radar-analyze

name	radar-analyze
description	Analyze your Claude Code and Cowork session history to surface actionable workflow insights. Diagnoses failures, identifies automation opportunities, aligns time allocation with goals, and flags repeated knowledge worth saving.
argument-hint	["--days N"]

Radar Analyze

Analyze recent Claude Code and Cowork sessions to generate actionable workflow insights.

Uses the bundled workflow-analyzer for session parsing and enrichment. Claude does the reasoning.

This skill is self-contained — it does not depend on or use the Open Brain plugin, ai-brain MCP, or any external memory service. All data comes from local files.

Arguments

• $ARGUMENTS — Optional:
  • --days N — How many days of history to analyze (default: 7)

Parse the days value from $ARGUMENTS if provided. Default to 7.

Workflow

Step 1: Parse & Enrich Session Data

Run the workflow-analyzer CLI to parse and enrich sessions from all configured sources (Claude Code + Cowork):

# Bundled binary (preferred)
node "${CLAUDE_PLUGIN_ROOT}/bin/workflow-analyzer/dist/cli.js" parse --since ${DAYS} --output /tmp/workflow-analyzer-parsed.json

# Fallback if bin/ not available:
# npx @flippyhead/workflow-analyzer@latest parse --since ${DAYS} --output /tmp/workflow-analyzer-parsed.json

If the command fails, surface the error output directly to the user. Do not swallow the error or exit silently.

Read the output file. It contains { sessions: [...], sessionGroups: [...] }.

If sessions is empty, report "No activity in the last N days" and stop.

Otherwise, note the summary: how many sessions, which sources (claude-code, cowork), how many session groups.

Step 2: Check Previous Insights

Read ~/.claude/radar/catalogue.json and look at the insights array. Note:

• Insights with status: "new" — unresolved insights to avoid duplicating
• Insights with status: "dismissed" — things the user doesn't want to see again

Build a set of existing insight IDs and observation text to use for deduplication. If the catalogue doesn't exist, skip this step.

Step 3: Check for User Goals

Read ~/.claude/CLAUDE.md if it exists. Look for stated goals, priorities, or focus areas. These are used by the Decision Support analysis below.

If no goals are found, skip goal-based analysis.

Step 4: Analyze

With all data gathered, analyze the parsed sessions and produce insights across four modules. For each insight, you MUST provide a concrete action — never just describe a problem without telling the user what to do about it.

Module A — Root Cause Diagnosis:

Look at tool failures in the session data. For each tool with a notable failure rate:

• Read the actual error messages (not just counts)
• Diagnose the root cause: is it an auth issue, config problem, API bug, user error, or transient?
• If fixable: provide the specific command or config change to fix it (action type: run or action type: install)
• If not fixable: acknowledge it so the user stops worrying (action type: acknowledge)
• Skip tools with very low failure counts (<3 calls) or very low failure rates (<20%)

Module B — Direct Automation:

Look for patterns that could be automated:

• Permission confirmations: Count short messages like "yes", "y", "a", "ok", "sure" that appear to be tool permission confirmations. If a specific tool triggers many confirmations, suggest an allowedTools config entry (action type: install with settings.json patch)
• Repeated prompts: Look at user messages across sessions for frequently typed prompts. If a prompt appears 5+ times, suggest creating a skill or alias (action type: install with skill content)
• Repeated tool sequences: Look for the same sequence of 3+ tool calls appearing across sessions. Suggest automation if a pattern repeats 5+ times.

Module C — Decision Support:

Using the session groups and any user goals from Step 3:

• Calculate time allocation by project/topic (sessions and minutes)
• Compare against stated goals — flag misalignments
• Note cross-platform patterns (e.g., "researched X in Cowork but never implemented in Claude Code")
• If a project is consuming disproportionate time without being in the goals, surface a decision (action type: decide)

Module D — Knowledge Nudges:

Look for repeated topics across sessions:

• Find user prompts that ask about the same thing in multiple sessions (sign the answer should be saved)
• Detect when the user provides the same context/background repeatedly at session start (should be in CLAUDE.md or memory)
• For repeated topics, generate a consolidated summary of what to save (action type: save)

Insight Format

For each insight, produce:

{
  "module": "root-cause | direct-automation | decision-support | knowledge-nudges",
  "severity": "alert | action | suggestion | info",
  "title": "One-line summary",
  "observation": "What the data shows (with specific numbers)",
  "diagnosis": "Why this is happening (optional)",
  "action": {
    "type": "install | run | save | review | decide | acknowledge"
  },
  "evidence": [{"metric": "specific numbers"}],
  "effort": "low | medium | high",
  "impact": "low | medium | high",
  "confidence": 0.0-1.0,
  "deduplicationKey": "unique-key-for-this-insight"
}

Action types:

• install: { type: "install", artifact: "filename", content: "file content to install" }
• run: { type: "run", command: "command to run", explanation: "why" }
• save: { type: "save", content: "what to save", destination: "CLAUDE.md or project memory" }
• review: { type: "review", summary: "what to look at", links: [] }
• decide: { type: "decide", question: "decision to make", options: ["option 1", "option 2"] }
• acknowledge: { type: "acknowledge", message: "FYI only, no action needed" }

Aim for 5-10 total insights. Prioritize high-impact/low-effort actions. Skip insights that duplicate existing insights from Step 2.

Step 5: Save Insights

1. Write insights to a temp file and use the CLI to publish as a markdown report:

# Bundled binary (preferred)
node "${CLAUDE_PLUGIN_ROOT}/bin/workflow-analyzer/dist/cli.js" publish --insights /tmp/workflow-analyzer-insights.json

# Fallback if bin/ not available:
# npx @flippyhead/workflow-analyzer@latest publish --insights /tmp/workflow-analyzer-insights.json

The insights JSON file should contain:

{
  "insights": [...],
  "metadata": {
    "period": { "since": "ISO date", "until": "ISO date" },
    "sessionCount": N,
    "sources": ["claude-code", "cowork"],
    "modulesRun": ["root-cause", "direct-automation", "decision-support", "knowledge-nudges"]
  }
}

Write this file before running the publish command.

2. Append insights to ~/.claude/radar/catalogue.json's insights array. If the file or ~/.claude/radar/ directory doesn't exist:

   • First check for legacy files at ~/.claude/radar-catalogue.json or ~/.claude/scout-catalogue.json. If found, migrate using the same transformation as radar-scan Step 1 (see below).
   • If no legacy file exists, create the directory and initialize with: { "version": "1.0", "updatedAt": null, "items": [], "insights": [] }.

   Convert each insight to the catalogue insight schema:

{
  "id": "analyze-<deduplicationKey>",
  "type": "pattern",
  "observation": "<insight.observation>",
  "recommendation": "<describe the action>",
  "evidence": ["<insight.evidence metrics>"],
  "relatedItems": [],
  "createdAt": "<ISO date>",
  "status": "new"
}

Step 6: Summary

Output a brief summary:

• How many insights were generated, by module
• The report period and session count (including Cowork if any)
• Top 2-3 "quick win" recommendations (highest impact, lowest effort)