agentleFS
Sign inStart free
PthahnixPthahnix/De-Anthropocentric-Research-Engine/skills/analyze-intervention/SKILL.md

analyze-intervention

Skill504 starsChanged 49 days agoApache-2.0View on GitHub

Apply an intervention/do-style graph modification and propagate predicted directional effects while recording assumptions.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the intervention analysis yields a causal attribution that needs an explicit validity check, consider validate-causal-link as the next …

    line 27
  2. Distinctive:

    Do not attribute an effect to the intervention when co-interventions or implementation changes are untracked.

    line 43

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

analyze-intervention

Purpose

Analyze an intervention's target, mechanism, implementation conditions, and observed effects.

Input contract

required: [intervention, target_system, outcome_records]
optional: [implementation_records, comparator, mechanism_hypotheses]
constraints: [effect interpretation must retain implementation and comparison conditions]

Procedure

  1. Define intervention components, dose, timing, and target mechanism.
  2. Map implementation fidelity and deviations to observed conditions.
  3. Compare outcomes with the declared comparator and plausible alternatives.
  4. Summarize mechanism evidence, effect heterogeneity, and unresolved attribution.

If the intervention analysis yields a causal attribution that needs an explicit validity check, consider validate-causal-link as the next tactic.

Output contract

produces: [intervention_map, implementation_profile, outcome_comparison, mechanism_assessment, attribution_limits]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]

Quality gates

  • Intervention components and implementation are separated.
  • Comparator and outcome conditions are explicit.

Failure and counterexamples

Do not attribute an effect to the intervention when co-interventions or implementation changes are untracked.

Provenance map

  • resolved: analyze-intervention

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

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.

Check your setup, free