and generate PRDs.

It helps connect customer problems to concrete product changes instead of guessing which features to build. It can use information from earlier debugging, security, or prediction work when available.

autoresearch:improve — Command for Claude Code 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.

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/improve/github.svg)](https://agentmods.dev/commands/wdblink/autoresearch-paper/improve)

Your own site

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

Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,514 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 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	$0.00016	$0.01514
Opus 5.5	$0.00006	$0.00606
Sonnet 5.5	$0.00003	$0.00303
Haiku 4.5	$0.00002	$0.00151

Measured 3d ago against content hash b5bd49cd4386, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

autoresearch:improve 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 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.

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

97% identical to autoresearch:improve — 0 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.

.claude/commands/autoresearch/improve.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

EXECUTE IMMEDIATELY.

Parse Arguments

Extract from $ARGUMENTS:

Goal: — product area to improve (or full $ARGUMENTS if no keyword)
--icp or ICP: — ideal customer profile description
--discover — force inline codebase scan even when context exists
--no-discover — skip auto-discover, warn instead
--seeds <categories> — override default research category seeds
--depth — shallow (5 iterations), standard (15), deep (30)
--features — comma-separated feature names to pre-select for PRD generation
Iterations: or --iterations — default 15. "unlimited" for unbounded.
--evals, --evals-interval N

If upstream handoff.json exists in CWD → read it. Map source findings to default seed categories:

probe → ICP challenges, UX & experience
predict → Competitor gaps, Revenue & growth
debug/security → Competitor gaps, ICP challenges
Override with --seeds.

Setup (if Goal or ICP missing)

AskUserQuestion (single batch): Q1 (Goal): "What product area to improve?" — open text Q2 (ICP): "Who is your ideal customer?" — open text describing target buyer/user Q3 (Pain points): "Top 3 pain points your customers face?" — open text Q4 (Competitors): "Key competitors?" — open text, or "skip" Q5 (Depth): "How deep?" — shallow (5 iterations, quick scan), standard (15, recommended), deep (30+, exhaustive) If all provided inline → skip.

Phase 1: Product Context

Resolve product context (priority chain):

Learn summary (autoresearch/learn-*/summary.md, most recent) → read it
README.md (≥500 chars, non-boilerplate) → extract product description
package.json / pyproject.toml / Cargo.toml description (≥10 chars) → use it
If ALL above absent AND NOT --no-discover → auto-discover: scan 10 key files (manifest, routes, models, config), cap 1500 tokens
If --discover → force scan regardless of above
If nothing found → warn: "No product context. Run /autoresearch:learn --mode summarize for better results."

Read the full file on GitHub · 118 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 · 118 lines · 16 tokens per session scan A b5bd49cd4386

Subscribe to this mod's changes

autoresearch:improve is a command published in the GitHub repository WdBlink/autoresearch-paper (2 stars, last pushed 4d ago), licensed MIT. It adds 16 tokens to every session and 1,514 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to autoresearch:improve, differing in 0 lines, and is treated as a copy.

Planning Research academic-writing agent-skills ai ai-agents autonomous-research

autoresearch:improve next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
autoresearch:improveWdBlink/autoresearch-paper	Command	2	4d ago	MIT	A	16	Claude Code
ars-planImbad0202/academic-research-skills	Command	56k	yesterday	none	A	19	Claude Code
ars-revision-coachImbad0202/academic-research-skills-codex	Command	14k	5d ago	none	A	27	Claude Code
ars-3wImbad0202/academic-research-skills-codex	Command	14k	5d ago	none	A	24	Claude Code
nextProjectDXAI/labrat	Command	237	3mo ago	MIT	A	0	Claude Code
ja-fallbackZaious/journal-atlas	Command	2	10d ago	none	A	37	Claude Code
ai-reviewStill-chowline394/MagicTeX-mcp	Command	0	1mo ago	AGPL-3.0	A	13	Claude Code