# Send Agentic Coding to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "agentic-coding",
    "name": "Agentic Coding",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/ivangdavila/agentic-coding",
    "canonicalUrl": "https://clawhub.ai/ivangdavila/agentic-coding",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/agentic-coding",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=agentic-coding",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md",
      "handoff.md",
      "memory-template.md",
      "prompt-contracts.md",
      "protocol.md",
      "setup.md"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/agentic-coding"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/agentic-coding",
    "downloadUrl": "https://openagent3.xyz/downloads/agentic-coding",
    "agentUrl": "https://openagent3.xyz/skills/agentic-coding/agent",
    "manifestUrl": "https://openagent3.xyz/skills/agentic-coding/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/agentic-coding/agent.md"
  }
}
```
## Documentation

### 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.

~/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.

TopicFileSetup processsetup.mdMemory templatememory-template.mdPACT loopprotocol.mdContract promptsprompt-contracts.mdMerge handoff checklisthandoff.md

### 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:

Problem framing: restate objective and assumptions
Acceptance design: define checks before edits
Change set: produce the smallest useful diff
Trace 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
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: ivangdavila
- Version: 1.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/agentic-coding)
- [Send to Agent page](https://openagent3.xyz/skills/agentic-coding/agent)
- [JSON manifest](https://openagent3.xyz/skills/agentic-coding/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/agentic-coding/agent.md)
- [Download page](https://openagent3.xyz/downloads/agentic-coding)