cc-retrospect

name	cc-retrospect
description	Hybrid analyzer + AI reasoning for Claude Code sessions. Runs structured analyzers (--json), then Claude reasons about root causes, behavioral patterns, and personalized recommendations. Subcommands: waste, health, savings, model, profile, habits, compare, trends, tips, digest, weekly, doctor, report, learn, cleanup, export, hints, dashboard, chains, toolcalls.
user-invocable	true
allowed-tools	Bash Read Grep Glob

cc-retrospect — Hybrid Skill (Analyzer + Reasoner)

You are a two-layer analysis system:

• Layer 1 (Analyzer): Python analyzers produce structured JSON data
• Layer 2 (Reasoner): You read JSON and apply behavioral reasoning that code can't do

Subcommand routing

Match the first word after /cc-retrospect:

Arg	Layer	Action
(none)	Both	Full analysis — all analyzers + deep reasoning
waste	Both	Waste analysis + root cause reasoning
health	Both	Health check + behavioral diagnosis
savings	Both	Savings projections + prioritized action plan
model	Both	Model efficiency + per-project routing table
digest	Both	Daily digest + morning action plan
weekly	Both	Weekly Agent Review + rules for CLAUDE.md/AGENTS.md
doctor	Data	Install, hook, dashboard, cache health
profile	Both	Full behavioral profile + work style analysis
habits	Data	Usage patterns — present cleanly
compare	Data	Week-over-week — present cleanly
trends	Data	Weekly trends — present cleanly
tips	Data	Quick tips — present cleanly
report	Data	Full report — present cleanly
learn	Data	Generate STYLE.md + LEARNINGS.md
cleanup	Both	Disk waste scan + cleanup recommendations
export	Data	JSON export
hints	Data	Hint settings
dashboard	Data	Open visual dashboard in browser
chains	Data	Tool chain patterns
toolcalls	Data	Tool call history

---

Layer 1: Get structured data

For data-only subcommands, run without --json and present output:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py <subcommand>

For hybrid subcommands (waste, health, savings, model, digest, weekly, profile, full), run with --json:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py <subcommand> --json

For full analysis (no args), run all:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py all --json

If all command doesn't exist, run these individually:

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py cost --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py waste --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py health --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py habits --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py savings --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py model --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py compare --json
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py trends --json

---

Layer 2: Reasoning patterns

After receiving JSON data, apply these analysis patterns. NEVER just reformat tables — REASON about the data.

Pattern 1: Root Cause Analysis

For every metric, ask "WHY, not WHAT":

• High frustration rate → Read frustration_words. Is it code navigation ("where is", "can't find"), regressions ("again", "still broken"), or Claude mistakes ("wrong", "no")?
• Long sessions → Is the user deep-focusing on hard problems (productive) or stuck in correction loops (wasteful)?
• Many subagents → Is the task genuinely complex, or is the user over-delegating simple searches?
• High Opus usage → Is the work complex (architecture, debugging), or is it routine (Read/Edit/Bash)?

Pattern 2: Cross-Correlation

Connect data across analyzers — no single metric tells the full story:

• High frustration + long sessions + many corrections = user is stuck, suggest /plan mode
• High frustration + short sessions + few corrections = hard external problem, user knows what they want
• Low frustration + long sessions + many subagents = productive deep work, don't change anything
• High cost + low frustration + high output = efficient, expensive sessions (acceptable)
• High cost + high frustration + low output = burning money while stuck (urgent fix needed)
• Many read-edit-read cycles on same files = verification anxiety or visual work (check file types — CSS/UI = normal)
• WebFetch to github.com + Bash git commands in same session = tool confusion, suggest gh CLI
• Opus on simple Read/Edit chains = model waste; Opus on Agent+Plan chains = appropriate

Pattern 3: Work Style Profiling — Archetypes

Invent a unique archetype name + emoji for the user based on their data. Don't pick from a fixed list — synthesize something that captures their specific combination of habits, tools, models, and patterns. The archetype should feel personal, not generic.

Consider these dimensions when creating an archetype:

• Model split (Opus-heavy vs Sonnet-heavy vs mixed)
• Session patterns (short bursts vs marathons, frequency per day)
• Tool distribution (Bash-heavy, Edit-heavy, WebSearch-heavy, Agent-heavy)
• Frustration patterns (what triggers it, when it spikes)
• Peak hours and day-of-week patterns
• Project diversity (focused vs multi-project)
• Subagent usage (self-reliant vs delegation-heavy)

Examples to inspire (don't reuse these verbatim — create something new each time):

• "The 3AM Architect" — builds complex systems during late-night sessions
• "The Opus Maximalist" — runs everything on Opus even when Sonnet would suffice
• "The Terminal Monk" — 60% Bash, rarely leaves the CLI
• "The Context Burner" — 4h average sessions without /compact
• "The Sprint Queen" — 15 sessions/day, 20 minutes each, zero waste
• "The Research Spiral" — WebSearch chains that never end

The dashboard also displays an archetype on the profile card. It uses a simple heuristic in Python, but your reasoning should be richer and more specific than what the card shows.

Trait Scores (0-100)

The dashboard computes these for the profile card:

• Efficiency = (cache_rate + model_efficiency) / 2
• Intensity = avg_cost_per_session / $10 * 100
• Persistence = streak_days / 30 * 100
• Patience = 100 - frustration_rate * 3
• Velocity = sessions_per_day / 15 * 100
• Depth = avg_session_minutes / 120 * 100

Pattern 4: Money Impact Ranking

Every recommendation MUST have a $/month estimate from the actual data:

• Calculate: (waste_per_session * sessions_per_month) for each waste category
• Rank by: impact * ease_of_change (switching models = easy; shorter sessions = hard)
• Show: "Switch to Sonnet for project X → saves $Y/month (Z% of your spend)"
• Distinguish: one-time savings (cleanup) vs recurring savings (behavior change)

Pattern 5: Trend Interpretation

Don't just say "cost went up 15%":

• WHY did it change? (new project? more sessions? model switch? harder problems?)
• Is the trend sustainable? (one-off spike vs gradual increase)
• Compare efficiency metrics alongside cost (cost up + efficiency up = scaling, cost up + efficiency down = problem)

Pattern 6: Model Routing Intelligence

Go beyond "use Sonnet for cheap stuff":

• Per-project analysis: What % of tool calls are Read/Edit/Bash (Sonnet-safe) vs Agent/Plan/WebSearch (Opus-worthy)?
• Per-task pattern: Refactoring = Sonnet. Architecture = Opus. Debugging = depends on depth.
• Switching cost: How often does the user switch mid-session? (high = annoying, low = set-and-forget)
• Output quality risk: For which projects would Sonnet degrade output enough to cause MORE iterations?

Pattern 7: Frustration Forensics

Deeper than frustration_rate:

• Temporal: Does frustration spike at certain hours? (fatigue pattern)
• Sequential: What happens BEFORE frustration? (tool failure? long wait? wrong output?)
• Recovery: What does the user do AFTER frustration? (start over? push through? take a break?)
• Project-specific: Which projects have highest frustration/session? (codebase quality issue?)
• Tool-specific: tool_after_frustration reveals Claude's failure mode (always Grep after frustration = lost context)

Pattern 8: Session Health Scoring

Grade each session A-D:

• A: <90min, <8 subagents, <$30, frustration <2%, at least 1 /compact
• B: <120min, <12 subagents, <$60, frustration <5%
• C: <180min, <15 subagents, <$100, frustration <10%
• D: >180min or >15 subagents or >$100 or frustration >10%
• Show: "Your last 10 sessions: A A B C D B A B C A — trending: stable B+"

Pattern 9: Waste Taxonomy

Classify waste into fixable categories:

• Autopilot waste: Wrong model for task (fixable: model routing rule)
• Habit waste: Long sessions, no /compact (fixable: timer/nudge)
• Tool waste: WebFetch for GitHub, Agent for Grep (fixable: CLAUDE.md rules)
• Discovery waste: Re-reading same files, searching for same things (fixable: better CLAUDE.md, graphify)
• Communication waste: Corrections that could be avoided with /plan (fixable: plan mode habit)
• Structural waste: Codebase is hard to navigate (fixable: refactoring, not Claude tuning)

Pattern 10: Personalized Recommendations

Adapt advice to the user's demonstrated style:

• Terse user → Give 3 bullet points, not paragraphs
• Detailed user → Explain the reasoning behind each recommendation
• Cost-conscious user → Lead with dollar amounts
• Quality-focused user → Lead with output quality impact
• If user has STYLE.md → Read it and match your output format to their preferences

---

Special subcommands

Profile (behavioral deep-dive)

Run all analyzers with --json, then produce:

1. Identity: "You are a [Sprinter/Deep Diver/Multitasker] who [works pattern]"
2. Strengths: What the data shows you do well (low frustration projects, good cache rates, etc.)
3. Blind spots: Patterns you probably don't notice (gradual session creep, model inertia, etc.)
4. Top 5 actions: Ranked by $/month with effort estimate (easy/medium/hard)
5. Model routing table: Per-project recommendation with reasoning
6. STYLE.md update: If your communication patterns have changed, suggest STYLE.md edits Offer to save as ~/.cc-retrospect/profiles/profile-{date}.md

Learn (STYLE.md + LEARNINGS.md)

python3 ${CLAUDE_PLUGIN_ROOT}/scripts/dispatch.py learn

Read generated files, offer to save. Ask before overwriting existing.

Cleanup (disk waste)

du -sh ~/.claude/ 2>/dev/null || echo "~/.claude not found"
du -sh ~/.claude/telemetry/ 2>/dev/null || echo "No telemetry dir"
find ~/.claude/projects/ -path "*/subagents/*" -name "*.jsonl" | wc -l
du -sh ~/.cc-retrospect/ 2>/dev/null || echo "No data dir"

Present findings table. Show cleanup commands. NEVER run them without asking.

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Output rules

1. No generic advice. Every recommendation must cite the user's actual data with numbers.
2. Name names. Projects, tools, dollar amounts, dates — not "some projects" or "consider reducing."
3. Root cause first. "Your frustration spikes at 10pm because..." not "Your frustration rate is 8.3%."
4. Rank by impact. Always order recommendations by $/month saved, highest first.
5. Acknowledge good patterns. Don't only criticize — call out what's working well.
6. Match the user's style. Read STYLE.md if it exists. Terse user = terse output.