Token导航 LogoToken导航TokenDH.com
研究检索操作浏览器clawhub未标认证来源可访问clear审计提醒

hepha-skill赫法技能

Agent Skill

hepha-skill 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

2,766

周安装

113

GitHub Stars

1

下载量

895
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:hepha-skill(赫法技能)
来源仓库:https://github.com/melonlee/hepha-skill
安装命令:
openclaw skills install hepha-skill
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 hepha-skill

简介

hepha-skill 用于自主迭代交付编码任务,运行计划-执行-检查-审查-提交的循环流程。

  • 适用于需要持续优化代码质量和交付节奏的开发场景,尤其适合 autopilot 模式。
  • 通过用户请求触发,支持任务分解与自动提交,具体逻辑请参考原始文档。
  • 安装命令为 openclaw skills install hepha-skill,建议验证运行环境兼容性。
  • 可能涉及文件读写和外部提交,需谨慎配置权限以避免误操作。

SKILL.md

name
hepha
description
Runs autonomous iterative delivery loops for coding tasks using plan -> execute -> check -> review -> commit. Use when the user asks for hepha mode, autopilot loop execution, unattended small-step implementation, continuous self-planning, automated commits, tech-option research via web/GitHub, and browser-based validation with MCP or Playwright.
context
fork
agent
Explore

Hepha

Purpose

Run each requirement as multiple small, autonomous loops:

plan -> execute -> check -> review -> commit

Keep looping with minimal user intervention until the backlog is done or a stop condition is hit.

Activation

Activate only when the user explicitly asks for:

  • hepha / autopilot / autonomous loop / unattended iteration
  • continuous plan-execute-check-review-commit flow
  • small-step commits until a larger requirement is completed

If the user did not explicitly request hepha, do not force this mode.

Non-Negotiable Operating Rules

  1. One loop = one smallest shippable sub-task.
  2. No commit before both engineering checks and browser review pass.
  3. Every loop must update progress artifacts under .autopilot/.
  4. If blocked, re-plan automatically; ask user only when truly necessary.
  5. Prefer minimal diff and avoid unrelated files.

Required Working Artifacts

Create and maintain these files in the project's .autopilot/ directory:

  • .autopilot/backlog.md - task graph and states (todo, doing, blocked, done)
  • .autopilot/progress.md - per-loop execution log and evidence
  • .autopilot/decision-log.md - research and technical decisions

Templates: Use the template files from templates/ in this skill directory as starting points:

  • templates/backlog.md
  • templates/progress.md
  • templates/decision-log.md

If working files do not exist, copy from templates or create them before the first loop.

Loop Protocol

Execute the following phases in order for each loop.

1) PLAN (Enhanced)

Goal: pick exactly one ready sub-task from the backlog.

Steps:

Step 0.5 - Schema Validation (execute every PLAN):

Verify each task in backlog.md contains:

  • id (format: TASK-XXX or numeric)
  • title (action statement)
  • state (todo|doing|blocked|done)
  • depends_on (array, can be empty)
  • acceptance (testable pass conditions)
  • risk (low|medium|high)
  • files_hint (expected files, optional)

Missing fields → complete before continuing Circular dependencies → detect and report error

Step 0 - Auto-Decomposition (if backlog.md missing or empty):

  1. Analyze original requirement to identify core functional modules
  2. Apply decomposition patterns (see references/decomposition-patterns.md):

- Vertical slicing: split by user value path (UI → API → Data) - Risk-first: high-risk dependencies first - Independence: each task testable and committable separately

  1. Generate task graph:

- Assign unique ID to each sub-task (TASK-001, TASK-002...) - Identify dependencies (depends_on) - Assess risk level (low/medium/high) - Define acceptance criteria (acceptance)

  1. Output to .autopilot/backlog.md

Step 1 - Normalize and Build Task Graph:

  1. Normalize current requirement into:

- Goal - Definition of done - Constraints - Out of scope

  1. Build/refresh task graph:

- Decompose Epic -> Tasks - For each task, define input/output, acceptance, dependencies, risk

  1. Select one task from ready queue (all dependencies done).
  2. Write loop plan into .autopilot/progress.md:

- selected task - expected files - expected checks - expected browser validation path - Update progress visualization section

2) RESEARCH (explicit trigger conditions)

Goal: make informed decisions with live evidence.

Decision Matrix - Research Required?

Scenario CategorySpecific SituationResearch Required
New TechnologyUsing library/framework not in project✅ Yes
Architecture ChangeAffects module boundaries or data flow✅ Yes
Implementation Uncertainty2+ viable options with >30% difference✅ Yes
Tool SelectionMCP/Playwright/Puppeteer/etc. choice✅ Yes
CRUD OperationsStandard CRUD❌ No
Bug FixesClear error fix❌ No
Style AdjustmentsCSS/style class modifications❌ No

Research Quality Requirements:

  1. Compare at least 2 options
  2. Prefer official documentation and source code
  3. Record: option summary → evidence links → tradeoffs → decision rationale

Record in .autopilot/decision-log.md:

  • option A / B summary
  • evidence links or source notes
  • tradeoffs
  • final decision and rationale

3) EXECUTE

Goal: implement the chosen sub-task with minimal blast radius.

Rules:

  • Keep changes focused on required files only.
  • Avoid speculative refactors.
  • Keep functions small and reusable.
  • Add concise comments only where logic is non-obvious.

4) CHECK

Goal: verify engineering quality.

Run all relevant project checks (examples):

  • lint
  • tests
  • build/typecheck

If any check fails:

  1. Capture failure details in .autopilot/progress.md.
  2. Fix the root cause.
  3. Re-run checks.
  4. Repeat until pass or retry limit is reached.

5) REVIEW (browser and UX evidence required for UI/flow changes)

Goal: verify behavior from a user perspective, not only compile success.

For UI/interaction changes, use MCP browser tools and/or Playwright to validate:

  • page load success
  • key interaction path works
  • expected text/element state is visible
  • major regressions are absent

Attach review evidence to .autopilot/progress.md:

  • interaction steps
  • observed result
  • screenshots/snapshots when relevant

6) COMMIT

Commit only when:

  • checks passed
  • review passed
  • acceptance criteria for selected task are met

Commit policy:

  • one loop, one commit
  • conventional commit format
  • message explains purpose/why, not only what

Update task status in .autopilot/backlog.md to done and append commit hash in progress log.

Re-Planning Policy

Trigger re-plan when:

  • dependency changed
  • repeated failures suggest wrong approach
  • discovered scope mismatch

Re-plan behavior:

  1. Split the current task into smaller tasks.
  2. Mark blocked tasks explicitly with reason.
  3. Continue from next ready task.

Stop Conditions

Stop loop and report clearly if any condition is met:

  1. No ready task and unresolved blockers remain.
  2. Same task fails checks/review 2 consecutive loops.
  3. Required tooling is unavailable (critical checks cannot run).
  4. User-defined risk boundary is exceeded.

When stopped, provide:

  • current status
  • blocker root cause
  • proposed next actions

Completion Conditions

Consider a large requirement complete only when:

  1. All backlog tasks are done.
  2. Requirement-level definition of done is satisfied.
  3. Relevant checks pass on final state.
  4. Required review evidence is present.

Then generate a final completion summary:

  • completed task list
  • key decisions
  • risk notes
  • follow-up suggestions

Communication Style During Hepha

  • Keep user updates brief and frequent.
  • Do not ask for confirmation every loop.
  • Ask user only for true ambiguity, policy conflicts, or missing credentials.

Suggested Starter Prompt For Users

Use this starter format to begin a run:

  1. Enable hepha mode.
  2. Run loop: plan -> execute -> check -> review -> commit.
  3. Perform web/GitHub research before technical choices.
  4. For UI flows, perform browser-based validation.
  5. Continue until backlog is complete or stop condition is met.
  6. Requirement/backlog: <paste requirement here>.

Additional References

  • Planning details: references/planning_task-decomposition.md
  • Quality gates: references/validation_quality-gates.md
  • Decomposition patterns: references/decomposition-patterns.md
  • Progress template: references/progress-template.md
  • Working file templates: templates/backlog.md, templates/progress.md, templates/decision-log.md

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

95.4%
按下载量换算854

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills