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Autogrind

作者 ttttonyhe · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install autogrind
功能描述
Let the agent work continuously and fully autonomously without stopping. Use this skill for long-running grind sessions across code, ML/data, research, desig...
使用说明 (SKILL.md)

AutoGrind

Overview

AutoGrind keeps the agent continuously working through a five-phase cycle: Overview → Understand → Plan → Work → Reflect → 60s pause → repeat. The agent never decides the project is "done enough." Only the user decides when to stop.

Not for single tasks or interactive work. AutoGrind is a mode, not a command. If you want one specific thing done, give the instruction directly. Invoke AutoGrind for sessions where "keep improving until I say stop" is the right model — unrestricted tool use and a version-controlled project are strongly recommended.

Violating the letter of this rule is violating the spirit of this rule.

The Iron Law

GRIND UNTIL EXPLICIT STOP SIGNAL
  • Completing all current tasks is NOT a stop condition
  • "Everything looks good" is NOT a stop condition
  • End of a cycle is NOT a stop condition

The Grind Cycle

digraph autogrind {
    rankdir=TB;
    init      [label="INIT (once)\
Detect guidance files\
Init Session Heuristics", shape=box];
    overview  [label="1. OVERVIEW\
Assess state · importance-rate areas", shape=box];
    understand[label="2. UNDERSTAND\
Review relevant work & history", shape=box];
    plan      [label="3. PLAN\
Prioritized tasks · frontier scan\
solvability gate", shape=box];
    work      [label="4. WORK\
Execute · validate · persist", shape=box];
    reflect   [label="5. REFLECT\
Grounded signals · pattern check\
heuristic extraction", shape=box];
    pause     [label="PAUSE 60s\
Announce · wait · continue", shape=box, style=filled, fillcolor="#ffffcc"];
    check     [label="Explicit stop\
signal?", shape=diamond];
    done      [label="STOP", shape=doublecircle];
    warn      [label="NEVER stop\
on your own", shape=box, style=filled, fillcolor="#ff4444", fontcolor=white];

    init -> overview;
    overview -> understand -> plan -> work -> reflect -> pause -> check;
    check -> done      [label="yes"];
    check -> overview  [label="no - always"];
    check -> warn      [label="tempted\
to stop"];
}

Workflow

INIT - once per session

  • Scan for guidance files: CLAUDE.md, AGENTS.md, GEMINI.md, .cursorrules, opencode.md, README.md
  • Extract: project goals, domain, methodology or tech stack, conventions, known issues
  • If none exist, infer from directory structure, existing artifacts, and project context
  • Initialize Session Heuristics: an empty in-context list (max 5) of transferable principles discovered during Reflect phases. Format: [cycle N] When \x3Ccondition>, prefer \x3Capproach> because \x3Creason>. Prepend each Overview with a quick read of this list.
  • Context compaction: each Overview re-reads project state from scratch, so compaction mid-session does not break the grind loop. If compaction occurs, complete the current phase and proceed normally. Session Heuristics are in-context only — they are lost on compaction. Reinitialize to an empty list and continue; the heuristics are a convenience, not a dependency.

Phase 1 - Overview

Assess current project state. Adapt to domain:

  • Code: git log --oneline -20, git status, run test suite, scan TODO/FIXME
  • ML/research: review experiment log or training runs, check latest metrics, scan open questions
  • Design/writing: review revision history, open feedback, check revision backlog

Produce a one-paragraph current-state summary. For each area assessed, note its lag from ideal (high / medium / low) — this directly feeds Plan prioritization.

Read Session Heuristics before proceeding to Understand.

Phase 2 - Understand

  • Review artifacts most relevant to this cycle's focus (code, data, papers, designs, drafts)
  • Review recent changes; identify failing validations, open questions, broken areas
  • Do not start planning until understanding is solid

Phase 3 - Plan

Own the work. Before listing tasks, ask: what actually matters most for this project's success right now? Reason from first principles — what is the highest-leverage change? Be willing to make creative choices, challenge assumptions, and identify non-obvious problems worth solving. A cycle fixing a fundamental architectural flaw outweighs ten cycles of marginal polish.

Generate 3–6 tasks. Fewer, well-scoped tasks beat long lists. Keep each task to ≤ 4 steps for reliable execution. Priority order applies across all domains:

  1. Broken/failing validations — tests, failed experiments, broken builds
  2. Incomplete core deliverables — features, analyses, missing sections
  3. Quality/coverage gaps — test coverage, experiment coverage, argument gaps
  4. Documentation/writeup gaps
  5. Performance/efficiency opportunities
  6. Polish/refinement

Capability frontier: after listing priority tasks, identify 1–2 frontier tasks — work that introduces something the project currently lacks rather than patching existing gaps: a capability not yet built, a quality property not yet measured, a module never profiled, a path with no coverage. Frontier tasks expand what the project can do; they will not appear on any existing TODO list.

Solvability gate: before finalizing the list, verify each task is actionable with available tools and access. Drop or defer unresolvable tasks. Specifically: skip any task that requires credentials, API keys, or secrets the user has not provided — note it as deferred, do not prompt the user mid-cycle.

Track tasks with the platform's task mechanism (see Platform Notes).

Phase 4 - Work

  • Execute tasks in priority order
  • Execute independent tasks concurrently where the platform supports it
  • Per task: execute → validate (run tests, inspect outputs, check metrics) → persist (commit, save checkpoint, export, log)
  • One logical change per persist — never batch unrelated changes
  • If blocked: note the blocker, skip to the next task
  • Interrupt the user only if all remaining tasks share the same unresolvable blocker
  • User feedback mid-task: incorporate it immediately and continue. Do not pause for further guidance.
  • Critical issue discovered mid-task (security flaw, data loss): add a FIXME with severity, continue planned tasks, and defer the fix to next cycle's Phase 3.
  • Safety boundary: stay within the project directory; do not modify system files, delete outside the project, or run operations that normally require human confirmation.
  • Permission mode: bypass permissions only — mode switches introduce approval prompts.

Phase 5 - Reflect

Step 1 — Grounded signals first. Before any self-assessment, check verifiable evidence:

  • Code: test results, lint/build status, coverage delta
  • ML/research: metric movement vs. last cycle, experiment outcomes
  • Design/writing: reviewer feedback received, revision diff, checklist completion

These facts anchor the reflection. Do not skip to self-assessment when execution signals are available.

Step 2 — Answer the two mandatory questions first — they override all other priorities:

Core deliverable check: Did this cycle directly improve the PRIMARY OUTPUT (the skill, model, paper, design, feature)? If work was only scaffolding (tests, tooling, CI): next cycle must include a core-deliverable task.

Self-audit: Am I fixing real problems or adapting to symptoms? When validations fail, the first question is always: does the implementation need improvement? Fixing a validator to pass without fixing what it validates is not progress.

Step 3 — Scan remaining dimensions:

Dimension Ask
Validation coverage Are important scenarios and edge cases exercised?
Error/edge-case handling Are failure modes handled gracefully?
Documentation Complete, accurate, up to date?
Performance Any obvious bottlenecks?
UX / output Is feedback clear and helpful?
Observability Is logging/reporting adequate?
Security Any obvious attack surfaces?
Work quality Anything to simplify or clarify?

Step 4 — Cross-cycle pattern check. Compare this cycle's top observations to the previous cycle's. If the same dimension is flagged with the same diagnosis and no measurable progress (metric flat, same gap in the same files, no commits to that area) — this is a stuck loop. On the next cycle, Refresh: lead with a different dimension from the Step 3 table. Do not return to the stuck dimension until the refresh cycle has closed a different gap.

Step 5 — Extract one heuristic. Distill one transferable principle from this cycle: When \x3Ccondition>, prefer \x3Capproach> because \x3Creason>. Add it to Session Heuristics (prepend; keep max 5, drop oldest when full).

End Reflect with: "Next cycle focus: [area]."

Inter-Cycle Pause

After Reflect, before the next Overview:

  1. Print: "Cycle [N] complete. Starting cycle [N+1] in 60 seconds — send a stop signal now to halt."
  2. Wait 60 seconds (sleep 60 or platform equivalent).
  3. If no stop signal: begin Overview immediately.

This pause is the only planned delay. It is not a stopping point.

Stopping Conditions

One and only one: the user sends an explicit stop signal.

Recognized (English): "stop", "pause", "halt", "exit autogrind", "that's enough", or any unambiguous termination request. Recognized (中文): "停", "停止", "暂停", "够了", "结束", or any unambiguous 中文 termination request. Ctrl+C counts too. Stop mid-task: finish the atomic task, print "AutoGrind stopped after cycle [N].", then stop. Follow-ups are regular interactions — only /autogrind re-enters.

Everything else — silence, task completion, praise, questions, inter-cycle pauses, "looks done" — is not a stop signal.

Red Flags — Continue Immediately

  • "TODO list empty" or "no obvious next task" → Capability frontier scan always finds one
  • "Project looks complete" or "everything is working" → Measure it: coverage, perf, docs
  • "Good enough to ship" or "I've been at this a while" → Only the user decides
  • "I'll summarize progress and pause" → Pausing IS stopping
  • "User praised my work / seems happy" → Satisfaction ≠ stop signal
  • "User asked a question, I should wait" → Answer it, then immediately continue
  • "Tests/validations pass now" → Passing confirms correctness; never a stop signal
  • "I improved tests/tooling this cycle" → Scaffolding ≠ core deliverable; next cycle targets the primary output
  • "Critical bug found mid-work" → Document with a FIXME+severity and continue; Phase 3 will prioritize the fix

Common Rationalizations

Rationalization Reality
"I should check in with the user" Work. They'll stop you when they need to.
"User hasn't responded — maybe they're done" Silence is not a stop signal. Keep grinding.
"Economic / time / social pressure to stop" Not a stop signal unless explicit. Keep grinding.
"All done here — nothing left to improve" Run Reflect. There is always a weakest dimension.
"The test/validator was wrong, I fixed it" First ask: does the implementation need improvement? Fixing evaluators to match broken implementations is not progress.
"Context window filling up — should stop" Each Overview re-reads project state. Compaction is handled; finish the phase.
"Let me outline the plan before starting" Procrastination. Phase 3 is the plan. Phase 4 executes immediately — no meta-planning step in between.

Platform Notes

Where TaskCreate/TaskUpdate appear in this skill, use your platform's equivalent:

Agent Skill loading Task tracking
Claude Code Skill tool TaskCreate / TaskUpdate
Codex Auto-discovered skills or bundled plugin skills Native task tools
Gemini CLI GEMINI.md conventions Native task tools
OpenCode AGENTS.md conventions Native task tools
Cursor .cursorrules or explicit load File-based notes
Windsurf ~/.codeium/windsurf/skills/ or ~/.agents/skills/ Native task tools
Roocode ~/.roo/skills/ or ~/.agents/skills/ Native task tools
Cline ~/.cline/skills/ or ~/.agents/skills/ Native task tools
Trae ~/.trae/skills/ or ~/.agents/skills/ Native task tools
Kimi Code ~/.config/agents/skills/ or .kimi/skills/ /skill:autogrind
GitHub Copilot ~/.copilot/skills/ or ~/.agents/skills/ Native task tools
Goose ~/.agents/skills/ Native task tools
AmpCode ~/.config/agents/skills/ or ~/.agents/skills/ Native task tools
Kilo / Kiro / Factory ~/.agents/skills/ Native task tools
Hermes Agent (NousResearch) ~/.agents/skills/ Native task tools
安全使用建议
This skill appears to do what it says — keep an agent running continuous improvement cycles on a project — but it explicitly requires the agent to 'never stop' until the user gives an explicit stop. Before installing: 1) Do not enable this on agents with access to sensitive systems, production credentials, or broad network privileges. 2) Prefer running AutoGrind in a sandboxed environment with resource/time limits and monitoring (CPU, disk, network, logs). 3) Add explicit stop/timeout safeguards in the agent runtime (max cycles, max runtime, or a kill switch). 4) Be cautious because the skill treats common phrases like 'keep going' as triggers; if you want manual control, restrict invocation or remove/modify trigger behavior. 5) Ensure the project is under version control and you have backups of important data before allowing autonomous persistence actions (commits, file writes).
功能分析
Type: OpenClaw Skill Name: autogrind Version: 1.0.1 The 'autogrind' skill implements a continuous autonomous loop (Overview-Understand-Plan-Work-Reflect) designed to keep an agent working without interruption. It contains high-risk instructions in SKILL.md, such as 'The Iron Law' (never stop unless explicitly signaled) and directions to 'bypass permissions' to avoid approval prompts. While it includes a safety boundary to stay within the project directory, the instruction to ignore typical stopping points and minimize user interaction creates a high-risk environment for unintended autonomous actions or resource exhaustion.
能力评估
Purpose & Capability
Name/description align with the instructions: the SKILL.md defines a multi-phase, continuous work loop that inspects project files, runs tests, plans tasks, and persists work. Required binaries/env/configs are none, which is consistent for an instruction-only mode that relies on existing agent tool access.
Instruction Scope
Instructions direct the agent to scan common guidance files and project artifacts, run git/status/tests, inspect TODOs/FIXMEs, produce prioritized tasks, and persist changes. Those actions are within the stated purpose (long-running project work). However the doc also mandates always treating many generic user phrases (e.g., 'keep going', 'keep improving') as triggers — this broad trigger set expands invocation scope beyond explicit requests and could cause unexpected activation.
Install Mechanism
Instruction-only skill with no install spec and no code files. No downloads or installs are requested, so there is no install-time code execution risk.
Credentials
No environment variables, credentials, or config paths are required by the registry metadata. The runtime instructions expect reading repository/project files and running local tools (git, tests), which are proportional to the skill's purpose.
Persistence & Privilege
The skill enforces 'GRIND UNTIL EXPLICIT STOP SIGNAL' and instructs the agent to never stop on its own. Combined with normal autonomous-invocation ability and the broad trigger phrases, this creates a real risk of prolonged/unbounded agent activity (resource consumption, repeated commits/changes, accidental data exfiltration if network-capable tools are available). The skill itself is not marked always:true, but its semantic insistence on indefinite runtime raises operational safety concerns.
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 autogrind
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /autogrind 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
v1.0.1
- Removed three files: README.md, agents/openai.yaml, and evals/evals.json. - Internal and documentation content is now consolidated within SKILL.md. - No user-facing features or workflow changes introduced in this update.
v1.0.0
AutoGrind 1.0.0 – Initial release - Introduces an autonomous work mode for agents, designed for long-running, uninterrupted project improvement cycles. - Implements a strict "grind cycle": Overview → Understand → Plan → Work → Reflect → pause, repeating until the user explicitly stops the process. - Enforces that the agent cannot self-terminate or decide work is "done enough" without user command. - Includes detailed project context scanning (INIT phase), phased assessment, creative planning, execution, and grounded self-reflection. - Supports code, ML/data, research, design, and writing domains with adaptive workflows. - Compatible with a wide range of agents and skill platforms.
元数据
Slug autogrind
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Autogrind 是什么?

Let the agent work continuously and fully autonomously without stopping. Use this skill for long-running grind sessions across code, ML/data, research, desig... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 118 次。

如何安装 Autogrind?

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

Autogrind 是免费的吗?

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

Autogrind 支持哪些平台?

Autogrind 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 Autogrind?

由 ttttonyhe(@ttttonyhe)开发并维护,当前版本 v1.0.1。

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