coding-strategy

name: coding-strategy description: > Consult before ANY coding task. Chooses the optimal coding agent strategy based on task complexity, available free tokens, parallelism potential, and provider quotas. Covers: OpenClaw sub-agents, Codex CLI, Claude Flow swarms/hive-minds, Jules (Google), GitHub Copilot coding models, Augment Code, Kimi K2.5, and OpenAI gpt-5.3-codex. All agents must read this before writing code.

Coding Strategy Skill

Read this before ANY coding task. Even small ones. The goal: maximize free tokens, spread load across providers, and pick the right tool for the job.

Provider Inventory

Tier 1 — Free / Subscription-Included (use first)

Tier 2 — Pay-per-token (use intentionally)

Tier 3 — Orchestration (for complex multi-file work)

Decision Matrix

Step 1: Classify the task

Step 2: Pick the strategy

Step 3: Maximize free tokens

Always exhaust free/subscription tiers before pay-per-token:

1. GitHub Copilot models — included in subscription, use freely
2. Codex CLI — $20/mo flat, use --full-auto or --yolo liberally
3. Kimi K2.5 — generous free tier, great for reasoning
4. Jules — included in Google Max plan, async background work
5. Augment — free tier tokens available
6. THEN fall back to Anthropic Sonnet/Opus or OpenAI API

Provider-Specific Instructions

Codex CLI (OpenAI)

# One-shot task (PTY required!)
exec pty:true workdir:/path/to/repo command:"codex exec --full-auto 'Your task description'"

# Background for longer work
exec pty:true workdir:/path/to/repo background:true command:"codex exec --full-auto 'Your task. When done, run: openclaw gateway wake --text \"Done: brief summary\" --mode now'"

# YOLO mode (no sandbox, no approvals — fastest)
exec pty:true workdir:/path/to/repo command:"codex exec --yolo 'Your task'"

# Code review
exec pty:true workdir:/path/to/repo command:"codex review --base origin/main"

Key: Codex needs a git repo. Model: gpt-5.3-codex. Config: ~/.codex/config.toml.

Claude Flow (Multi-Agent Orchestration)

# Initialize in project (one-time)
exec workdir:/path/to/repo command:"claude-flow init"

# Swarm — hierarchical 15-agent mesh for large features
exec pty:true workdir:/path/to/repo background:true command:"claude-flow swarm start -o 'Build the REST API with auth, tests, and docs' -s development"

# Hive-Mind — consensus-based for architecture decisions
exec pty:true workdir:/path/to/repo background:true command:"claude-flow hive-mind init -t hierarchical-mesh"
# Then spawn workers:
exec pty:true command:"claude-flow hive-mind spawn --claude"
# Submit task:
exec command:"claude-flow hive-mind task 'Refactor auth module for OAuth2 support'"

# Check status
exec command:"claude-flow swarm status"
exec command:"claude-flow hive-mind status"

Key: Claude Flow has hooks in .claude-flow/ that enable self-learning. Projects with heavy Claude Flow use should have claude-flow init run first.

Jules (Google AI Coding Agent)

Jules works asynchronously — submit tasks via the Google AI Studio / Jules interface, it creates PRs.

# Get the API key
JULES_KEY=$(op read "op://DeLoSecrets/Jules/API Key")

# Jules is primarily browser-based at jules.google.com
# For API access, check current docs — the API surface is evolving
# Key pattern: submit task → Jules works in background → creates PR → you review

# For now, use browser automation or the web interface:
# 1. Navigate to jules.google.com
# 2. Connect repo
# 3. Submit task description
# 4. Jules creates a branch + PR asynchronously

API Key: op://DeLoSecrets/Jules/API Key Best for: Async background tasks where you don't need the result immediately. Submit the task, let Jules work, pull the PR when ready. Great for fire-and-forget work while other agents handle synchronous tasks.

Augment Code (auggie CLI)

# One-shot task (print mode)
exec pty:true workdir:/path/to/repo command:"auggie -p 'Your task description'"

# Interactive mode
exec pty:true workdir:/path/to/repo background:true command:"auggie 'Your task description'"

# Quiet mode (only final output)
exec pty:true workdir:/path/to/repo command:"auggie -q 'Your task'"

# With image input
exec pty:true workdir:/path/to/repo command:"auggie --image screenshot.png 'Implement this UI'"

# Ask mode (read-only, no edits — good for analysis)
exec pty:true workdir:/path/to/repo command:"auggie --ask 'Explain the auth flow in this codebase'"

Binary: ~/.bun/bin/auggie Best for: Code generation, refactoring, codebase-aware tasks. Has workspace indexing for deep context. Note: First run in a new workspace triggers an indexing step. Use --print from workspace root to index.

OpenClaw Sub-Agents (Built-in)

# Spawn a coding sub-agent on a free provider
sessions_spawn(
    task="Implement the user settings page with React and TypeScript",
    model="github-copilot/claude-sonnet-4.5",  # FREE via subscription
    label="settings-page"
)

# Spawn on Kimi for reasoning-heavy work
sessions_spawn(
    task="Architect the event sourcing system for order processing",
    model="kimi-coding/k2p5",  # Free tier
    label="event-sourcing-arch"
)

# Parallel workers — different providers, different tasks
sessions_spawn(task="Write unit tests for auth module", model="github-copilot/gpt-5.2-codex", label="auth-tests")
sessions_spawn(task="Write integration tests for API", model="kimi-coding/k2p5", label="api-tests")
sessions_spawn(task="Update API documentation", model="github-copilot/gemini-3-flash-preview", label="api-docs")

GitHub Copilot Models (via OpenClaw)

Available models through github-copilot/ prefix — all included in subscription:

• github-copilot/claude-sonnet-4.5 — best all-rounder
• github-copilot/claude-opus-4.6 — heavy reasoning
• github-copilot/claude-haiku-4.5 — fast, cheap tasks
• github-copilot/gpt-5.2-codex — strong coding
• github-copilot/gpt-4o — general purpose
• github-copilot/gemini-3-pro-preview — large context
• github-copilot/gemini-3-flash-preview — fast, large context
• github-copilot/grok-code-fast-1 — fast coding

Parallelism Patterns

Pattern 1: Fan-Out (independent tasks)

Task: "Build user dashboard"
├── Sub-agent 1 (copilot/sonnet): "Build UserProfile component"
├── Sub-agent 2 (kimi-k2.5): "Build ActivityFeed component"
├── Sub-agent 3 (codex CLI): "Build SettingsPanel component"
└── Sub-agent 4 (copilot/gemini): "Write tests for all components"

Pattern 2: Pipeline (sequential dependencies)

Step 1 (kimi-k2.5): "Design the API schema and types"
  → Step 2 (codex --full-auto): "Implement the API endpoints"
    → Step 3 (copilot/sonnet): "Write integration tests"
      → Step 4 (copilot/haiku): "Generate API documentation"

Pattern 3: Claude Flow Swarm (complex features)

claude-flow swarm start -o "Build complete auth system: OAuth2, JWT, RBAC, tests, docs" -s development
# Swarm auto-coordinates 15 agents across the feature

Pattern 4: Review Army (batch PR reviews)

# Fetch all PR refs
git fetch origin '+refs/pull/*/head:refs/remotes/origin/pr/*'
# Deploy parallel Codex reviewers
exec pty:true background:true command:"codex exec 'Review PR #86. git diff origin/main...origin/pr/86'"
exec pty:true background:true command:"codex exec 'Review PR #87. git diff origin/main...origin/pr/87'"

Rules

1. Always check this skill before coding — even for small tasks
2. Free tokens first — exhaust GitHub Copilot, Codex CLI, Kimi, Jules, Augment before paying
3. PTY required for Codex CLI — pty:true always
4. Codex needs a git repo — won't run outside one
5. Never run coding agents in ~/.openclaw/ — they'll read soul docs and get weird
6. Notify on completion — append openclaw gateway wake to long-running prompts
7. Track what's running — use process action:list and sessions_list to monitor
8. Spread the load — don't burn one provider when others have free tokens
9. Claude Flow for 5+ file changes — swarms are more efficient than manual coordination
10. Jules for async — submit and forget, pull PR when ready