plannotator-compound

Analyzes denied Plannotator plans to extract patterns and build corrective prompts.

1|1|Updated Apr 24, 2026
One-click install
npx skills add https://github.com/luan/agents --skill plannotator-compound
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: plannotator-compound
Source: https://github.com/luan/agents/tree/main/skills/plannotator-compound
Command: npx skills add https://github.com/luan/agents --skill plannotator-compound

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It analyzes a user's Plannotator plan archive (or falls back to Claude Code ExitPlanMode logs) to extract denial patterns and convert them into actionable, personalized prompt instructions, then packages everything into a polished HTML dashboard report.

Core Features & Use Cases

  • Archive analysis (first-class Plannotator): Reads all denied plans from ~/.plannotator/plans/ and builds a denial taxonomy, recurring phrases, and an evolution-over-time view.
  • Claude Code fallback mode: If Plannotator data is missing, it extracts human-authored denial reasons from Claude Code JSONL logs using the bundled parser in scripts/.
  • Map-reduce extraction + reduction: Uses deterministic, batched extraction for every denied item, then performs analytical reduction to produce the taxonomy and corrective prompt.
  • Versioned HTML dashboard output: Writes a self-contained, versioned report to the user's plans directory, with incremental options when prior reports exist.
  • Optional improvement hook: Can write the corrective instructions to a Plannotator EnterPlanMode hook file so future planning sessions automatically incorporate the user's denial patterns.

Quick Start

Ask the AI to run the plannotator-compound skill to generate your compound planning denial dashboard report from your local Plannotator archive.

Frequently Asked Questions about plannotator-compound

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

FAQPage Schema
How do I analyze Claude Code ExitPlanMode denial logs to improve my prompts?

Analyzing Claude Code ExitPlanMode denial logs extracts human-authored denial reasons from JSONL files to build a feedback taxonomy and generate a corrective prompt. The parser reads the local log directories to identify recurring rejection patterns and compute actionable planning instructions.

What is the best way to extract denial patterns from a plan archive?

Extracting denial patterns from a plan archive requires exhaustive reading and map-reduce batching across denied items. This process reduces structured denial reasons into ranked taxonomies and evolution insights, rendering the results into a self-contained versioned HTML dashboard.

Can I automatically apply corrective prompt instructions to future planning sessions?

Corrective prompt instructions can be automatically applied to future planning sessions by writing an improvement hook file. This hook integrates the computed denial taxonomy directly into the EnterPlanMode workflow, ensuring subsequent plans avoid past rejection reasons.

Does the denial analysis workflow support incremental HTML reporting based on prior cutoffs?

The denial analysis workflow supports incremental HTML reporting based on prior report cutoffs. When a versioned HTML dashboard already exists in the plans directory, the system can generate an updated report that only processes new denied items since the last analysis run.

What happens if Plannotator plan data is missing during denial analysis?

If Plannotator plan data is missing, the denial analysis falls back to parsing Claude Code JSONL logs. The bundled parser in the scripts directory extracts structured denial reasons directly from the ExitPlanMode logs to ensure the taxonomy extraction and reduction process continues.

When should I not use map-reduce batching for denied plan extraction?

Map-reduce batching for denied plan extraction should not be used when the plan archive contains only a single denied item or lacks structured denial reasons. The batched extraction and analytical reduction process requires multi-file archival workflows with strict integrity checks to function correctly.