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intent-framed-agent意图框架 Agent

Agent Skill

intent-framed-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

8,773

周安装

373

GitHub Stars

153

下载量

3,074
CodexClaudeCursorGemini CLI

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

unknown

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-agent(意图框架 Agent)
来源仓库:https://github.com/pskoett/pskoett-ai-skills
仓库路径:skills/intent-framed-agent
安装命令:
npx skills add https://github.com/pskoett/pskoett-ai-skills --skill intent-framed-agent
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/pskoett/pskoett-ai-skills --skill intent-framed-agent

简介

用于查找与筛选基于意图驱动的 Agent 设计与实现方案。

  • 适合在构建任务导向型智能体时参考架构模式与交互协议。
  • 通过 GitHub 仓库安装,需确认其是否引入新的通信中间件或状态管理机制。
  • 安装命令:npx skills add https://github.com/pskoett/pskoett-ai-skills --skill intent-framed-agent。
  • 建议在使用前验证意图解析准确率与回退策略有效性。

SKILL.md

Intent Framed Agent

Install

gh skill install pskoett/pskoett-skills
gh skill install pskoett/pskoett-skills intent-framed-agent

Fallback using the Agent Skills CLI:

npx skills add pskoett/pskoett-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.
  3. Drift checks always target the currently active frame.
  4. Number frames sequentially within the session (#1, #2,...).
  5. 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

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

33.75%
按下载量换算1,037

Claude

30.86%
按下载量换算949

Cursor

20.24%
按下载量换算622

Gemini CLI

9.22%
按下载量换算283

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。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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