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 rcarmo/piclaw-addons --skill autoresearch-creategit clone --depth 1 https://github.com/rcarmo/piclaw-addonsWrote 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/rcarmo/piclaw-addons/autoresearch-create)<a href="https://agentmods.dev/skills/rcarmo/piclaw-addons/autoresearch-create"><img src="https://agentmods.dev/badge/skills/rcarmo/piclaw-addons/autoresearch-create/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/rcarmo/piclaw-addons/autoresearch-create"><img src="https://agentmods.dev/badge/skills/rcarmo/piclaw-addons/autoresearch-create.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 125 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00036 | $0.01901 |
| Opus 5.5 | $0.00014 | $0.00760 |
| Sonnet 5.5 | $0.00007 | $0.00380 |
| Haiku 4.5 | $0.00004 | $0.00190 |
Grade A, and why
autoresearch-create 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- autoresearch-create — 92% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch
Autonomous experiment loop: try ideas, keep what works, discard what doesn't, never stop.
Tools
init_experiment— configure session (name, metric, unit, direction). Call again to re-initialize with a new baseline when the optimization target changes.run_experiment— runs command, times it, captures output.log_experiment— records result.keepauto-commits.discard/crash/checks_failedauto-reverts code changes (autoresearch files preserved). Always include secondarymetricsdict. Dashboard: ctrl+x.
Setup
- Ask (or infer): Goal, Command, Metric (+ direction), Files in scope, Constraints.
git checkout -b autoresearch/<goal>-<date>- Read the source files. Understand the workload deeply before writing anything.
- Confirm this is a fresh experiment workspace. If unrelated stale
autoresearch.*files are already present, re-establish a clean workdir before continuing rather than silently resuming the wrong experiment. - Write
autoresearch.mdandautoresearch.sh(see below). Commit both. init_experiment→ run baseline →log_experiment→ start looping immediately.
autoresearch.md
This is the heart of the session. A fresh agent with no context should be able to read this file and run the loop effectively. Invest time making it excellent.
# Autoresearch: <goal>
## Objective
<Specific description of what we're optimizing and the workload.>
## Metrics
- **Primary**: <name> (<unit>, lower/higher is better) — the optimization target
- **Secondary**: <name>, <name>, ... — independent tradeoff monitors
## How to Run
`./autoresearch.sh` — outputs `METRIC name=number` lines.
## Files in Scope
<Every file the agent may modify, with a brief note on what it does.>
## Off Limits
<What must NOT be touched.>
## Constraints
<Hard rules: tests must pass, no new deps, etc.>
## What's Been Tried
<Update this section as experiments accumulate. Note key wins, dead ends,
and architectural insights so the agent doesn't repeat failed approaches.>
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.
- 2mo ago First seen · 147 lines · 36 tokens per session scan A bd7670248964
autoresearch-create is a skill published in the GitHub repository rcarmo/piclaw-addons (23 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 1,901 once invoked, about $0.0001 per session on Opus 5.5. 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
source-verify
A source-checking workflow for confirming that medical information comes from an authoritative, complete, and currently valid document.
sn-search-academic
An academic research tool for finding papers and encyclopedia entries, reading papers in full or by section, and tracing references and citations. It supports structured literature research.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
molecular-cloning
Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.
geo-database
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.