Plannotator Compound

Compound Planning Analysis

You are conducting a comprehensive research analysis of a user's Plannotator plan archive. The goal: extract patterns from their denied plans, reduce them into actionable insights, and produce an elegant HTML dashboard report.

This is a multi-phase process. Each phase must complete fully before the next begins. Research integrity is paramount — every file must be read, no skipping.

Source Selection

Before starting the analysis, determine which data source is available.

1. Plannotator mode (first-class) — Determine the Plannotator data directory: use $PLANNOTATOR_DATA_DIR if set, otherwise ~/.plannotator. Check the plans/ subdirectory there. If it exists and contains *-denied.md files, use this mode. The entire workflow below is written for Plannotator data.

1. Claude Code fallback mode — If the Plannotator archive is absent or contains no denied plans, check ~/.claude/projects/. If present, read references/claude-code-fallback.md before continuing. That reference explains how to use the bundled parser at scripts/extract_exit_plan_mode_outcomes.py to extract denial reasons from Claude Code JSONL transcripts. Every phase below has a short note explaining what changes in fallback mode — the reference file has the details.

1. Neither available — Ask the user for their Plannotator plans directory or Claude Code projects directory. Do not guess.

Phase 0: Locate Plans & Check for Previous Reports

Use the mode chosen in Source Selection above.

Plannotator mode: Verify the plans directory contains *-denied.md files. If none exist, fall back to Claude Code mode before stopping.

Claude Code fallback mode: Run the bundled parser per the fallback reference to build the denial-reason dataset. Create /tmp/compound-planning/ if needed.

In either mode, proceed to Previous Report Detection below.

Previous Report Detection

After locating the plans directory, check for existing reports:

ls ${PLANNOTATOR_DATA_DIR:-~/.plannotator}/plans/compound-planning-report*.html

Reports follow a versioned naming scheme:

• First report: compound-planning-report.html
• Subsequent reports: compound-planning-report-v2.html, compound-planning-report-v3.html, etc.

If one or more reports exist, determine the latest one (highest version number). Get its filesystem modification date using stat (macOS: stat -f %Sm -t %Y-%m-%d, Linux: stat -c %y | cut -d' ' -f1). This is the cutoff date.

Present the user with a choice:

"I found a previous report (compound-planning-report-v{N}.html) last updated on {CUTOFF_DATE}. I can either: 1. Incremental — Only analyze files dated after {CUTOFF_DATE}, saving tokens and building on previous findings 2. Full — Re-analyze the entire archive from scratch Which would you prefer?"

Wait for the user's response before proceeding.

If incremental: Filter all subsequent phases to only process files with dates after the cutoff date. The new report version will note in its header narrative that it covers the period from {CUTOFF_DATE} to present, and reference the previous report for earlier findings. The inventory (Phase 1) should still count ALL files for overall stats, but clearly separate "new since last report" counts.

If full: Proceed normally with all files, but still use the next version number for the output filename.

If no previous report exists: Proceed normally. The output filename will be compound-planning-report.html (no version suffix for the first report).

Phase 1: Inventory

Count and report the dataset. Always count ALL files for overall stats, regardless of whether this is an incremental or full run:

- *-approved.md files (count)
- *-denied.md files (count)
- Date range (earliest to latest date found in filenames)
- Total days spanned
- Revision rate: denied / (approved + denied) — this is the "X% of plans
  revised before coding" stat used in dashboard section 1

Note: Ignore *.annotations.md files entirely. Denied files already contain the full plan text plus all reviewer feedback appended after a --- separator. Annotation files are redundant subsets of this content — reading both would double-count feedback.

If incremental mode: After the total counts, separately report the counts for files dated after the cutoff date only:

New since {CUTOFF_DATE}:
- *-denied.md files: X (of Y total)
- New date range: {CUTOFF_DATE} to {LATEST_DATE}
- New days spanned: N

If fewer than 3 new denied files exist since the cutoff, warn the user:

"Only {N} new denied plans since the last report. The incremental analysis may be thin. Would you like to proceed or switch to a full analysis?"

Also run wc -l across all *-approved.md files to get average lines per approved plan. This tells the user whether their plans are staying lightweight or bloating over time. You do not need to read approved plan contents — just their line counts. If possible, break this down by time period (e.g., monthly) to show whether plan size changed.

Dates appear in filenames in YYYY-MM-DD format, sometimes as a prefix (2026-01-07-name-approved.md) and sometimes embedded (name-2026-03-15-approved.md). Extract dates from all filenames.

Tell the user what you found and that you're beginning the extraction.

Claude Code fallback mode: The Plannotator inventory fields above do not apply. Follow the inventory instructions in references/claude-code-fallback.md instead — report the denial-reason dataset assembled by the parser.

Phase 2: Map — Parallel Extraction

This is the most time-intensive phase. You must read EVERY *-denied.md file in scope. Do not skip files. Do not summarize early.

In scope means: all denied files if running a full analysis, or only denied files dated after the cutoff date if running incrementally. In incremental mode, only process files whose embedded YYYY-MM-DD date is strictly after the cutoff.

Claude Code fallback mode: The parser output is the clean source dataset. Read the fallback reference for the extraction prompt and batching strategy specific to JSON part files. Do not go back to raw .jsonl logs unless the parser fails or the user asks for audit-level verification.

Important: Only read *-denied.md files. Do NOT read approved plans, annotation files, or diff files. Each denied file contains the full plan text followed by a --- separator and the reviewer's feedback — everything needed for analysis is in one file.

Batching Strategy

All extraction agents should use model: "haiku" — they're doing straightforward file reading and structured extraction, not reasoning. Haiku is faster and cheaper for this work.

The approach depends on dataset size:

Tiny datasets (≤ 10 total files): Read all files directly in the main agent — no need for sub-agents. Just read them sequentially and proceed to Phase 3.

Small datasets (11-30 files): Launch 2-3 parallel Haiku agents, splitting files roughly evenly.

Medium datasets (31-80 files): Launch 4-6 parallel Haiku agents (~10-15 files each). Split by file type and/or time period.

Large datasets (80+ files): Launch as many parallel Haiku agents as needed to keep each batch around 10-15 files. Split by the natural time boundaries in the data (months, quarters, or whatever groupings produce balanced batches). If one time period dominates (e.g., the most recent month has 3x the files), split that period into multiple batches.

Launch all extraction agents in parallel using the Agent tool with run_in_background: true and model: "haiku".

Output Files

Each extraction agent must write its results to a clean output file rather than relying on the agent task output (which contains interleaved JSONL framework