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.
npx skills add ShreeMulay/autoresearch-mcp --skill autoresearchgit clone --depth 1 https://github.com/ShreeMulay/autoresearch-mcpWrote 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/shreemulay/autoresearch-mcp/autoresearch)<a href="https://agentmods.dev/skills/shreemulay/autoresearch-mcp/autoresearch"><img src="https://agentmods.dev/badge/skills/shreemulay/autoresearch-mcp/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.
<a href="https://agentmods.dev/skills/shreemulay/autoresearch-mcp/autoresearch"><img src="https://agentmods.dev/badge/skills/shreemulay/autoresearch-mcp/autoresearch.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 | $0.00094 | $0.02885 |
| Opus 5 | $0.00047 | $0.01443 |
| Sonnet 5 | $0.00019 | $0.00577 |
| Haiku 4.5 | $0.00009 | $0.00288 |
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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch Skill
When to Activate
Explicit Triggers
User says any of: "optimize", "improve this", "run experiments", "find best technique", "ratchet", "hill-climbing", "prompt optimization", "autoresearch", "experiment tracking", "measure performance", "tune hyperparameters", "iterate on", "A/B test", "champion-challenger"
Implicit Signals
- User describes a problem with a measurable outcome but no clear solution path
- User wants to improve something that has been working but could be better
- User mentions comparing options, iterating, or testing variations
- User is writing prompts, code, configs, or content and wants the best version
When NOT to Use
- One-off tasks with no repeatable evaluation ("write a greeting email")
- Problems with no measurable metric ("make this nicer")
- Time-critical fixes where experimentation delays matter ("production is down")
- User explicitly says "just pick one" or "I don't care about optimal"
Data Safety Boundary
Use synthetic, non-sensitive data only; examples and tests use synthetic data only. Never send PHI, patient identifiers, PHI-bearing prompts or model responses, clinical records, secrets, or production datasets to this MCP server, evaluators, review tooling, logs, fixtures, or CI.
Core Philosophy
Autoresearch is iterative improvement against a repeatable evaluation. The loop is:
Discover → Suggest → Scaffold → Run → Evaluate → Log → Ratchet → Meta-Learn
Ratchet principle: Only keep improvements. The best-so-far (champion) is replaced only by something measurably better.
Meta-learning principle: Every experiment teaches us what works in which domain. Log outcomes so future suggestions improve.
Decision Tree: Which Technique?
Start here. Answer these questions in order.
Q1: Do you have a scalar metric?
A single number that defines success (accuracy, latency, score, cost, conversion rate).
YES → Use a ratchet pattern (single-ratchet, champion-challenger, two-loop) NO → Use an evaluator-first approach (llm-as-judge, rubric-scorer, human-approval-gate)
What ships with it
3 files 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.
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.
- 11d ago First seen · 251 lines · 94 tokens per session scan A de9114e1add0
autoresearch is a skill published in the GitHub repository ShreeMulay/autoresearch-mcp (0 stars, last pushed 2d ago), licensed Apache-2.0. It adds 94 tokens to every session and 2,885 once invoked, about $0.0005 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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