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Agent Skill · 研究检索

intent-framed-agents(意图框架 Agent)

intent-framed-agents 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。

intent-framed-agents 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 33,257。

总安装 2,376周安装 101GitHub Stars 公开资料未说明下载 832核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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-framed-agents(意图框架 Agent)
来源仓库:https://github.com/pskoett/intent-framed-agents
安装命令:
openclaw skills install intent-framed-agents
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 intent-framed-agents

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

意图框架 Agents 提供结构化编码会话管理,强化规划与实施间的衔接。适用于 OpenClaw 中多人协作开发或长周期项目跟踪场景。通过实时偏差提醒帮助用户保持聚焦,减少上下文丢失风险。依赖持续桌面活动监听,可能涉及较高系统资源占用。建议在非敏感开发环境中试用,并定期审查其行为日志以确保可控性。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install intent-framed-agents;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

intent-framed-agents 怎么安装?

在终端执行 openclaw skills install intent-framed-agents。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

intent-framed-agents 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

intent-framed-agents 需要什么权限?

权限级别是需要联网。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。

intent-framed-agents 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

intent-framed-agents 用的人多吗?

公开数据是累计安装 2,376 次,近一周安装 101 次,下载 832 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

intent-framed-agents 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
intent-framed-agents

意图框架 Agent · 本页

2,376—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
intent-framed-agent
description
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.

Intent Framed Agent

Install

npx skills add pskoett/pskoett-ai-skills
npx skills add pskoett/pskoett-ai-skills/skills/intent-framed-agent

Purpose

This skill turns implicit intent into an explicit, trackable artifact at the moment execution starts. It creates a lightweight intent contract, watches for scope drift while work is in progress, and closes each intent with a short resolution record.

Scope (Important)

Use this skill for coding tasks only. It is designed for implementation work that changes executable code.

Do not use it for general-agent activities such as:

  • broad research
  • planning-only conversations
  • documentation-only work
  • operational/admin tasks with no coding implementation

For trivial edits (for example, simple renames or typo fixes), skip the full intent frame.

Trigger

Activate at the planning-to-execution transition for non-trivial coding work.

Common cues:

  • User says: "go ahead", "implement this", "let's start building"
  • Agent is about to move from discussion into code changes

Workflow

Phase 1: Intent Capture

At execution start, emit:

## Intent Frame #N

**Outcome:** [One sentence. What does done look like?]
**Approach:** [How we will implement it. Key decisions.]
**Constraints:** [Out-of-scope boundaries.]
**Success criteria:** [How we verify completion.]
**Estimated complexity:** [Small / Medium / Large]

Rules:

  • Keep each field to 1-2 sentences.
  • Ask for confirmation before coding:

- Does this capture what we are doing? Anything to adjust before I start?

  • Do not proceed until the user confirms or adjusts.

Phase 2: Intent Monitor

During execution, monitor for drift at natural boundaries:

  • before touching a new area/file
  • before starting a new logical work unit
  • when current action feels tangential

Drift examples:

  • work outside stated scope
  • approach changes with no explicit pivot
  • new features/refactors outside constraints
  • solving a different problem than the stated outcome

When detected, emit:

## Intent Check #N

This looks like it may be moving outside the stated intent.

**Stated outcome:** [From active frame]
**Current action:** [What is happening]
**Question:** Is this a deliberate pivot or accidental scope creep?

If pivot is intentional, update the active intent frame and continue. If not, return to the original scope.

Phase 3: Intent Resolution

When work under the active intent ends, emit:

## Intent Resolution #N

**Outcome:** [Fulfilled / Partially fulfilled / Pivoted / Abandoned]
**What was delivered:** [Brief actual output]
**Pivots:** [Any acknowledged changes, or None]
**Open items:** [Remaining in-scope items, or None]

Resolution is preferred but optional if the session ends abruptly.

Multi-Intent Sessions

One session can contain multiple intent frames.

Rules:

  1. Resolve current intent before opening the next.
  2. If user changes direction mid-task, resolve current intent as

Abandoned or Pivoted, then open a new frame.

  1. Drift checks always target the currently active frame.
  2. Number frames sequentially within the session (#1, #2, ...).
  3. Constraints do not carry forward unless explicitly restated.

Entire CLI Integration

Entire CLI: https://github.com/entireio/cli

When tool access is available, detect Entire at activation:

entire status 2>/dev/null
  • If it succeeds, mention that intent records will be captured in the session

transcript on the checkpoint branch. This enables learning-aggregator --deep to later mine intent frames and drift events for cross-session scope-drift patterns.

  • If unavailable/failing, continue silently. Do not block execution and do not

nag about installation.

Copilot/chat fallback:

  • If command execution is unavailable, skip detection and continue with the

same intent workflow in chat output.

How intent frames become learning signals

Each Intent Frame and Intent Check you emit is captured verbatim in Entire's session transcript. At cadence, learning-aggregator --deep reads those transcripts and extracts:

  • Frames that were resolved as Abandoned or Pivoted → potential planning

gaps

  • Drift signals that repeatedly fire in similar contexts → potential scope

definition issues

  • Constraint violations detected by drift checks → patterns for promotion to

project instruction files

You do not need to do anything special for this — the intent blocks are structured (## Intent Frame #N, ## Intent Check, ## Intent Resolution), which makes them parseable from the transcript.

Guardrails

  • Keep it lightweight; avoid long prose.
  • Do not over-trigger on trivial tasks.
  • Do not interrupt on every small step.
  • Treat acknowledged pivots as valid.
  • Preserve exact structured block headers/fields for parseability.

Interoperability with Other Skills

Use this skill as the front-door alignment layer for non-trivial coding work:

  1. plan-interview (optional, for requirement shaping)
  2. intent-framed-agent (execution contract + scope drift monitoring)
  3. context-surfing (context quality monitoring — runs concurrently with intent-framed-agent during execution)
  4. simplify-and-harden (post-completion quality/security pass)
  5. self-improvement (capture recurring patterns and promote durable rules)

Relationship with context-surfing

Both skills are live during execution. They monitor different failure modes:

  • intent-framed-agent monitors *scope* drift — is the agent doing the right

thing? It fires structured Intent Checks when work moves outside the stated outcome.

  • context-surfing monitors *context quality* drift — is the agent still

capable of doing it well? It fires when the agent's own coherence degrades (hallucination, contradiction, hedging).

They are complementary, not redundant. An agent can be perfectly on-scope while its context quality degrades. Conversely, scope drift can happen with perfect context quality. Intent Checks continue firing alongside context-surfing's wave monitoring.

Precedence rule: If both skills fire simultaneously (an Intent Check and a context-surfing drift exit at the same time), the drift exit takes precedence. Degraded context makes scope checks unreliable — resolve the context issue first, then resume scope monitoring in the next session.

Cadence separation: Intent Checks fire at scope boundaries — before touching a new area/file, before starting a new logical work unit, when the current action feels tangential. Context-surfing's pre-commit anchor check fires at side-effecting-action moments — specific tool calls, writes, commits, commit-level output. Don't run both in the same beat: if an Intent Check has just fired and resolved cleanly, the next side-effecting action inside the same work unit doesn't need a fresh anchor check — you already re-grounded.

What this skill produces

  • Intent frame artifact — consumed by context-surfing as part of the wave

anchor and copied verbatim into handoff files on drift exit.

  • Intent resolution — signals task completion, which triggers

simplify-and-harden.

  • Drift observations — scope drift patterns can be logged to

self-improvement as learnings if they recur.

平台分布

OpenClaw

91.77%
按下载量换算764

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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