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intent-align意图对齐

Agent Skill

intent-align 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

14,665

周安装

605

GitHub Stars

公开资料未说明

下载量

4,792
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:intent-align(意图对齐)
来源仓库:https://github.com/oscraters/intent-align
安装命令:
openclaw skills install intent-align
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。

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openclaw skills install intent-align

简介

意图对齐技能协调多主机环境下的代理团队目标,保持用户中心的工作流一致性。

  • 适合在 OpenClaw 中管理分布式代理协作时维持战略方向统一。
  • 通过意图捕获与偏差检测机制预防执行偏离预期目标的情况发生。
  • 部署前应确认各节点通信安全与身份认证机制的有效性。
  • 建议查阅源码了解具体协调协议与冲突解决策略的实现逻辑。

SKILL.md

name
intent-align
description
Intent-alignment orchestration for OpenClaw agent teams across diverse host environments. Use when work must stay anchored to user goals while allowing flexible execution: (1) coding with repos/PRs/issues, (2) multi-phase delivery with checkpoints, (3) multi-agent or multi-repo coordination, (4) evolving requirements needing structured re-alignment and user clarification loops.

Intent-Align v2 Core

Keep execution aligned to user intent while preserving agent autonomy.

Quick Start

  1. Read references/core-contract.md.
  2. Create alignment hub from references/alignment-hub-template.md.
  3. Run Intent Quality Gate before planning.
  4. Select adapters from references/adapters/.
  5. Execute phases with realignment and verification gates.

Workflow Contract

  1. Build intent_snapshot and run intent_lint (see core contract).
  2. Ask for strictness mode and scope overrides (project/repo/workflow/task).
  3. Resolve effective strictness by precedence: task > workflow > repo > project > default.
  4. Evaluate ambiguity action using severity + strictness + risk class.
  5. Generate phase plan and Mermaid diagram with explicit dependencies and gates.
  6. Bind available adapters through capability_matrix.
  7. Execute phase-by-phase.
  8. Before phase start, run Pre-Execution Clarification Gate.
  9. On each phase end, run verification gates (including output conformance) and update drift evidence.
  10. If intent or constraints change, apply intent_delta and re-plan only impacted phases.
  11. Close with final alignment report and open ambiguity list (if any).

Autonomy Levels

  • 1 Strict: Require user confirmation before each phase start.
  • 2 Balanced: Require user confirmation at phase end or any critical drift.
  • 3 Aggressive: Auto-continue on low drift; require confirmation on major deltas.
  • 4 Exploratory: Continue with log-only check-ins unless risk or ambiguity threshold is exceeded.

Override rule: high-risk ambiguity is never advisory-only; enforce at least soft_gate. Strictness rule: strictness mode controls whether ambiguities block or proceed with guardrails.

Required References

Adapter Selection

Use only adapters needed for the task:

If no adapter can satisfy a required capability:

  1. Generate an ad-hoc adapter spec from adapter-template.md.
  2. Add provenance metadata (created_by, created_at, environment_assumptions, tool_access_required).
  3. Validate required fields before use.
  4. Register the new adapter in capability_matrix.adapters_selected.
  5. Continue in degraded mode only if validation fails or auth/capability remains unavailable.

Anti-Bloat Rules

  • Keep core contract tool-agnostic.
  • Do not add tracker- or host-specific logic to core files.
  • Add a new adapter only for a proven capability gap.
  • Keep schemas single-source; do not duplicate fields across files.
  • Tie each new feature to one concrete failure mode and one test scenario.
  • Generate ad-hoc adapters only for current task scope; do not pre-generate broad catalogs.
  • Use canonical capability IDs first; extend only when needed via namespaced custom IDs.

Edge Cases

  • Multi-repo: maintain one hub with per-repo adapter bindings and dependency graph.
  • Non-git prototype: use local artifacts and explicit acceptance criteria checkpoints.
  • Team swarm: assign owner per phase and keep decision log in hub.

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

72.58%
按下载量换算3,478

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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

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