agentleFS
SonghongleiSonghonglei/better-agent-skills/skills/copy-my-profile/SKILL.md

copy-my-profile

Skill4 starsChanged 5 months agoMITView on GitHub

Extract a standardized cross-tool user profile from any AI agent's long-term memory (USER.md, MEMORY.md, AGENTS.md, CLAUDE.md, .cursorrules, etc.) so you can re-use it in another AI tool without retraining. Output is a portable Markdown document with five categories — instructions, identity, profession, projects, preferences. Works with Claude Code, OpenClaw, Codex CLI, Cursor, Cline, Continue, Aider, or any LLM agent. 适用场景:生成我的画像/导出 profile/ 拷贝到其他 AI 工具/跨工具同步偏好/换 agent 不丢记忆。

The file

Show its rules and text

We split this skill into the rules it gives an agent

13 rules. About 0 are common, 5 are distinctive, and 8 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 8

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run memory_search for topics like "instructions", "preferences", "projects"

    line 57
  2. Distinctive:

    Read key files based on the host tool:

    line 59
  3. Distinctive:

    Read the most recent N daily memory logs (if the tool keeps them) to capture recent context

    line 69
  4. Very specific:

    Ask the user to provide files or paste relevant content (see references/tools-memory-map.md for what to ask for)

    line 75
  5. Distinctive:

    If the user only supplies an old profile, normalize the format and note at the end: "Based on existing profile, not re-extracted from raw …

    line 77
  6. Distinctive:

    If a key file is missing, continue with what's available and list "missing sources" at the bottom

    line 82
  7. Very specific:

    If a category has zero data, output (no data) — don't skip the category

    line 84
  8. Very specific:

    If memory_search returns empty, fall back to Path B

    line 85
  9. Very specific:

    Instructions: Only items clearly identifiable as rules in memory files.

    line 89
  10. Very specific:

    Identity: Only non-sensitive info the user has actively shared.

    line 91
  11. Very specific:

    Write the profile to ./my-profile.md (current directory) and also reply to the user inline.

    line 98
  12. Very specific:

    Use [unknown] if the date is unclear

    line 102
  13. Distinctive:

    After the code block, add a brief note covering:

    line 109

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Copy My Profile

Generate a portable Markdown profile from one AI tool's long-term memory and re-use it in another. Solves the "I just trained Tool A to know me, now I want to use Tool B" problem without retraining.

Why this skill

Modern developers use multiple AI tools: Claude Code at home, Cursor at work, ChatGPT for brainstorming, OpenClaw for backend automation, etc. Each tool accumulates its own memory of who you are — but none of them talk to each other. This skill defines a portable Markdown format (like vCard for contacts, or ICS for calendars) so your "AI profile" can move with you.

  • Zero infrastructure: no cloud, no account, no sync server
  • Privacy-controlled: you see the full output before copying anywhere
  • Standard format: 5 categories that any LLM can parse reliably

Output categories (5 in order)

Detailed format examples and filtering rules live in references/profile-template.md (read it during step 2-3).

  1. Instructions — Explicit rules the user wants followed (tone, format, behavior corrections, ⚠️-marked rules in memory files)
  2. Identity — Name, education, interests, non-sensitive personal info
  3. Profession — Role, company, skill domains
  4. Projects — Projects the user actually built or invested effort in
  5. Preferences — Broadly-applicable work style and taste preferences

Execution steps

Step 1 — Pick execution path

Choose based on the current runtime environment.

Path A — Memory-search-capable environment (Claude Code, OpenClaw, Codex CLI, or any agent that exposes a memory_search / file-read tool):

  1. Run memory_search for topics like "instructions", "preferences", "projects"
  2. Read key files based on the host tool:
    • Claude Code: ~/.claude/CLAUDE.md, project CLAUDE.md
    • OpenClaw: USER.md, MEMORY.md, AGENTS.md, memory/*.md
    • Codex CLI: AGENTS.md, ~/.codex/instructions.md
    • Cursor: project .cursorrules, .cursor/rules/*.md
    • Cline: project .clinerules
    • Continue: ~/.continue/config.json (system message), workspace .continue/*.md
    • Aider: CONVENTIONS.md, .aider.conf.yml
    • See references/tools-memory-map.md for the full table.
  3. Read the most recent N daily memory logs (if the tool keeps them) to capture recent context

Path B — No memory-search tool (Cline without filesystem, web ChatGPT, fresh Cursor session, etc.):

  1. Ask the user to provide files or paste relevant content (see references/tools-memory-map.md for what to ask for)
  2. If the user only supplies an old profile, normalize the format and note at the end: "Based on existing profile, not re-extracted from raw memory"

Degradation strategy (both paths):

  • If a key file is missing, continue with what's available and list "missing sources" at the bottom
  • If a category has zero data, output (no data) — don't skip the category
  • If memory_search returns empty, fall back to Path B

Step 2 — Filter

  • Instructions: Only items clearly identifiable as rules in memory files; don't fabricate. Distinction rules in profile-template.md
  • Identity: Only non-sensitive info the user has actively shared. Privacy filter rules in profile-template.md
  • Projects: One line per project — function, status, key decisions
  • Preserve the user's original phrasing as much as possible

Step 3 — Output format

Write the profile to ./my-profile.md (current directory) and also reply to the user inline. Format:

  • Each line: [YYYY-MM-DD] - entry content
  • Use [unknown] if the date is unclear
  • Within each category, sort by date ascending
  • Wrap the entire profile in a single fenced Markdown code block (so the user can copy-paste in one click)

Step 4 — Closing notes

After the code block, add a brief note covering:

  • Whether all relevant info from current memory was included
  • Any categories that had no data or used degradation
  • Any dimensions or uncertain entries excluded (so the user can decide whether to add them)
  • Import hint: which file to paste this into for the target tool — see references/import-prompts.md for ready-to-use prompts

Tips for cross-tool transfer

  • Pull (export): Run this skill in your source tool → copy my-profile.md
  • Push (import): In the target tool, paste the profile with a prompt from references/import-prompts.md
  • The Markdown is plain text — works through clipboard, email, Notion, GitHub gist, file transfer, anything

See also

  • references/profile-template.md — full output format example + filtering rules + privacy rules
  • references/tools-memory-map.md — where each AI tool stores its memory (where to read FROM and where to write TO)
  • references/import-prompts.md — copy-paste prompts for importing the profile into each target tool

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.