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 oliver-kriska/claude-elixir-phoenix --skill autoresearchgit clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenixWrote 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/oliver-kriska/claude-elixir-phoenix/autoresearch)<a href="https://agentmods.dev/skills/oliver-kriska/claude-elixir-phoenix/autoresearch"><img src="https://agentmods.dev/badge/skills/oliver-kriska/claude-elixir-phoenix/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/oliver-kriska/claude-elixir-phoenix/autoresearch"><img src="https://agentmods.dev/badge/skills/oliver-kriska/claude-elixir-phoenix/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 | $0.00052 | $0.01193 |
| Opus 5.5 | $0.00021 | $0.00477 |
| Sonnet 5.5 | $0.00010 | $0.00239 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
lab: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 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.
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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch — Plugin Skill Self-Improvement
Iteratively improve plugin skills via the autoresearch pattern: propose one mutation -> eval -> keep/revert -> repeat.
Usage
/lab:autoresearch # Targeted: attack weakest skill+dimension
/lab:autoresearch --skill review # Focus on one skill
/lab:autoresearch --strategy sweep # Process all skills alphabetically
/lab:autoresearch --dry-run # Show what would change, don't commit
For overnight runs:
/loop 5m /lab:autoresearch --strategy sweep --max-iterations 200
Iron Laws
- ONE mutation per iteration — if description needs "and", split into two
- NEVER mutate read-only files — check program.md before every write
- EVAL is deterministic — always use the wrapper script, never LLM-judge
- REVERT on regression OR checks failure — no exceptions
- LOG every iteration — use
keeporrevertcommand (never skip) - CHECK ideas.md before proposing — don't rediscover known optimizations
Wrapper Script Commands
All eval/git/journal operations go through ONE script. Do NOT run these manually.
# Find the weakest skill+dimension
python3 lab/autoresearch/scripts/run-iteration.py target --strategy targeted
# Score a skill (before mutation, to get baseline)
python3 lab/autoresearch/scripts/run-iteration.py score <skill-name>
# After mutation: score + checks + compare → verdict (KEEP or REVERT)
python3 lab/autoresearch/scripts/run-iteration.py eval <skill-name>
# Act on verdict:
python3 lab/autoresearch/scripts/run-iteration.py keep <skill> <dim> <old> <new> \
--desc "what changed" --asi '{"hypothesis": "why", "mechanism": "how"}'
python3 lab/autoresearch/scripts/run-iteration.py revert <skill> <dim> <old> <new> \
--desc "what was attempted" --asi '{"hypothesis": "why", "regression": "what broke", "avoid": "do not retry this"}'
# Check overall progress
python3 lab/autoresearch/scripts/run-iteration.py status
What ships with it
12 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.
- .gitignore 130 B
- program.md 3.9 KB
- references/mutation-strategies.md 2.4 KB
- references/state-management.md 1.1 KB
- retention.py 5.7 KB runs code
- scripts/checks.sh 3.2 KB runs code
- scripts/protected_sections.py 3.4 KB runs code
- scripts/run-iteration.py 25 KB runs code
- scripts/score-skill.py 1.2 KB runs code
- tests/__init__.py 0 B runs code
- tests/test_deviation_dispatch.py 3.1 KB runs code
- tests/test_protected_sections.py 3.3 KB runs code
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 · 131 lines · 52 tokens per session scan A c72c8d11ef02
lab:autoresearch is a skill published in the GitHub repository oliver-kriska/claude-elixir-phoenix (571 stars, last pushed 6d ago), licensed MIT. It adds 52 tokens to every session and 1,193 once invoked, about $0.0002 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.
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