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intent-framed-agents

作者 pskoett · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install intent-framed-agents
功能描述
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for c...
使用说明 (SKILL.md)

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.
  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.
安全使用建议
This skill appears coherent and low-risk: it only emits structured intent/check/resolution text and asks you to confirm before proceeding. Before installing/use, verify provenance (source/homepage is missing here) and double-check where session transcripts and checkpoint branches are stored and who can access them—if you run Entire CLI or learning-aggregator, confirm whether those tools upload transcripts off-host or make them available to others. If you have sensitive data that might appear in intent frames, avoid including it (or confirm retention/processing policies). Also review any external packages (the SKILL.md references a pskoett package) before running installation commands.
功能分析
Type: OpenClaw Skill Name: intent-framed-agents Version: 1.0.0 The skill is a process-management framework designed to help AI agents track intent and prevent scope drift during coding tasks. It uses structured markdown templates (Intent Frame, Intent Check, Intent Resolution) to maintain alignment with the user and optionally integrates with the 'entire' CLI tool via a simple status check (entire status). No malicious code, data exfiltration, or harmful prompt injection was found in SKILL.md or _meta.json.
能力评估
Purpose & Capability
Name/description match the SKILL.md: the skill's sole function is to capture intent frames, monitor drift, and emit structured intent/resolution blocks. It does not require unrelated binaries, environment variables, or credentials.
Instruction Scope
Instructions stay within the stated scope (capture/monitor/resolve intent for coding work). They do instruct the agent to emit structured blocks into the session transcript and to detect the Entire CLI if available; this means intent blocks may be recorded in transcripts and later mined by tooling (learning-aggregator). That is expected for the skill's purpose but is a privacy/retention consideration.
Install Mechanism
No install spec or code files are present; the SKILL.md contains example commands (npx skills add ...) for adding related packages but those are guidance only. There is no automatic download/install described in the skill itself.
Credentials
The skill declares no required environment variables, no credentials, and no special config paths. Nothing requested is disproportionate to its described functionality.
Persistence & Privilege
The skill is not configured always:true and does not request permanent presence or elevated privileges. It does describe interoperability with local tooling (Entire CLI and learning-aggregator) but does not instruct modifying other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install intent-framed-agents
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /intent-framed-agents 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Updated installation instructions to reflect the correct subpath: use `pskoett-ai-skills/skills/intent-framed-agent`. - Expanded the Entire CLI integration section with details on how intent frames and drift events are mined for learning signals. - Added a section explaining how intent frames are used as structured learning signals with tools like `learning-aggregator --deep`. - Revised the interoperability section to clarify concurrent use with the `context-surfing` skill, providing a detailed description of their complementary roles and precedence rules. - Clarified what artifacts and outputs this skill produces and how they interact with related skills.
元数据
Slug intent-framed-agents
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

intent-framed-agents 是什么?

Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for c... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 82 次。

如何安装 intent-framed-agents?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install intent-framed-agents」即可一键安装,无需额外配置。

intent-framed-agents 是免费的吗?

是的,intent-framed-agents 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

intent-framed-agents 支持哪些平台?

intent-framed-agents 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 intent-framed-agents?

由 pskoett(@pskoett)开发并维护,当前版本 v1.0.0。

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