token-coach

What this skill does

# Token Coach: Plan Token-Efficient Before You Build

Interactive coaching for Claude Code or Codex 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.

---

## Phase 0: Initialize

1. **Resolve runtime and measure.py path** (same as token-optimizer):
```bash
RUNTIME="${TOKEN_OPTIMIZER_RUNTIME:-}"
if [ -z "$RUNTIME" ]; then
  if [ -n "$CLAUDE_PLUGIN_ROOT" ] || [ -n "$CLAUDE_PLUGIN_DATA" ]; then
    RUNTIME="claude"
  elif [ -n "$CODEX_HOME" ] || [ -d "$HOME/.codex" ]; then
    RUNTIME="codex"
  else
    RUNTIME="claude"
  fi
fi

MEASURE_PY=""
for f in "$HOME/.codex/skills/token-optimizer/scripts/measure.py" \
         "$HOME/.codex/plugins/cache"/*/token-optimizer/*/skills/token-optimizer/scripts/measure.py \
         "$HOME/.claude/skills/token-optimizer/scripts/measure.py" \
         "$HOME/.claude/plugins/cache"/*/token-optimizer/*/skills/token-optimizer/scripts/measure.py \
         "$PWD/skills/token-optimizer/scripts/measure.py"; do
  [ -f "$f" ] && MEASURE_PY="$f" && break
done
[ -z "$MEASURE_PY" ] || [ ! -f "$MEASURE_PY" ] && { echo "[Error] measure.py not found. Is Token Optimizer installed?"; exit 1; }
export TOKEN_OPTIMIZER_RUNTIME="$RUNTIME"
```

2. **Collect coaching data**:
```bash
python3 "$MEASURE_PY" coach --json
```
Parse the JSON output. This gives you: snapshot (current measurements), detected patterns, coaching questions, focus suggestions, and **history** (trend data from past sessions).

The `history` key contains (when trends.db has enough data):
- `quality_recent_avg` / `quality_prior_avg` - 7-day vs older quality scores
- `duration_recent_avg` / `duration_prior_avg` - session length trends (minutes)
- `cache_hit_recent_avg` / `cache_hit_prior_avg` - prompt cache hit rate trends
- `grade_d_pct_recent` / `grade_distribution` - recent grade breakdown
- `total_cost_usd` / `cost_per_session_usd` / `sessions_in_period` - spend summary
- `quality_short_sessions` / `quality_long_sessions` / `optimal_session_hint` - duration-quality correlation
- `compression_measured_saved` / `compression_opportunity_tokens` - compression gap
- `multi_model_session_pct` - percentage of recent sessions that switched models mid-session

Historical patterns also appear in the `patterns_bad` array (e.g. "Quality Declining", "Session Duration Creep", "Cache Hit Rate Dropping", "Cache Hit Rate Dropping (Model Switches)", "Frequent Model Switching", "High Cost Per Session", "Compression Opportunity Gap").

3. **Check context quality** (v2.0):
```bash
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.

4. **For Codex, check setup readiness**:
```bash
if [ "$RUNTIME" = "codex" ]; then
  python3 "$MEASURE_PY" codex-doctor --project "$PWD" --json 2>/dev/null
fi
```
Use this to tell the user whether balanced hooks, compact prompt guidance, dashboard refresh, and status-line support are installed.

## 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:
```bash
COACH_DIR=""
if [ -d "$HOME/.codex/skills/token-coach" ]; then
  COACH_DIR="$HOME/.codex/skills/token-coach"
elif [ -d "$HOME/.codex/skills/token-optimizer/../token-coach" ]; then
  COACH_DIR="$HOME/.codex/skills/token-optimizer/../token-coach"
elif [ -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/.codex/plugins/cache" "$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. If `history` data is available, weave in trend insights naturally:
   - Quality trending down? Lead with that, it's more urgent than a static snapshot
   - Cost data? Ground advice in dollars ("At $X.XX/session across Y sessions, routing alone could save $Z/month")
   - Duration-quality correlation? "Your short sessions score X vs Y for long ones" is a concrete, actionable insight
   - Grade distribution? "N% of your sessions scored D" hits harder than an abstract score
   - Model switching? If multi_model_session_pct is high, explain: switching models mid-session invalidates the prompt cache. Set model at session start, not mid-conversation. Subagent routing to cheaper models is fine (separate context)
   - Don't dump all history data at once. Pick the 1-2 most relevant trends for their intake choice
4. Reference their actual numbers ("You have 47 skills costing ~4,700 tokens at startup")
5. Ask a follow-up question. Don't dump everything at once.
6. For agentic systems (option c): walk through their architecture step by step
7. Use the coaching scripts for structure, but keep it natural

For Codex specifically, translate all advice to native Codex concepts:
- `AGENTS.md` instead of `CLAUDE.md`
- Codex memories instead of `MEMORY.md`
- balanced Codex hooks instead of Claude hooks
- Intelligence levels (Low/Medium/High/Extra High) and model selection (GPT-5.5, GPT-5.4, GPT-5.4-Mini, GPT-5.3-Codex, GPT-5.2) instead of Opus/Sonnet/Haiku routing
- Reasoning effort settings instead of model-per-agent routing
- compact prompt guidance instead of PreCompact/PostCompact lifecycle hooks
- Never reference Claude-specific concepts (Opus, Sonnet, Haiku, CLAUDE.md) when coaching a Codex user

**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 in Claude Code: include "Set up Smart Compaction" as a recommended action (`python3 $MEASURE_PY setup-smart-compact`)
4. If quality score < 70 in Codex: include "Install balanced Codex hooks and compact prompt guidance" (`TOKEN_OPTIMIZER_RUNTIME=codex python3 $MEASURE_PY codex-install --project .`)
5. If quality scor