Before dispatching a single token to Claude Code, CC Commander runs four analysis modules that together decide the right model, budget, turn limit, and skills for your task. You never touch a config flag — the Intelligence Layer handles every dispatch automatically based on what your task actually needs.
The Intelligence Layer runs automatically — no configuration required.
The four modules
1. Weighted complexity scoring
Every task you describe is scored from 0 to 100 using 47 keyword signals, word count, and fuzzy regex matching. The score maps directly to a model, turn limit, and dollar budget.
"fix typo" → score 0 → 10 turns, $1 budget, Haiku
"add dark mode" → score 25 → 20 turns, $3 budget, Sonnet
"refactor auth module" → score 60 → 35 turns, $6 budget, Sonnet
"build SaaS platform" → score 100 → 50 turns, $10 budget, Opus
| Signal type | Example | Effect |
|---|
| Trivial reducers | fix typo, rename, one line | −15 to −30 pts |
| Moderate adders | add feature, refactor, integrate | +10 to +20 pts |
| High-complexity adders | build saas, from scratch, multi-tenant | +25 to +40 pts |
| Long description (25–50 words) | Detailed task prompt | +10 pts |
| Long description (50+ words) | Very detailed prompt | +20 pts |
| File scope bonus | Matches many project files | 0 to +20 pts |
| Score range | Label | Turns | Budget | Effort |
|---|
| 0–25 | Trivial | 10 | $1 | low |
| 26–50 | Simple | 20 | $3 | low |
| 51–75 | Moderate | 35 | $6 | medium |
| 76–100 | Complex | 50 | $10 | high |
--model, --budget, or --max-turns manually.
2. Stack detection
Before every dispatch, CCC reads your project directory and detects your technology stack automatically. It parses package.json, Dockerfile, go.mod, and requirements.txt. It also reads your current git branch and the themes of your last five commits, and detects monorepo setups (workspaces, lerna, turbo, nx).
From those files it recognizes: nextjs, react, vue, docker, python, rust, go, github-actions, orm, billing, and testing frameworks. No configuration needed — CCC reads what is already in your project.
3. Skill recommendations
Once the stack and task are known, CCC’s skill recommender combines three signals to surface the most relevant skills:
| Signal | Weight | What it does |
|---|
| Stack match | 10 pts | Next.js project → nextjs-app-router ranks first |
| Keyword match | 2 pts per hit | ”auth” task → auth, jwt, better-auth bubble up |
| Usage history | Boost multiplier | Skills that succeeded for you rank higher over time |
4. Knowledge compounding
Every completed session extracts a lesson — keywords, category, tech stack, error patterns, success patterns — and stores it in ~/.claude/commander/knowledge/. The next time you dispatch a similar task, those lessons are injected into the session prompt automatically.
Relevance is weighted by time:
| Age | Multiplier |
|---|
| Less than 7 days | 2× |
| 7–30 days | 1.5× |
| Older than 30 days | 1× |
Smart dispatch in action
Here is what CCC’s analysis output looks like before a session starts:
Task: "Add authentication with JWT and OAuth"
Intelligence Analysis:
├─ Complexity Score: 72/100 (complex)
├─ Keyword signals: +15 (auth) +15 (implement) +20 (production)
├─ Word count: +10 (detailed description)
├─ Stack detected: nextjs, react, tailwind
└─ Related lessons: 2 found (JWT auth succeeded, OAuth2 pattern)
Auto-configured dispatch:
Model: opus | Turns: 35 | Budget: $6
Skills: nextjs-app-router, ccc-saas, auth-patterns
Knowledge: 2 past lessons injected
Dispatching... ████████████████████░░░░ 78%
How it compounds over sessions
| Session | What changed |
|---|
| 1 | Dispatches based on complexity score alone |
| 5 | Knows your stack, recommends proven skills |
| 20 | Has learned your patterns — feels like a PM who knows your codebase |
Smart retry
The Intelligence Layer also handles failures automatically so you do not need to babysit long sessions:
- Rate limit hit — waits 60 seconds, then retries automatically
- Context overflow — reduces turns to 60% of original, then retries
- Budget exceeded — surfaces a clear error with next steps
You can view your accumulated knowledge base stats at any time by running ccc --stats.