token-coach

Token Coach: Plan Token-Efficient Before You Build

Interactive coaching for Claude Code architecture decisions. Analyzes your setup, identifies patterns (good and bad), and gives personalized advice with real numbers.

Use when: Building something new, existing setup feels slow, designing multi-agent systems, or want a quick health check.

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Phase 0: Initialize

1. Resolve measure.py path (same as token-optimizer):

MEASURE_PY=""
if [ -f "$HOME/.claude/skills/token-optimizer/scripts/measure.py" ]; then
  MEASURE_PY="$HOME/.claude/skills/token-optimizer/scripts/measure.py"
else
  MEASURE_PY="$(find "$HOME/.claude/plugins/cache" -path "*/token-optimizer/scripts/measure.py" 2>/dev/null | head -1)"
fi
[ -z "$MEASURE_PY" ] || [ ! -f "$MEASURE_PY" ] && { echo "[Error] measure.py not found. Is Token Optimizer installed?"; exit 1; }

2. Collect coaching data:

python3 $MEASURE_PY coach --json

Parse the JSON output. This gives you: snapshot (current measurements), detected patterns, coaching questions, and focus suggestions.

3. Check context quality (v2.0):

python3 $MEASURE_PY quality current --json 2>/dev/null

If available, parse the quality score and issues. This enriches coaching with session-level insights (not just setup overhead). If the command fails (pre-v2.0 install), skip gracefully.

Phase 1: Intake

Ask ONE question:

What's your goal today? a) Building something new, want it token-efficient from the start b) Existing project feels sluggish / context fills too fast c) Designing a multi-agent system, want architecture advice d) Quick health check with actionable tips

Wait for the answer. Don't dump info before they choose.

Phase 2: Load Context (based on intake)

Resolve the token-coach skill directory:

COACH_DIR=""
if [ -d "$HOME/.claude/skills/token-coach" ]; then
  COACH_DIR="$HOME/.claude/skills/token-coach"
elif [ -d "$HOME/.claude/skills/token-optimizer/../token-coach" ]; then
  COACH_DIR="$HOME/.claude/skills/token-optimizer/../token-coach"
else
  COACH_DIR="$(find "$HOME/.claude/plugins/cache" -path "*/token-coach" -type d 2>/dev/null | head -1)"
fi

Load references based on intake choice:

• Option a or b: Read $COACH_DIR/references/coach-patterns.md + $COACH_DIR/references/quick-reference.md
• Option c: Read $COACH_DIR/references/agentic-systems.md + $COACH_DIR/references/quick-reference.md
• Option d: Read $COACH_DIR/references/quick-reference.md only (fast path)

Read the matching example from $COACH_DIR/examples/ as a few-shot template:

• Option a: coaching-session-new-project.md
• Option b: coaching-session-heavy-setup.md
• Option c: coaching-session-agentic.md
• Option d: Skip example (keep it fast)

Read $COACH_DIR/references/coaching-scripts.md for conversation structure.

Phase 3: Coach (conversation, not report)

This is a CONVERSATION. Not a wall of text.

1. Lead with the 1-2 most impactful findings from the coaching data
2. If quality data is available and score < 70, lead with that instead: "Your current session quality is [X]/100. [Top issue] is eating [Y tokens]."
3. Reference their actual numbers ("You have 47 skills costing ~4,700 tokens at startup")
4. Ask a follow-up question. Don't dump everything at once.
5. For agentic systems (option c): walk through their architecture step by step
6. Use the coaching scripts for structure, but keep it natural

Tone: Knowledgeable friend, not corporate consultant. Be direct about what matters and why. Use real numbers from their data.

Anti-patterns to call out: Reference the anti-patterns from coach-patterns.md. Name them ("You've got the 50-Skill Trap going on").

Continue the conversation for 2-4 exchanges. Let the user ask questions. Adjust advice based on what they tell you about their workflow.

Phase 4: Action Plan

After the conversation, generate a prioritized action plan:

1. Summarize 3-5 concrete actions, ordered by impact
2. Include estimated token savings for each action (use the numbers from quick-reference.md)
3. If quality score < 70: include "Set up Smart Compaction" as a recommended action (python3 $MEASURE_PY setup-smart-compact)
4. If quality score < 50: recommend immediate /compact or /clear before continuing
5. Flag which actions are quick wins vs deeper changes
6. Offer to run /token-optimizer for the full audit + implementation if they want to go beyond coaching

Format: Keep it scannable. Numbered list with bold action names, one-line description, estimated savings.

Phase 5: Dashboard (optional)

If measure.py generated a coach dashboard tab, mention it: "Your Token Health Score and pattern analysis are in the dashboard. Run python3 $MEASURE_PY dashboard to see it."