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.
git clone --depth 1 https://github.com/wjgoarxiv/autoresearch-skillnpx agentmods add skills/wjgoarxiv/autoresearch-skill/planWrote 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.
[](https://agentmods.dev/skills/wjgoarxiv/autoresearch-skill/plan)<a href="https://agentmods.dev/skills/wjgoarxiv/autoresearch-skill/plan"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/plan/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.
<a href="https://agentmods.dev/skills/wjgoarxiv/autoresearch-skill/plan"><img src="https://agentmods.dev/badge/skills/wjgoarxiv/autoresearch-skill/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00134 | $0.02338 |
| Opus 5 | $0.00067 | $0.01169 |
| Sonnet 5 | $0.00027 | $0.00468 |
| Haiku 4.5 | $0.00013 | $0.00234 |
Grade A, and why
autoresearch:plan scanned grade A 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 10d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(["python", "TARGET_SCRIPT.py"], check=True) How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autoresearch:plan — Research Setup Wizard
A 7-step interview that produces a complete research.md (and optionally evaluate.py) before a single experiment runs. The wizard is conversational — ask each step, wait for the answer, then proceed. Do not batch all questions at once.
Wizard Protocol
One step at a time. Present the step title and question(s). Wait for the user's response. Summarize what you recorded ("Got it — I'll set metric: accuracy, direction: maximize"). Then proceed to the next step.
Do not skip steps. Each step produces a concrete artifact that feeds Step 7. If the user's answer is vague, probe once for specificity, then record your best interpretation and note it as an assumption.
Step 1 — Goal Clarification
Probe for specificity. Vague goals produce useless research loops.
Ask:
- "What are you trying to improve or discover?"
- "What does success look like in concrete terms — not 'better', but what number or outcome?"
- "Is there anything this work must NOT break?"
Probe rules:
- If the answer contains words like "better", "faster", "improve" without a reference point → ask "compared to what baseline?"
- If no domain is mentioned → ask "what system/file/model/prompt are we working on?"
- If multiple goals are stated → ask "if you could only achieve one of these, which one?"
Record: goal_statement (1-2 sentences, specific and measurable)
Step 2 — Metric Definition
What to measure, how to measure it, and what direction counts as progress.
Ask:
- "What is the single number that determines if this experiment succeeded or failed?"
- "Are you maximizing or minimizing it?"
- "What value would make you stop and say 'we're done'? That's the target."
- "Is this metric noisy? (e.g., varies between runs due to randomness or timing)"
Guide the user if stuck:
- Performance → latency (ms), throughput (req/s), memory (MB) — direction: minimize
- Quality → accuracy (%), F1, LLM-judge score (1-10) — direction: maximize
- Cost → tokens, dollars, lines of code — direction: minimize
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.
- 10d ago First seen · 279 lines · 134 tokens per session scan A af5d9d926b62
autoresearch:plan is a skill published in the GitHub repository wjgoarxiv/autoresearch-skill (32 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 2,338 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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