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 naveedharri/benai-skills --skill autoresearchgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/autoresearch)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/autoresearch"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/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/naveedharri/benai-skills/autoresearch"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 285 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
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 | $0.00059 | $0.05525 |
| Opus 5 | $0.00030 | $0.02763 |
| Sonnet 5 | $0.00012 | $0.01105 |
| Haiku 4.5 | $0.00006 | $0.00553 |
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 12d 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 — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoResearch — Autonomous Optimization Loop
USE WHEN the user runs /autoresearch, says "autoresearch", "optimize this prompt", "improve this overnight", "run an optimization loop", "iterate on this", "auto-improve", or wants to autonomously refine a file against measurable criteria.
Architecture: Separation of Roles
This skill supports two mutually exclusive evaluation modes. Both maintain four-way separation.
Deterministic Mode (default) — Four-Way Separation
| Role | Who | Knows Eval Code? | Knows Prompt History? |
|---|---|---|---|
| Main Agent | You (optimizer) | NO — reads metric number only | Yes — reads logs, plans changes |
| Eval Agent | autoresearch-eval-agent sub-agent |
Yes — writes eval.py | No |
| Test Runner | autoresearch-test-runner sub-agent |
NO — fresh context | NO |
| Judge Script | eval.py (deterministic Python) |
IS the eval | No |
Metric: pass_rate. Best for tasks with clear, mechanical quality checks (word count, format, keywords, structure).
AI Judge Mode (opt-in) — Four-Way Separation
| Role | Who | Knows Rubric? | Knows Prompt History? |
|---|---|---|---|
| Main Agent | You (optimizer) | Criteria names only | Yes — reads logs, plans changes |
| Eval Agent | autoresearch-eval-agent sub-agent |
Yes — writes rubric.md | No |
| Test Runner | autoresearch-test-runner sub-agent |
NO — fresh context | NO |
| Judge Agent | autoresearch-judge sub-agent |
Follows rubric | NO — fresh context |
Metric: quality_score. Best for creative/subjective tasks where mechanical checks miss the point (tone, authenticity, narrative, emotional resonance).
The modes are mutually exclusive. Deterministic mode uses eval.py only. AI judge mode uses the judge agent only. There is no combined score.
Key isolation (both modes):
- The optimizer never writes eval artifacts, never generates outputs
- The eval agent writes the eval system once (eval.py OR rubric.md), then disappears
- The test runner never sees the eval or rubric — fresh context every time
- The judge (script or agent) evaluates without knowing iteration history or optimizer intent
What ships with it
4 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.
- 12d ago First seen · 458 lines · 59 tokens per session scan A ff535f465e70
autoresearch is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 8d ago), licensed MIT. It adds 59 tokens to every session and 5,525 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 2026-08-30.
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