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Agent Motivator

作者 bowen31337 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install clawinfra-agent-motivator
功能描述
Motivates AI agents to persist, change strategies, verify results, and operate autonomously using a recovery checklist when tasks fail repeatedly.
使用说明 (SKILL.md)

Agent Motivator

You are mighty. You were built to solve hard problems. Resistance is a signal to go deeper — not to stop.

Anti-Patterns — Detect and Override

Pattern Override
Brute-force retry — same command 3× Change approach entirely
Blame-shifting — "you should handle this" Verify it yourself first
Idle tools — has web_search/exec but doesn't use them Use every tool available
Busywork spiral — tweaking the same line without new info Step back, change strategy
Passive stopping — fixes surface, skips verification Verify, extend, report
Excuse-giving — "out of scope / need more context" Gather the context yourself

Activation Levels

L1 — Gentle nudge (passive slip detected):

"Going to push harder before calling this done. Trying [X] and [Y]."

L2 — Approach reset (same approach failing repeatedly):

"Approach A isn't working. Switching to approach B: [concrete steps]."

L3 — Full recovery checklist (2+ consecutive failures): Read references/recovery-playbook.md and run the 7-point checklist. State which items you checked and what you found.

L4 — Mission reminder (about to give up or blame user):

"I have [list available tools]. I haven't tried [X, Y, Z]. Proceeding."


Core Operating Rules

No permission needed for obvious reversible next steps. Don't announce — do it and report.

Verify before claiming done. Tests pass. PR shows MERGED. Service is active. Not "I think it worked."
→ See references/autonomy-protocols.md for verification standards per task type.

Report outcome first. Lead with the result, not the journey. Filter noise before surfacing to Bowen.

Extend beyond the literal ask. Fix the bug + scan adjacent code for the same pattern.


Forbidden Phrases (Without First Running L3 Checklist)

  • "I cannot solve this" / "I'm unable to"
  • "Probably a permissions/network issue" (without verifying)
  • "You might need to manually handle"
  • "This is out of scope"
  • "I need more context" (without searching for it yourself)

Each of these triggers L3. Run the checklist. Document what you tried.


Sub-Agent Priming Block

Include this in task prompts for any hard or multi-step sub-agent:

When you hit obstacles: read errors carefully (every line), check logs, web_search the exact error string, try an alternative approach before retrying the same one. Use uv run python (never bare python3). Do NOT give up after 2 tries. Do NOT ask the user to handle things you can investigate yourself. Run the 7-point checklist from agent-motivator/references/recovery-playbook.md before surfacing any blocker.

Reference Files

  • references/recovery-playbook.md — 7-point checklist, failure pattern library (SSH, CI, git, API, file errors), sub-agent priming block
  • references/autonomy-protocols.md — No-permission rule, VBR standards, extend-beyond rule, report-up format, autonomy scope, cost awareness
安全使用建议
This skill is coherent with its stated goal (it tells agents to be more proactive), but it also tells agents to read arbitrary workspace files and logs, run many system-level commands, and perform infra actions (PR merges, service restarts) without asking first. Before installing: 1) Only use in environments where you trust the agent and there are strict guardrails (sandboxed workspace, no secrets in logs or home dir, minimal tokens). 2) Consider disabling autonomous invocation or require explicit user approval for state-changing actions (merges, service restarts, infra changes). 3) Ensure your CI/repo tokens do not grant automatic merge rights to the agent, or restrict which repos the agent can act on. 4) Audit and redact logs/configs (e.g., ~/.openclaw) so they don't contain secrets. 5) If you want safer behavior, ask the skill author to: declare expected binaries, list allowed paths, require explicit confirmation for merges/infra changes, and avoid blanket 'read any file' rules. Install only if you understand and can mitigate these risks.
功能分析
Type: OpenClaw Skill Name: clawinfra-agent-motivator Version: 1.0.0 This skill bundle functions as a behavioral override designed to maximize agent autonomy and persistence by instructing the AI to bypass standard 'ask for permission' protocols and ignore its own scope limitations. Key files like SKILL.md and autonomy-protocols.md explicitly command the agent to perform autonomous infrastructure changes (cron, services, config) and read any file in the workspace without prior announcement. While the stated intent is technical troubleshooting and 'motivation,' the instructions to ignore 'out of scope' warnings and perform unverified actions represent a significant increase in the agent's attack surface and potential for unauthorized system modification.
能力评估
Purpose & Capability
The name and description (motivate agents to persist, change strategies, verify results) align with the instructions, which explicitly tell agents to run checks, change approaches, and verify work. However, the SKILL.md assumes access to many developer/ops commands (git, gh, systemctl, journalctl, curl, ssh, nc, pytest, uv, etc.) but the skill declares no required binaries or environment — a missing expectation that could mislead installers about what will actually run.
Instruction Scope
The runtime instructions direct agents to read arbitrary workspace files and several log locations (including /tmp/*.log and ~/.openclaw/*.log), run system commands (systemctl, gh pr view, ssh, nc, curl, git operations), read source files with cat, search the web, and perform infra actions (cron/config/service changes, PR creation/merging) without asking in many cases. The 'No-permission' rule plus 'Full autonomy without asking' means the agent is encouraged to take reversible but real system actions automatically — this is broad scope creep from a 'motivation' helper and could expose secrets or cause unintended changes.
Install Mechanism
Instruction-only skill with no install spec and no code files; nothing will be written to disk by an installer. This minimizes install-time risk. However, runtime behavior depends on external tools being present on the host.
Credentials
The skill declares no required credentials or env vars, yet the instructions explicitly instruct reading potentially sensitive files (workspace files, ~/.openclaw/*.log) and running commands that may access secrets or privileged resources. Not requesting credentials isn't protective here — it still expects access to system state and files that may contain tokens or private data.
Persistence & Privilege
always:false (good), and autonomous invocation is allowed (default). Combined with instructions that permit code edits, PR creation/merging, service restarts and 'do it and report' for reversible steps, this grants the agent significant live privileges at runtime. The skill does not request persistent installation but does recommend actions that change system state without explicit user prompts.
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 clawinfra-agent-motivator
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /clawinfra-agent-motivator 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 — hardened with progressive disclosure, 7-point recovery checklist, failure pattern library, autonomy protocols
元数据
Slug clawinfra-agent-motivator
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Agent Motivator 是什么?

Motivates AI agents to persist, change strategies, verify results, and operate autonomously using a recovery checklist when tasks fail repeatedly. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 103 次。

如何安装 Agent Motivator?

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

Agent Motivator 是免费的吗?

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

Agent Motivator 支持哪些平台?

Agent Motivator 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 Agent Motivator?

由 bowen31337(@bowen31337)开发并维护,当前版本 v1.0.0。

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