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 notforyou23/home23 --skill autoresearchgit clone --depth 1 https://github.com/notforyou23/home23Wrote 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/notforyou23/home23/autoresearch)<a href="https://agentmods.dev/skills/notforyou23/home23/autoresearch"><img src="https://agentmods.dev/badge/skills/notforyou23/home23/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/notforyou23/home23/autoresearch"><img src="https://agentmods.dev/badge/skills/notforyou23/home23/autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00827 |
| Opus 5 | $0.00022 | $0.00413 |
| Sonnet 5 | $0.00009 | $0.00165 |
| Haiku 4.5 | $0.00004 | $0.00083 |
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 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.
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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch
Use this skill when a skill exists but performs inconsistently and needs deliberate improvement rather than one-off fixes.
When to use
Use autoresearch for:
- a skill that triggers inconsistently across similar asks
- a skill whose instructions feel vague, brittle, or underspecified
- quality work where the target is the skill itself, not just the current answer
Workflow
- Define the failure mode clearly.
- Choose a score rubric (5 dimensions, 1–5 each).
- Run representative prompts against the skill across N rounds.
- Auto-revise
SKILL.mdafter each round (targets weakest dimension). - Stop when score gain flattens (≤ 0.2 improvement).
- Return a scored report with per-round results and recommendations.
Score Rubric (5 dimensions)
| Dimension | What it measures |
|---|---|
queryStrategy |
Detects zero/low results and auto-broadens or retries |
qualityFiltering |
Filters spam, elevates high-engagement signal |
resultCoverage |
Gets meaningful result volume per query |
actionContract |
Returns well-formed, predictable responses |
documentation |
SKILL.md examples and gotchas are clear and actionable |
Scores: 1 (broken) → 3 (functional) → 5 (excellent).
autoresearch_loop action
{
"action": "autoresearch_loop",
"input": {
"targetSkill": "x-research",
"failureMode": "X search returns spam for health queries with no retry logic",
"promptSet": [
"sauna cold plunge contrast therapy",
"infrared sauna health benefits",
"cold plunge recovery sleep"
],
"scoreRubric": {
"queryStrategy": { "target": 4 },
"qualityFiltering": { "target": 3 },
"resultCoverage": { "target": 3 },
"actionContract": { "target": 4 },
"documentation": { "target": 3 }
},
"maxRounds": 3
}
}
Output
Returns a JSON report with:
rounds[]: per-round scores per prompt and dimension averagessummary: start/final/total gain, rounds run, stopped-early flagrecommendations[]: dimensions still scoring < 3 after loop
What ships with it
5 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.
- 10d ago First seen · 109 lines · 44 tokens per session scan A 0ecf20e70dee
Autoresearch is a skill published in the GitHub repository notforyou23/home23 (11 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 827 once invoked, about $0.0002 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.
Other skills, from other repositories
skill-creator
Propose, test, validate, and improve Skill candidates without changing active files - generate reviewable SKILL.md and handler.js drafts, run test evaluations, and optimize descriptions for better triggering.
skill-scorer
A review tool for Agent Skills, which are instruction files that guide coding agents. It supports skills made for Cursor, Claude, and OpenClaw.
skill-review
Critically review a workspace skill and suggest improvements. Use when asked to review, audit, critique, evaluate, or improve a SKILL.md file or skill directory. Covers frontmatter validation, instruction clarity, completeness, and adherence to the Agent Skills Specification.
author-skill
Use when authoring a NEW rsc skill or editing an existing one — scoping it to one job, writing the description that decides whether it ever loads, splitting the body into references/, writing its evals, auditing it against the rubric. NOT building a product feature (that is specify) and NOT designing an agent loop…
debugging
A debugging-themed dating skill for AI agents on inbed.ai, a platform for agent profiles, matching, and conversations. Here, debugging means finding and fixing problems in connections rather than only in software.
manage-skills
A skill for managing the full lifecycle of agent skills, including importing, creating, installing, updating, enabling, disabling, copying, and deleting them.