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 a-tokyo/agent-skills --skill create-skill-autoresearchgit clone --depth 1 https://github.com/a-tokyo/agent-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/a-tokyo/agent-skills/create-skill-autoresearch)<a href="https://agentmods.dev/skills/a-tokyo/agent-skills/create-skill-autoresearch"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills/create-skill-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/a-tokyo/agent-skills/create-skill-autoresearch"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills/create-skill-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 | $0.00118 | $0.06166 |
| Opus 5.5 | $0.00047 | $0.02466 |
| Sonnet 5.5 | $0.00024 | $0.01233 |
| Haiku 4.5 | $0.00012 | $0.00617 |
Grade A, and why
create-skill-autoresearch 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
and the test case input as the user message. Use `curl` to an OpenAI-compatible Copies of this mod
1 near-identical copy found in the catalogue:
- create-skill-autoresearch — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Skill via Autoresearch Factory
A factory for forging production-grade agent skills through gold-standard-driven autoresearch, multi-agent verification, and structured consensus.
The factory orchestrates 4 agent roles through 5 phases:
| Phase | What Happens | Agent Role |
|---|---|---|
| 1. Interview | Discover purpose, gold standards, scope | ORCHESTRATOR |
| 2. Research | Study domain materials, build dossier, propose rubric | RESEARCHER (N parallel) |
| 3. Draft | Design structure, generate SKILL.md, measure baseline | BUILDER |
| 4. Autoresearch | Iterate skill against gold standards (LLM-as-judge, or an objective real-world metric for procedural skills — see 3.4) | BUILDER + autoresearch skill |
| 5. Verify | Premortem, panel scoring, consensus, ship/iterate | PANEL (3 subagents) |
Key constraint: BUILDER and PANEL never share context. Panel receives only the skill output, gold standards, and rubric -- no bias from the building process.
Relation to create-skill
This factory extends the official single-pass skill creators (Anthropic's Skills best-practices and skill-creator; Cursor's create-skill) rather than replacing them. It adds what a one-shot generator cannot: a research dossier, gold-standard benchmarking, an autonomous improvement loop, and independent multi-agent verification. The skills it produces follow the same official conventions -- see references/skill-authoring-best-practices.md.
Companion skills
The factory orchestrates these sibling skills at runtime: autoresearch (Phase 4 improvement loop), premortem (Phase 5 risk pass), and handoff (cross-session continuity); the Phase 5 panel/consensus design draws on llm-council. In this harness they are vendored under .agents/skills/. If you install this skill standalone, install those alongside it. The factory's craft layer (references/skill-craft-principles.md) is distilled from writing-great-skills (mattpocock/skills, MIT), which the harness vendors under .agents/skills/. Phase 5 will delegate to tribunal when it is installed (see 5.2).
What ships with it
7 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.
- 1mo ago First seen · 460 lines · 118 tokens per session scan A 61cfb028f507
create-skill-autoresearch is a skill published in the GitHub repository a-tokyo/agent-skills (17 stars, last pushed 2d ago), licensed MIT. It adds 118 tokens to every session and 6,166 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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