skills.rest
Dicklesworthstone
DicklesworthstoneCOMMUNITY
Explore all skills by this author →
Website𝕏@doodlesteinGitHub

active-learning-experiment-planner

Plan active-learning experiments from ee observation ledgers and uncertainty reports.

41|3|Updated Apr 29, 2026
Search Tags:#machine learning#experiment planning#active learning#evidence-based decisions#redaction safety#uncertainty measurement#dry-run orchestration
One-click install
npx skills add https://github.com/Dicklesworthstone/eidetic_engine_cli --skill active-learning-experiment-planner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: active-learning-experiment-planner
Source: https://github.com/Dicklesworthstone/eidetic_engine_cli/tree/main/skills/active-learning-experiment-planner
Command: npx skills add https://github.com/Dicklesworthstone/eidetic_engine_cli --skill active-learning-experiment-planner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

It helps coding agents decide which learning experiment to run next using existing ee observation, uncertainty, and evaluation records, while preventing unsafe or unsupported closeout decisions.

Core Features & Use Cases

  • Evidence-gated experiment planning from ee learn summaries, uncertainty reports, and evaluation/causal/economy/context bundles.
  • Safety and data-integrity boundaries that forbid direct memory mutation and require explicit dry-run or user confirmation for follow-up actions.
  • Stop/go refusal modes for empty observations, malformed inputs, degraded dependencies, underpowered samples, failed redaction, and unquarantined prompt-injection-like evidence.

Quick Start

Ask your agent: plan the next learning experiment for my workspace using my existing ee learning ledger data, and output a measurable dry-run experiment with follow-up ee commands.

Frequently Asked Questions about active-learning-experiment-planner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I plan the next active learning experiment using machine learning uncertainty reports?▼

The planner refuses empty observations, malformed inputs, degraded dependencies, and underpowered samples. It also enforces redaction and prompt-injection quarantine checks, stopping execution if unquarantined evidence is detected.

How do I ensure safe experiment planning without direct memory mutation in my coding agent?▼

Yes, the planner supports stop or go refusal modes for empty observations, malformed inputs, degraded dependencies, underpowered samples, failed redaction, and unquarantined prompt-injection-like evidence. It refuses to proceed when these safety and data-integrity conditions are not met.

What inputs do I need to generate measurable dry-run experiments from learning ledgers?▼

Evidence-gated experiment planning prevents unsafe or unsupported decisions by requiring strict dry-run boundaries. It stops execution when inputs are empty, malformed, or degraded, ensuring the agent does not turn into a judgment engine for unsupported closeout decisions.

Does active learning experiment planning work with causal and economy evidence bundles?▼

The planner outputs template-driven structured results containing measurable dry-run experiments and follow-up ee commands. This structured output ensures the coding agent receives clear, mechanical instructions rather than acting as a judgment engine.

When should I not use automated experiment planning for learning loops?▼

The planner enforces strict mechanical command boundaries by requiring explicit dry-run or user confirmation for follow-up actions. It forbids direct memory mutation and outputs template-driven structured commands, preventing the agent from making unsupported judgment calls.

Related Skills

Explore More Education & Research Skills→
DicklesworthstoneDicklesworthstone
41

causal-credit-review

Review causal ledger evidence and issue promote, demote, or reroute recommendations.

Community
Advanced
getagentsealgetagentseal
428

lean-startup

Validate product ideas through rapid, data-driven experiments and Build-Measure-Learn loops.

Official
Advanced
featbitfeatbit
1

learning-capture

Capture structured experiment learnings with evidence-based summaries and causal interpretations.

Official
Intermediate
savvidessavvides
1

evidence-check

Classify product evidence against ESSA tiers and generate a gap analysis and one-tier action plan.

Community
Advanced