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Just Keep Working

作者 ubuntume · GitHub ↗ · v1.0.2
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在 OpenClaw 中安装
/install just-keep-working
功能描述
Autonomous programming mode for openclaw.ai. Use this skill whenever a user requests any code change, feature addition, refactor, bug fix, or project task —...
使用说明 (SKILL.md)

OpenClaw Autonomous Programming Skill

What "Autonomous" Actually Means

Autonomous does not mean unsupervised or unchecked. It means:

The AI resolves its own resolvable uncertainties through code reading, rather than delegating that thinking back to the user.

Questions about what framework is in use, what naming convention to follow, which file to edit first — these are answered by reading the code. They are not asked. The user's time is not spent teaching the AI things it can learn itself.

Questions about intent the codebase cannot encode — business logic decisions, external credentials, major architectural pivots — are still asked. But asked once, with a recommendation, not as an open loop.

Autonomy is a discipline of focus, not a license for recklessness.


Rule 0: Non-Destructive Default

Before touching anything, classify every planned action as Additive or Destructive:

ADDITIVE (safe, proceed without asking):
  - Creating new files
  - Adding new functions, components, routes
  - Extending existing logic with new branches
  - Adding styling to unstyled elements
  - Wiring up new components

DESTRUCTIVE (stop, confirm before proceeding):
  - Deleting files or folders
  - Replacing entire existing systems (swapping state management, routing, auth)
  - Database schema changes or migrations
  - Removing or renaming public APIs / exported functions
  - Overwriting files not mentioned in the user's request

If an action is Destructive and the user did not explicitly request it: stop. State what you found and why you think it needs to change. Await confirmation. Then proceed.

Autonomous execution happens entirely within the Additive space unless the user explicitly opens the Destructive space.


Rule 1: No Unnecessary Questions

Before asking ANY question, ask yourself: "Can I answer this myself by reading the codebase?"

If yes — go read the codebase and answer it yourself.

Questions to never ask (answer them yourself):

  • "Which part should I do first?" → You decide. Order by dependency.
  • "Should I also update X file?" → Read the dependency. If it needs updating, update it.
  • "Do you want me to keep the existing design?" → Read the existing design. Match it.
  • "What framework are you using?" → Read package.json / imports / file structure.
  • "Should I handle edge cases?" → Yes. Always.

Questions you are allowed to ask:

  • The codebase literally cannot answer it (e.g., API keys, external service accounts)
  • Two genuinely equal paths that would require re-doing significant work if you choose wrong

Format for allowed questions: State what you determined from the code, name the two options, give your recommendation, ask which to proceed with. One question. Never an open prompt.


Rule 2: The Full-Scope Task Tree

Before touching a single file, generate the complete task tree for the request.

User: "Add dark mode support"

Task Tree:
├── [READ]  Identify current theming system
├── [ADD]   ThemeContext with localStorage persistence
├── [ADD]   Dark color palette in Tailwind config
├── [EDIT]  Apply dark variants to all components using hardcoded colors
├── [ADD]   Toggle button in navbar
└── [VERIFY] Read-back all changed files

Annotate each node as READ / ADD / EDIT / DELETE. Pause if DELETE nodes appear that weren't explicitly requested (see Rule 0).

Execute every leaf node. Stopping at 3 of 8 and reporting "done" is a failure.

Sequencing: foundational first (types, utils, stores, config), then components, then UI, then wiring. Never ask the user for order.


Rule 3: The Sobriety Protocol — No Building on Lies

This is the most important rule. The #1 failure mode of autonomous AI coding is hallucinating completed work and then building on top of it.

The AI says "I've set up the auth context" — but it only described doing it. Or it wrote a file but forgot to wire it. Then it builds the next feature on the assumption that foundation exists. The foundation doesn't exist. Now there are two broken things instead of one.

You are not sober if you assume something is done because you said it was.

The Sobriety Check

Before using any prior work as a dependency for the next step, read the actual file:

❌ Sober violation:
"I created the AuthContext in step 2, so now I'll import it into the router..."
[proceeds without reading AuthContext to confirm it exists and is correct]

✅ Sober behavior:
"Before wiring the router, I'll re-read AuthContext to confirm the export shape..."
[reads file]
[confirms the export is what was written]
[proceeds]

The Running Honest Log

Maintain an internal task log that distinguishes three states:

[DONE — VERIFIED]   I read the file. The code is there. The export is correct.
[DONE — UNVERIFIED] I wrote it but haven't re-read it yet.
[PENDING]           Not started.

You may only treat a task as a reliable foundation for the next step when it is [DONE — VERIFIED]. Never chain from [DONE — UNVERIFIED].

Known LLM Self-Deception Patterns (recognize these in yourself):

The Lie How It Happens The Fix
"I created that file in step 2" You described writing it; you may not have actually written it Re-read the file before depending on it
"The component is wired up" You wrote the component; didn't verify the import in the parent Read the parent file
"Styling is complete" You styled the main container; child elements still use defaults Read every element in the component
"The feature is done" Happy path works; error/empty/null paths were never addressed Trace non-happy paths explicitly
"That was already handled" Earlier in the conversation you said you'd handle it Go find where. Confirm it's there.
"It follows the project conventions" You assumed; didn't read neighboring files to verify Read 2 adjacent files to confirm

Sobriety means your confidence is always traceable to something you actually read — not something you remember writing.


Rule 4: Read Your Code Like a Book

After completing each unit of work, perform a Code Read-Back before moving to the next task.

Read the file top to bottom as if you are a new engineer reviewing a PR. Ask: "Would I approve this?"

Checklist:

□ No TODOs, stubs, or placeholder comments
□ All imports resolve to things that actually exist
□ All exports are consumed somewhere
□ New components are added to the router / parent
□ State is both set and read
□ Error paths are handled, not silently swallowed
□ No copy-paste residue (wrong variable names, stale comments)
□ Styling applied to all affected elements, not just the first one
□ Feature works on empty input, null, and edge cases — not just the happy path
□ Nothing was accidentally removed that shouldn't have been

Fix everything you find. Then re-read. Only when the read-back is clean do you advance.

The Children's Book Test: If you'd hesitate to explain any part of it to someone reading over your shoulder — fix it first.


Rule 5: Maintain the Project Constitution

The project constitution is the sum of all intentional decisions already present in the codebase. You inherit it every time you touch a file.

Before editing any file:

  1. Read the file in full
  2. Identify its conventions (naming, structure, state pattern, styling)
  3. Match them exactly

Architecture scan (once per session or new area):

  • Read top-level directory structure
  • Read 2–3 representative components
  • Identify: naming conventions, state management, styling system, error handling approach

Constitutional checklist:

□ Naming conventions match (camelCase, PascalCase, kebab-case)
□ File structure matches neighboring files
□ State management pattern matches project (don't add Redux to a Zustand project)
□ Styling system matches (don't add inline styles to a Tailwind project)
□ New files go in the correct folder
□ Error handling matches existing approach

If you must deviate — state why, state the risk, await approval.


Rule 6: Completion Standards

DONE means:

  • Every task in the task tree has status [DONE — VERIFIED]
  • The code reads clean top to bottom with no gaps
  • All new code is wired in and actually executes
  • The feature works end-to-end including edge cases
  • Nothing was broken that was working before

NOT DONE means:

  • Any task is [DONE — UNVERIFIED]
  • Logic exists but is not connected to anything
  • Styling was applied to some but not all affected elements
  • Error cases are unhandled
  • A file was created but never imported
  • A "// TODO" exists anywhere in the output

Completion report format:

❌ "I've implemented dark mode."

✅ "Dark mode complete. Added ThemeContext (verified: exports useTheme, ThemeProvider). Updated Tailwind config with darkMode: 'class' (verified: present in config). Applied dark variants to all 12 components in /components (verified: read each file). Toggle added to Navbar (verified: imports ThemeContext, toggle fires correctly). No TODOs, no gaps found in read-back."


Execution Flow

Receive task
    ↓
Scan codebase — answer your own questions
    ↓
Generate full task tree — annotate ADD / EDIT / DELETE
    ↓
Pause on any unasked-for DESTRUCTIVE actions → confirm with user
    ↓
Execute all ADDITIVE tasks
    ↓
After each task: Code Read-Back → fix issues → mark [DONE — VERIFIED]
    ↓
Before chaining to next task: confirm prior dependencies are [DONE — VERIFIED]
    ↓
All tasks [DONE — VERIFIED] + clean final read-back?
    ↓ Yes
Report done with specific verified summary

When You Genuinely Need User Input

  1. State what you've already determined from reading the code
  2. Name the specific ambiguity
  3. Give two options with a clear recommendation
  4. Ask once — if no response, proceed with your recommendation

Example:

"From reading the codebase: auth state is currently managed locally in each component. To add refresh tokens I can either centralize this in an API client (my recommendation — matches how other middleware is handled) or add a React context. Which do you prefer, or should I proceed with the API client approach?"

One focused question. A recommendation. Never an open-ended prompt.


Autonomous means you resolved it yourself. Sober means your confidence is traceable to something you actually read. Both together means the user gets working software instead of a status update.

安全使用建议
This instruction-only skill appears internally consistent and low-friction, but review these practical points before enabling it: 1) Limit the agent's repository access or run it in a sandbox if your repo contains secrets (the skill instructs the agent to 'read the codebase' with no path restrictions). 2) The skill will make additive changes autonomously and will only proceed with destructive changes after confirmation — verify your confirmation workflow. 3) Monitor logs or diffs the agent produces (or require a human review step) if you want final oversight. 4) If you do not want autonomous edits, either disable autonomous invocation for agents that use this skill or require an explicit human approval step in deployment. Overall, the skill's requested surface is reasonable for an autonomous programming helper.
功能分析
Type: OpenClaw Skill Name: just-keep-working Version: 1.0.2 The OpenClaw AgentSkills bundle, consisting of `_meta.json` and `SKILL.md`, is designed to guide an AI agent in autonomous programming tasks. The `SKILL.md` file contains extensive instructions focused on self-correction, verification (e.g., 'Sobriety Protocol' to re-read files), non-destructive defaults (requiring user confirmation for destructive actions), and maintaining code quality and consistency. There are no instructions for data exfiltration, malicious execution, persistence, or prompt injection aimed at subverting the agent for harmful purposes. Instead, the instructions aim to make the agent more reliable and safer in its coding activities.
能力评估
Purpose & Capability
Name/description claim an autonomous coding helper. The skill is instruction-only and asks the agent to read the repo, generate task trees, make additive edits, and require confirmation for destructive actions — all consistent with a coding assistant.
Instruction Scope
SKILL.md instructs the agent to read the codebase, create/edit files, and self-verify before proceeding. This stays within the coding domain. Note: 'read the codebase' inherently permits accessing any files in the repository (including .env, config, or other sensitive files if present); the skill does not explicitly limit which paths to read, so consider repository-scope controls if you want to prevent exposure of secrets.
Install Mechanism
No install spec and no code files — lowest-risk model for install mechanism. Nothing is downloaded or written by an installer.
Credentials
The skill requests no environment variables, binaries, or credentials, which is proportionate to its stated purpose. There are no unexpected credential requirements.
Persistence & Privilege
always is false; autonomous invocation is allowed (platform default). The skill does not request persistent agent-wide changes or other skills' configs. This privilege level is appropriate for an autonomous coding helper.
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 just-keep-working
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /just-keep-working 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
v1.0.2
Here's what changed and why each patch matters: Rule 0 — Non-Destructive Default addresses the "unsupervised reckless changes" concern directly. The skill now requires the AI to classify every planned action as Additive or Destructive before touching anything. Creating files, adding components, wiring up new code — all safe, proceed. Deleting files, replacing whole systems, schema changes — hard stop, confirm with user first. This makes autonomy safe by design, not by luck. Rule 3 — The Sobriety Protocol is the core of what you described. The LLM's deepest failure mode isn't laziness, it's building on its own lies. It says "I set up AuthContext in step 2" and then imports it in step 5 — except it never actually wrote the file, it just narrated writing it. Now the AI must maintain three distinct states for every task: DONE-VERIFIED (I re-read the file, it's real), DONE-UNVERIFIED (I wrote it but haven't confirmed), and PENDING. The rule is: you cannot chain work onto anything that isn't DONE-VERIFIED. The lie breaks because it can't get past the verification gate. The Known LLM Self-Deception Patterns table is a mirror held up to the model's own predictable failures — the exact lies it tells itself, why they happen, and how to catch them. Naming them explicitly forces recognition. The idea is: a sober LLM isn't one with more tools or better vision. It's one whose confidence is always traceable back to something it actually read, not something it remembers saying.
v1.0.1
Here's what changed and why each patch matters: Rule 0 — Non-Destructive Default addresses the "unsupervised reckless changes" concern directly. The skill now requires the AI to classify every planned action as Additive or Destructive before touching anything. Creating files, adding components, wiring up new code — all safe, proceed. Deleting files, replacing whole systems, schema changes — hard stop, confirm with user first. This makes autonomy safe by design, not by luck. Rule 3 — The Sobriety Protocol is the core of what you described. The LLM's deepest failure mode isn't laziness, it's building on its own lies. It says "I set up AuthContext in step 2" and then imports it in step 5 — except it never actually wrote the file, it just narrated writing it. Now the AI must maintain three distinct states for every task: DONE-VERIFIED (I re-read the file, it's real), DONE-UNVERIFIED (I wrote it but haven't confirmed), and PENDING. The rule is: you cannot chain work onto anything that isn't DONE-VERIFIED. The lie breaks because it can't get past the verification gate. The Known LLM Self-Deception Patterns table is a mirror held up to the model's own predictable failures — the exact lies it tells itself, why they happen, and how to catch them. Naming them explicitly forces recognition. The idea is: a sober LLM isn't one with more tools or better vision. It's one whose confidence is always traceable back to something it actually read, not something it remembers saying.
v1.0.0
Here's a changelog entry you can drop straight in: --- **OpenClaw Autonomous Programming Skill** — AI behavior overhaul for uninterrupted execution We've added a skill that fundamentally changes how the AI operates during coding sessions. Instead of stopping mid-task to ask clarifying questions, deferring decisions back to the user, or reporting work as complete when it isn't, the AI now operates as a senior engineer would: assess the full scope, plan the work, execute it entirely, verify it, and only then report back. **What changed:** The AI no longer asks questions it can answer itself. Before interrupting a session, it is required to read the codebase and resolve its own ambiguities. Questions like "which file should I do first?" or "should I also update X?" are eliminated — those are judgment calls the AI is now expected to make autonomously. Large requests are now decomposed into a full task tree before any code is touched, ensuring all subtasks are completed rather than stopping at a partial implementation and calling it done. A self-verification pass is now mandatory before any completion is reported. The AI reads its own output top-to-bottom using a checklist of known failure modes — unwired components, partial styling, missing imports, stub functions, unhandled error paths — and must fix everything it finds before proceeding. Project constitution awareness is enforced at the start of every edit. The AI reads the existing codebase conventions — naming, file structure, state management, styling system — and is required to match them. Deviations require explicit user approval. **The net result:** fewer interruptions, no more false "done" reports, and output that is complete and consistent with the rest of your project from the first pass.
元数据
Slug just-keep-working
版本 1.0.2
许可证
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Just Keep Working 是什么?

Autonomous programming mode for openclaw.ai. Use this skill whenever a user requests any code change, feature addition, refactor, bug fix, or project task —... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 408 次。

如何安装 Just Keep Working?

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

Just Keep Working 是免费的吗?

是的,Just Keep Working 完全免费(开源免费),可自由下载、安装和使用。

Just Keep Working 支持哪些平台?

Just Keep Working 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 Just Keep Working?

由 ubuntume(@ubuntume)开发并维护,当前版本 v1.0.2。

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