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 aeonfun/aeon --skill autoresearchgit clone --depth 1 https://github.com/aeonfun/aeonWrote 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/aeonfun/aeon/autoresearch)<a href="https://agentmods.dev/skills/aeonfun/aeon/autoresearch"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/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/aeonfun/aeon/autoresearch"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/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 | $0.00019 | $0.01368 |
| Opus 5.5 | $0.00008 | $0.00547 |
| Sonnet 5.5 | $0.00004 | $0.00274 |
| Haiku 4.5 | $0.00002 | $0.00137 |
Grade A, and why
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.
There is no network sandbox — `curl` works, with **WebFetch** as the fallback for a flaky public GET. For an auth'd API, call `./secretcurl` with a `{ENV_NAME}` placeholder (the key is injected via `requires:`), never a Copies of this mod
1 near-identical copy found in the catalogue:
- autoresearch — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} — Name of the skill to evolve (e.g.
token-movers). Required.
If ${var} is empty, abort with: "autoresearch requires var= set to a skill name" and exit.
Read memory/MEMORY.md for context.
Goal
Improve an existing skill by researching better approaches, generating 4 distinct variations, scoring them against a rubric, and committing the winning version as a PR.
Steps
1. Load the target skill
Read skills/${var}/SKILL.md. If the file doesn't exist, abort and notify: "Skill '${var}' not found."
Parse the skill's:
- Purpose: what it does
- Data sources: APIs, URLs, commands it calls
- Output format: what it produces (article, notification, file)
- Dependencies: env vars, tools, other files it reads
Save the original content — you'll need it for the PR diff later.
2. Research improvements
Search the web for better approaches to what this skill does:
- Alternative or complementary APIs/data sources
- Best practices for the skill's domain (e.g., crypto analysis, RSS aggregation, security scanning)
- Common pitfalls or failure modes for the techniques the skill uses
- Output formats that are more actionable or readable
Also review:
- Recent memory/logs/ entries where this skill ran — did it produce useful output? Were there failures?
memory/cron-state.json— has this skill been failing?
3. Generate 4 variations
Create 4 distinct improved versions of the SKILL.md, each with a different thesis:
Variation A — Better inputs: Improve data sources. Add alternative/complementary APIs, better search queries, more reliable endpoints. Fix any broken or deprecated sources found in step 2.
Variation B — Sharper output: Improve the output format and content quality. Make notifications more actionable, articles more substantive, analysis more insightful. Reduce noise, improve signal.
Variation C — More robust: Improve reliability and edge-case handling. Add fallback logic for when APIs fail, better deduplication, graceful handling of empty data, clearer error messages.
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 · 143 lines · 19 tokens per session scan A 84e2d35e4119
autoresearch is a skill published in the GitHub repository aeonfun/aeon (778 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,368 once invoked, about $0.0001 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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