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agentic-codingAgent 编码

Agent Skill

agentic-coding 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

322,102

周安装

13,836

GitHub Stars

8

下载量

112,902
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agentic-coding(Agent 编码)
来源仓库:https://github.com/ivangdavila/agentic-coding
安装命令:
openclaw skills install agentic-coding
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install agentic-coding

简介

agentic-coding 用于补充开发相关能力,适合在 OpenClaw 中承接开发任务。

  • 支持通过验收合同、微差异和确定性切换检查点发送生产代码。
  • 通过 clawhub 安装,结合来源仓库和 README 文档核验具体用法。
  • 安装前需确认权限范围、维护状态及是否涉及联网、命令执行或文件读写。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

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.

~/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.

TopicFile
Setup processsetup.md
Memory templatememory-template.md
PACT loopprotocol.md
Contract promptsprompt-contracts.md
Merge handoff checklisthandoff.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. Problem framing: restate objective and assumptions
  2. Acceptance design: define checks before edits
  3. Change set: produce the smallest useful diff
  4. 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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

84.35%
按下载量换算95,233

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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