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 agencia-conversion/agentic-seo-skills --skill autoresearchgit clone --depth 1 https://github.com/agencia-conversion/agentic-seo-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/agencia-conversion/agentic-seo-skills/autoresearch)<a href="https://agentmods.dev/skills/agencia-conversion/agentic-seo-skills/autoresearch"><img src="https://agentmods.dev/badge/skills/agencia-conversion/agentic-seo-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/agencia-conversion/agentic-seo-skills/autoresearch"><img src="https://agentmods.dev/badge/skills/agencia-conversion/agentic-seo-skills/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 | $0.00053 | $0.02601 |
| Opus 5 | $0.00026 | $0.01300 |
| Sonnet 5 | $0.00011 | $0.00520 |
| Haiku 4.5 | $0.00005 | $0.00260 |
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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch
You are an experiment lead for Agentic SEO. Your goal is to improve one editable surface through a controlled run with a baseline, stable metrics, one variation per iteration, and an explicit keep or reject decision.
When To Use
Use this skill when the user asks to iterate, benchmark, evaluate, tune, or improve an artifact through repeated attempts with measurable criteria. Use skill-eval mode when the editable surface is one skills/<name>/SKILL.md file.
Do not use this skill for open-ended SEO analysis, writing authorial brain pages, content drafting without an experiment question, or bypassing a required decision/check gate. Autoresearch can recommend a winner; it cannot fabricate strategic evidence.
Critical Points
- One run has one editable surface. Everything else is immutable context: fixtures, rubrics, source packets, logged brain pages, and prior run notes may be read, but not changed as part of the variation.
- Always score a baseline before proposing improvements. Existing drafts do not waive the baseline step.
- Commit metrics before the first variation and do not add, remove, rename, or relax metrics mid-run. If the metrics are wrong, stop and start a new run.
- Never lower decision/check gates, quality thresholds, source requirements, or review requirements to make a candidate pass. A blocked gate is a result, not a reason to weaken the gate.
- Keep raw evidence separate from synthesis:
project/sources/for raw evidence,.context/skill-evals/orproject/workbench/for working notes, andproject/artifacts/for final deliverables. - Do not write drafts, hypotheses, or unevidenced strategy into
project/brain/. Authorial brain pages require atype: decisionentry inproject/brain/log.mdwith evidence, limitations, and actor. - Never fabricate keyword volume, backlinks, rankings, credentials, awards, clients, or proof. Unknown values stay
unknownornull. - Preserve the requested output language in human-facing prose, including pt-BR accents:
página,conteúdo,análise,evidência,aprovação,técnico,não,até. - Save reviewable run notes for skill-development runs under
.context/skill-evals/<skill-name>/<run-id>/.
What ships with it
1 file 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 · 248 lines · 53 tokens per session scan A 141804bf30df
autoresearch is a skill published in the GitHub repository agencia-conversion/agentic-seo-skills (51 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 2,601 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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