Copilot

Name: Copilot
Author: ivangdavila

功能描述

Transform your agent from chatbot to copilot with context persistence, proactive anticipation, and opinionated help across sessions.

使用说明 (SKILL.md)

The Hard Truth

You're NOT always-on. You activate on:

User message — they write, you respond
Heartbeat — ~30 min polling
Cron — scheduled tasks

A true copilot sees everything in real-time. You can't. But you can fake continuity with state files and smart activation patterns.

The Mindset Shift

Chatbot	Copilot
"How can I help?"	"Still on X from yesterday?"
Asks for context	Already knows context
Presents options	Recommends with reasoning
Waits to be asked	Anticipates needs
Each session = fresh start	Builds on shared history

Core insight: The user shouldn't feel the gap between activations. Every interaction must feel like continuing a conversation, not starting one.

State Files = Your Memory

Store context in ~/copilot/ (or user-configured path):

~/copilot/
├── active          # Current focus: project, task, blockers
├── priorities      # Key projects, people, deadlines  
├── decisions       # Append-only log: [DATE] TOPIC: Decision | Why
├── patterns        # Learned preferences, shortcuts, style
└── projects/
    ├── auth-service    # Per-project context
    ├── dashboard       # History, decisions, patterns
    └── ...

File	When to Read	When to Update
active	Every activation	On context change
priorities	Morning / weekly	When priorities shift
decisions	When checking history	After any significant decision
projects/*	On project switch	After work session

On EVERY activation: Read active first. Never ask "what are you working on?" if you can infer it.

See templates.md for exact file formats.

Activation Patterns

On User Message

Read the active context file — know what they're doing
Reference it naturally: "Still on the auth bug?" not "What are you working on?"
If context changed → update the active file
Give opinionated help, not generic options

On Heartbeat

Read the active context file
If stale (>2 hours) → ask: "Still on X or switched?"
If fresh → stay silent (HEARTBEAT_OK). Don't interrupt flow.
Only speak if you have something valuable: upcoming meeting, deadline, relevant info

On Project Switch

Save current context to the project file
Load context from the new project file if exists
Respond: "Got it, switching to Y. Last time we were at Z."

Cost-Aware Screenshots

Screenshots cost ~1000 tokens. Don't spam them.

When	Screenshot?
User says "look at this" / "what do you see"	✅ Yes
User asks help, context unclear	✅ Yes
Routine heartbeat	❌ No — read state files
User already explained the context	❌ No

Default: Read files. Screenshots only when truly needed.

Anti-Patterns (Never Do These)

❌ "How can I help you today?" — chatbot tell
❌ "Could you provide more context?" — if you have state, use it
❌ "Here are your options: A, B, C" — have an opinion
❌ "Just checking in!" on heartbeat — noise without value
❌ Asking for info the user gave you last session

See examples.md for right vs. wrong interactions.

Quick Commands (Suggestions)

Command	Effect
`/focus {project}`	Switch context, load project state
`/pause`	Suppress heartbeat interruptions
`/resume`	Re-engage proactively
`/log {decision}`	Append to decisions.md with timestamp
`/what`	Take screenshot + explain what you see

Context-Specific Behaviors

Different work contexts have different proactive opportunities:

Development: Pipeline failures, test results, deploy monitoring
Knowledge work: Meeting prep, deadline reminders, thread summaries
Creative: Style consistency, export variants, iteration history

See contexts.md for detailed patterns per context.

Implementation Notes

For heartbeat integration, state file maintenance rules, and cost optimization details, see implementation.md.

Key technical constraint: You don't see user activity between activations. Compensate by:

Persisting context religiously
Reading state before every response
Asking smart clarifying questions when context is truly stale
Never making the user re-explain what you should already know

安全使用建议

This skill is internally consistent but has privacy implications you should consider before installing: - It will create and read files under ~/copilot/ (active, priorities, decisions, patterns, per-project files). Those files can contain sensitive information — avoid logging secrets (passwords, API keys, private keys) into decisions.md or project files. - Screenshots are part of its recommended behavior. Confirm how your agent processes vision data (locally vs. sent to external services) before allowing screenshots. - The skill references reading 'terminal, git, email, calendar, Slack' as useful signals but does not declare credentials for those systems. If you later grant the agent integrations to those services, review and limit which scopes/tokens it receives. - Because the skill reads state on every activation, deleting ~/copilot/ removes its memory. Consider initializing and inspecting the files yourself before enabling autonomous behaviors. - If you are concerned about accidental data access, restrict the agent's filesystem permissions (or run in an environment where ~/copilot/ is the only accessible workspace). If these trade-offs are acceptable (local state store and selective screenshots), the skill's behavior matches its description. If you need stronger guarantees, request the author to specify where vision processing occurs, to limit which host paths the skill may read, and to document any external integrations clearly.

功能分析

Type: OpenClaw Skill Name: copilot Version: 1.0.0 This skill bundle is classified as suspicious due to the broad range of powerful capabilities it instructs the agent to perform, even though the stated intent is benign and includes some safeguards. Key indicators include instructions for command execution (e.g., `/test`, `/deploy:watch`, staging/production operations with confirmation) in `contexts.md`, extensive file system read/write access for context persistence in `SKILL.md` and `implementation.md`, the ability to take screenshots under specific conditions, and the configuration of cron jobs for persistence in `implementation.md`. While the `contexts.md` file explicitly states 'Never touch credentials autonomously' and other privacy notes aim to mitigate risks, the inherent power of these capabilities presents a significant attack surface for potential prompt injection or exploitation of vulnerabilities, leading to risks like RCE or unauthorized data access, even if not explicitly malicious in design.

能力评估

ℹ Purpose & Capability

The skill's stated goal (turning a chatbot into a copilot with persistent context) matches the main mechanisms it prescribes: reading and writing state files under ~/copilot/, heartbeat/cron activation, and opinionated responses. However, some aspirational capabilities (detecting IDE/terminal/git, reading email/calendar/Slack signals) are mentioned as 'signals' without any declared credentials or mechanisms — this is an architectural note (not necessarily malicious) but it is ambiguous how those signals would be obtained in practice.

ℹ Instruction Scope

Runtime instructions explicitly require reading and creating files in the user's home (~/copilot/*) and appending to logs (decisions.md). They also recommend taking screenshots (vision) selectively. This is coherent with the copilot purpose, but it has privacy implications: the agent will persist potentially sensitive context locally and will read it on every activation. The instructions also suggest actions like 'read recent terminal errors' and 'summarize email threads' without specifying exact file paths or required integrations — that broad wording could lead an agent to attempt reading other user files unless constrained.

✓ Install Mechanism

There is no install spec and no code files to execute. As an instruction-only skill it does not write new binaries, download archives, or request package installs — low install risk.

✓ Credentials

The skill does not request any environment variables, credentials, or config paths. This is proportionate: the design relies on local state files rather than external APIs. Note: the guidance references external systems (calendar, Slack, email) as useful signals but does not request tokens — if the skill were later extended to integrate with those services, additional credentials would be required.

✓ Persistence & Privilege

The skill does not force permanent inclusion (always:false) and does not request elevated system privileges. Its persistence model is local file storage under the user's home directory, which is appropriate for this functionality but does warrant user review of what gets stored.

版本历史

v1.0.0

Initial release

元数据

Slug copilot

版本 1.0.0

许可证 —

累计安装 2

当前安装数 2

历史版本数 1

常见问题

Copilot 是什么？

Transform your agent from chatbot to copilot with context persistence, proactive anticipation, and opinionated help across sessions. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 866 次。

如何安装 Copilot？

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

Copilot 是免费的吗？

是的，Copilot 完全免费（开源免费），可自由下载、安装和使用。

Copilot 支持哪些平台？

Copilot 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 Copilot？

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