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 agentmods add skills/wesleysimplicio/simplicio-loop/simplicio-autoresearchnpx skills add wesleysimplicio/simplicio-loop --skill simplicio-autoresearchgit clone --depth 1 https://github.com/wesleysimplicio/simplicio-loopWrote 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/wesleysimplicio/simplicio-loop/simplicio-autoresearch)<a href="https://agentmods.dev/skills/wesleysimplicio/simplicio-loop/simplicio-autoresearch"><img src="https://agentmods.dev/badge/skills/wesleysimplicio/simplicio-loop/simplicio-autoresearch.svg" alt="Measured on agentmods" 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 | $0.00194 | $0.02473 |
| Opus 5 | $0.00097 | $0.01236 |
| Sonnet 5 | $0.00039 | $0.00495 |
| Haiku 4.5 | $0.00019 | $0.00247 |
Grade A, and why
simplicio-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 3d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
simplicio-autoresearch — evolutionary optimize-by-metric loop
Credit: Andrej Karpathy's autoresearch pattern
(https://github.com/balukosuri/Andrej-Karpathy-s-Autoresearch-As-a-Universal-Skill) and the ECC
bundle's autoresearch-agent, adapted into a first-class simplicio skill. The upstream loop is
NOT installed raw — it conflicts with this ecosystem's rules (unbounded iteration, no git
isolation discipline, single-metric hill-climbing). This skill fixes all three. The mechanical
bookkeeping (caps, git actions, journal, plateau math, receipt) is a deterministic, model-free
worker (scripts/autoresearch.py) — it never mutates the target itself. You (the LLM driving
this skill) propose the mutation; the worker enforces the contract around it.
yool guardrails (§11 — MANDATORY, not optional)
Register this skill's runtime as an agent with a hard cap on iterations and budget BEFORE the first mutation — an uncapped loop is a review-blocker, per spec:
### simplicio-autoresearch
- yool_id: `agent.dev.autoresearch`
- authority: dev
- lane: background
- agent_terms:
cpu_quota_pct: 60
disk_quota_mb: 100
timeout_s: 300
max_iterations: <N> # MANDATORY — set at `init`, never omitted
max_token_budget: <N> # MANDATORY — set at `init`, never omitted
scripts/autoresearch.py init refuses to start (exit 2) without both --max-iterations and
--max-token-budget as positive integers — the cap is enforced mechanically, not by convention.
record refuses (exit 12) any iteration number beyond the frozen max_iterations.
When to use / when NOT to use
Fit requires: a concrete target file (or small set of files), a command that can score it, and a correctness gate that is genuinely binary.
| Good fit (pilots) | Why |
|---|---|
| mapper — TOON encoder heuristics | eval = round-trip fixtures (correctness) + measured tokens on real artifacts (score) |
| dev-cli — prompt template | eval = the A/B bench pass-rate, fixed validation set |
| runtime — local model-ladder tuning | eval = ladder success-rate / latency on a fixed task set |
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.
- 3d ago First seen · 166 lines · 0 tokens per session scan A 09fdcd034134
simplicio-autoresearch is a skill published in the GitHub repository wesleysimplicio/simplicio-loop (2 stars, last pushed 4d ago), licensed MIT. It adds 194 tokens to every session and 2,473 once invoked, about $0.0010 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 2026-08-31.
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