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intent-framed-agents意图框架 Agent

Agent Skill

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。

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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-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

简介

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。

SKILL.md

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.

适合场景

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02

用户想查找某类 Agent Skill 时

03

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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。

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