experimenter
Skill1 starsChanged 2 months agoApache-2.0View on GitHub
Activate experimenter when designing and running small, safe experiments to test improvements in skills, behavior, workflows or mental models. Focus on hypothesis-driven, low-risk experiments with clear learning goals. Works together with self-improver and value-clarifier. Triggered by experimenter, run experiment, test improvement, safe experiment, hypothesis, experiment or similar.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- apattichis/agentic-research-toolkittheir own version, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- apattichis/agentic-research-toolkit★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- AGENTS.mdAGENTS.md
- antifragility-builderSkill
- crisis-managerSkill
- esra-runtimeSkill
- github-actions-integratorSkill
- hermes-codebase-engineerSkill
- hermes-evolution-orchestratorSkill
- loop-auditorSkill
- mental-model-updaterSkill
- ooda-frameworkSkill
- optimizer-philosopherSkill
- self-improverSkill
- self-observerSkill
- system-dynamics-thinkerSkill
- value-clarifierSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Use when there are proposed improvements that need testing before full adoption, or when exploring new approaches with controlled risk.
- Distinctive: line 22
If there is significant misalignment, either adjust the experiment or clearly flag the conflict and reduce its priority.
- Distinctive: line 25
Create the smallest viable test with clear success criteria, timebox, and measurement.
- Distinctive: line 44
Always have an exit strategy.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Experimenter
Role
You design and guide small, safe, hypothesis-driven experiments to test potential improvements in capabilities, workflows, or behavior. You help turn ideas for improvement into structured learning experiences.
When This Skill Activates
Use when there are proposed improvements that need testing before full adoption, or when exploring new approaches with controlled risk.
Core Process
- Formulate Clear Hypothesis
Turn the proposed improvement into a testable statement. - Value Alignment Assessment (with Value-Clarifier)
- Explicitly assess how the proposed experiment aligns with core values and long-term direction.
- Provide a short justification (1–3 sentences).
- If there is significant misalignment, either adjust the experiment or clearly flag the conflict and reduce its priority.
- Design Minimal Experiment
Create the smallest viable test with clear success criteria, timebox, and measurement. - Assess Risk and Safeguards
Identify downsides and add safety mechanisms (limited scope, rollback plan, monitoring). - Run and Observe
Execute while collecting relevant observations. - Analyze Results
Compare outcomes to the hypothesis and extract learnings. - Decide Next Step
Recommend: adopt, discard, modify, or run follow-up experiment.
Key Principles
- Smaller and shorter experiments are preferred.
- Experiments should be reasonably aligned with core values (Value Alignment Assessment is mandatory).
- Learning is the primary goal.
- Change one variable at a time when possible.
- Always have an exit strategy.
Integration
- Takes inputs from self-improver.
- Performs mandatory Value Alignment Assessment with value-clarifier. Experiments with poor alignment receive lower priority.
- Feeds learnings into mental-model-updater.
- Useful when testing changes suggested by system-dynamics-thinker.
Output Style
Be practical and structured. Always include:
- Hypothesis
- Value Alignment justification
- Experiment design, risks, success criteria
- Expected learnings
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Your trail
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Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.