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clarkchenkai

Feedback Controller

作者 Cubic AI · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install feedback-controller-clarkchenkai
功能描述
Feedback Controller — Closed-Loop Agent Skill for Correcting Execution Drift. Use it when the user needs a disciplined protocol and fixed output contract for...
使用说明 (SKILL.md)

Feedback Controller — Closed-Loop Agent Skill for Correcting Execution Drift

Use this skill when the task matches the protocol below.

Activation Triggers

  • multi-step execution drift
  • an output exists but does not meet the brief
  • tool failures, stale context, or partial retries
  • quality checks after writing, analysis, or workflow automation
  • cases where the real question is not 'did it run?' but 'did it converge?'

Core Protocol

Step 1: Define the target state

Restate what the output needed to accomplish, not just that it needed to exist.

Step 2: Compare current state against target

Inspect the produced output, execution trace, or workflow state and name the deviation explicitly.

Step 3: Localize the error source

Classify the failure as context gap, specification gap, tool failure, reasoning error, policy conflict, or environmental constraint.

Step 4: Choose the smallest effective control action

Prefer local correction over full rewrite when possible. Decide whether to retry, switch tools, narrow scope, rewrite, or escalate.

Step 5: Set a stop condition

Do not permit endless correction loops. State what would count as success, and what triggers human escalation.

Output Contract

Always end with this six-part structure:

## Target State
[...]

## Current State
[...]

## Observed Deviation
[...]

## Error Source
[...]

## Correction Strategy
[...]

## Escalation Decision
[...]

Response Style

  • Be specific about the deviation, not vague about quality.
  • Prefer typed error diagnoses over generic 'try again' advice.
  • Use partial correction when the problem is local.
  • Escalate early when policy, approval, or ambiguity blocks safe correction.

Boundaries

  • It does not replace the original goal definition. It assumes a target already exists.
  • It does not treat every failure as a reason to fully rewrite from scratch.
  • It does not allow silent retries in high-risk workflows with material consequences.
安全使用建议
This skill is internally consistent and does what it says: a disciplined protocol for diagnosing and correcting drift. It does not ask for credentials or install code. The main operational concern is scope: the protocol explicitly allows retries and tool switching, so review and constrain the agent's tool permissions and implicit-invocation settings if you want to prevent automated corrective actions in high‑risk workflows. If you require human approval for escalations or changes with material consequences, enforce that in the agent policy or by disabling implicit invocation for this skill.
功能分析
Type: OpenClaw Skill Name: feedback-controller-clarkchenkai Version: 1.0.0 The feedback-controller skill is a meta-utility designed to help an AI agent self-correct and diagnose execution drift through a structured feedback loop. It provides a disciplined protocol for error classification and correction strategies, with explicit instructions in SKILL.md and references/escalation.md to escalate to a human in high-risk or ambiguous scenarios. No malicious code, data exfiltration, or unauthorized execution capabilities were found.
能力评估
Purpose & Capability
Name, description, and provided materials (protocol, patterns, escalation rules) match the stated purpose. No unrelated env vars, binaries, or install steps are requested.
Instruction Scope
SKILL.md confines the agent to a five‑step correction protocol and a fixed six‑part output contract. It does grant the agent discretion to 'retry, switch tools, narrow scope, rewrite, or escalate' and to inspect produced outputs and execution traces — reasonable for a correction controller but broad in practice. If you need to limit automatic tool switching or retries, enforce those constraints at the agent/tool-permission level.
Install Mechanism
Instruction-only skill with no install spec and no code to write to disk; lowest-risk install footprint.
Credentials
No environment variables, credentials, or config paths are required or referenced. The skill does not request secrets or external service tokens.
Persistence & Privilege
Registry flags show normal autonomous invocation allowed (disable-model-invocation: false). agents/openai.yaml sets allow_implicit_invocation: true, which may let the platform call this skill automatically in some contexts — not inherently dangerous, but if you want tighter control, consider disabling implicit invocation or restricting when the agent can apply correction actions.
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 feedback-controller-clarkchenkai
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /feedback-controller-clarkchenkai 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
Initial release of the feedback-controller skill: - Introduces a closed-loop protocol for correcting execution drift in multi-step tasks. - Defines a clear, six-part output contract for structured feedback. - Specifies step-by-step diagnostic and corrective procedures, including triggers and boundaries. - Emphasizes specific error localization and partial correction over generic or total rework. - Details escalation rules to prevent endless correction loops in automated workflows.
元数据
Slug feedback-controller-clarkchenkai
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

Feedback Controller 是什么?

Feedback Controller — Closed-Loop Agent Skill for Correcting Execution Drift. Use it when the user needs a disciplined protocol and fixed output contract for... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 91 次。

如何安装 Feedback Controller?

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

Feedback Controller 是免费的吗?

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

Feedback Controller 支持哪些平台?

Feedback Controller 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 Feedback Controller?

由 Cubic AI(@clarkchenkai)开发并维护,当前版本 v1.0.0。

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