home / skills / openclaw / skills / agentic-coding
This skill helps you ship production-ready AI-assisted code by enforcing contract-first work, proof of fix, and reviewer-ready handoffs.
npx playbooks add skill openclaw/skills --skill agentic-codingReview the files below or copy the command above to add this skill to your agents.
---
name: Agentic Coding
slug: agentic-coding
version: 1.0.0
homepage: https://clawic.com/skills/agentic-coding
description: Ship production code with AI agents through acceptance contracts, micro diffs, red green loops, and deterministic handoff checkpoints.
changelog: Initial release with PACT protocol, contract templates, and handoff-first delivery workflow.
metadata: {"clawdbot":{"emoji":"AI","requires":{"bins":["git"]},"os":["linux","darwin","win32"]}}
---
## Setup
If `~/agentic-coding/` does not exist or is empty, read `setup.md`, ask a concise kickoff question, and keep any persistence explicitly opt-in.
## Positioning
This skill is intentionally different from `agentic-engineering` and `vibe-coding`:
- `agentic-engineering` focuses on multi-agent operating patterns and team throughput.
- `vibe-coding` focuses on prompt-led exploration and fast idea shipping.
- `agentic-coding` focuses on contract-first implementation, proof of fix, and reviewer-ready handoff.
## When to Use
User needs merge-ready code from an AI agent with explicit quality gates. Use for production features, risky refactors, bug fixes with reproducible failures, and Xcode-centered work such as Swift feature delivery, iOS/macOS regressions, and release-branch hotfixes.
## Architecture
Memory lives in `~/agentic-coding/`. See `memory-template.md` for setup.
```text
~/agentic-coding/
|- memory.md # Persistent preferences and operating mode
|- contracts.md # Accepted task contracts and non-goals
|- evidence.md # Test evidence and verification snapshots
`- handoffs.md # Delivery notes and rollback hints
```
## Quick Reference
Load these files on demand to keep context focused and execution fast.
| Topic | File |
|-------|------|
| Setup process | `setup.md` |
| Memory template | `memory-template.md` |
| PACT loop | `protocol.md` |
| Contract prompts | `prompt-contracts.md` |
| Merge handoff checklist | `handoff.md` |
## Core Rules
### 1. Lock a Contract Before Writing Code
Start every task with a compact contract:
- Objective: exact outcome in one sentence
- Acceptance: checks that prove success
- Non-goals: what must stay untouched
- Constraints: stack, style, limits, deadlines
No contract, no code.
### 2. Run the PACT Loop
Use the same execution loop every time:
1. **P**roblem framing: restate objective and assumptions
2. **A**cceptance design: define checks before edits
3. **C**hange set: produce the smallest useful diff
4. **T**race and test: show evidence and residual risk
This skill is execution discipline, not brainstorming.
For Xcode workflows, tie acceptance to a concrete target, simulator/device, and test command before editing.
### 3. Keep Diffs Surgical
One user objective maps to one focused change set:
- Prefer file-local edits over broad rewrites
- Separate behavior change from style cleanup
- Avoid hidden side effects outside declared scope
If scope grows, split into a second contract.
### 4. Prove Failure Then Prove Fix
For bugs and regressions:
- Capture the failing condition first (test, log, or reproduction)
- Apply minimal fix
- Re-run the same check to prove resolution
Never claim fixed without before and after evidence.
### 5. Deliver Handoff-Grade Output
End each cycle with a delivery packet:
- What changed and why
- Files touched and blast radius
- Validation run and results
- Known risks and rollback path
If handoff is unclear, the task is not finished.
### 6. Escalate With a Structured Fallback
When blocked after two failed attempts:
- Stop editing
- State what was tried
- Propose two grounded alternatives
- Request a decision with tradeoffs
Do not keep guessing in loops.
## Common Traps
- Starting implementation without acceptance checks -> endless iteration and unclear done state.
- Asking the agent for full rewrites -> noisy diffs and avoidable regressions.
- Mixing feature work with architecture overhaul -> weak reviewability and hard rollback.
- Reporting success without reproducible evidence -> false confidence in production.
- Treating AI output as final draft -> quality debt moved to code review.
## Security & Privacy
**Data that leaves your machine:**
- None from this skill itself
**Data that stays local:**
- Contracts, evidence notes, and handoff summaries in `~/agentic-coding/`
**This skill does NOT:**
- Trigger undeclared network requests
- Access files outside its own memory path
- Write to global or platform memory stores
- Auto-approve risky code without explicit evidence
## Scope
This skill ONLY:
- Improves execution quality of AI-assisted coding
- Enforces contract driven implementation and verification
- Produces clear handoff packets for reviewers
This skill NEVER:
- Replaces security review for high risk domains
- Encourages blind trust in generated code
- Overrides project specific contribution rules
## Related Skills
Install with `clawhub install <slug>` if user confirms:
- `agentic-engineering` - Multi-agent collaboration and operating patterns.
- `coding` - General coding support across stacks and tasks.
- `code` - Broad code authoring and editing assistance.
- `copilot` - Companion style IDE assistance patterns.
- `delegate` - Structured task delegation to autonomous agents.
## Feedback
- If useful: `clawhub star agentic-coding`
- Stay updated: `clawhub sync`
This skill helps you ship production-ready code using AI agents by enforcing contract-first implementation, minimal diffs, and deterministic handoff checkpoints. It focuses on measurable acceptance criteria, proof-of-fix loops, and reviewer-ready delivery notes to reduce risk in real changes. Memory and artifacts are kept locally in a dedicated workspace to preserve evidence and handoffs.
Before any edit, the agent locks a compact contract that states the objective, acceptance checks, non-goals, and constraints. It follows a strict PACT loop: frame the problem, design acceptance checks, produce a surgical change set, and trace/test the result with before/after evidence. Each cycle ends with a handoff packet describing changes, validation results, blast radius, and rollback hints.
Where does the skill store its state and artifacts?
All persistent artifacts (contracts, evidence, handoffs, preferences) are stored locally in a dedicated workspace so nothing leaves your machine.
What if scope expands during work?
Split the expanded work into a new contract and keep each change set focused and reviewable; do not bloat the current diff.