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personal-profile-generator

Generate a personal work profile from a user's own Feishu/Lark documents, calendar or meeting traces, chat history, persistent memory, and prior profile records. Suitable for creating, analyzing, updating, or storing a personal work portrait, career profile, work style summary, growth direction, strengths positioning, or hand-drawn style profile infographic based on collaboration data. Supports portable Feishu/Lark CLI workflows for different users, tenants, and profiles.
Generate a personal work profile from a user's own Feishu/Lark documents, calendar or meeting traces, chat history, persistent memory, and prior profile records. Suitable for creating, analyzing, updating, or storing a personal work portrait, career profile, work style summary, growth direction, strengths positioning, or hand-drawn style profile infographic based on collaboration data. Supports portable Feishu/Lark CLI workflows for different users, tenants, and profiles.
MeMeShun
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概述

Personal Profile Generator

Purpose

Generate an evidence-backed personal work profile by collecting multi-source work signals from the current user's authorized workspace, extracting concise traits, saving reusable profile memory when possible, and delivering a readable report plus an optional infographic prompt.

It is designed for portable Feishu/Lark CLI workflows across different users, tenants, and workspaces.

Workflow

  1. Establish the user's environment:
    • Locate the available Feishu/Lark CLI command.
    • Check configured profiles and the current authorized user.
    • Never assume a hardcoded profile, tenant, app, local path, or person.
  2. Confirm scope only if missing or risky:
    • Target person: default to the current authorized Feishu/Lark user for the selected profile.
    • Time range: default to the last 30 days.
    • Output: default to a markdown report; add infographic prompt unless the user asks for image generation.
  3. Collect evidence from available sources:
    • Feishu documents and Wiki pages edited, created, searched, or referenced by the user.
    • Calendar events, meeting titles, meeting notes, and minutes if accessible.
    • Feishu IM groups and messages where permitted.
    • Local persistent memory files and prior profile records if present.
  4. Build an evidence ledger before judging:
    • Record source, date, short quote or paraphrase, inferred signal, and confidence.
    • Separate observed facts from interpretation.
    • Prefer repeated signals across sources over one-off anecdotes.
  5. Derive three core labels:
    • 工作画像: 7 Chinese characters or fewer.
    • 工作风格: 7 Chinese characters or fewer.
    • 成长方向: 10 Chinese characters or fewer.
  6. Generate the report:
    • Load references/profile-schema.md.
    • Follow its report sections, core trait constraints, evidence ledger, quality bar, and save format.
    • For Chinese-facing maintenance, use references/zh-CN-output-spec.md as the Chinese companion spec.
  7. Persist the result when a persistence mechanism is available:
    • Prefer the user's existing profile store or memory convention.
    • If no store exists, create/update a local JSON file only when the user asked for a saved artifact.
  8. Optionally create or prompt an infographic:
    • Use a hand-drawn, warm, structured information-card style.
    • Include the three labels, capability tags, evidence highlights, representative scenarios, and track advice.
    • If the user asks to generate the image directly, load references/infographic-generation.md, build the final image prompt from the profile, then call the image generation tool.

Evidence Rules

  • Do not invent inaccessible data. Say which sources were unavailable or unauthorized.
  • Do not request broad permissions silently. Explain missing scopes and ask before initiating a new authorization flow.
  • Do not overfit on a single document, meeting, or message.
  • Keep sensitive raw chat excerpts minimal; paraphrase unless the exact wording matters.
  • Treat profile labels as hypotheses grounded in evidence, not fixed judgments.
  • When data is thin, produce a "low-confidence draft" and list the missing evidence needed.

Feishu CLI Collection

When the task involves Feishu/Lark data, read references/feishu-cli.md for portable command patterns, profile discovery, authorization, and scope checks.

Reference Routing

Load reference files on demand for the current task; do not load every reference by default.

  • references/feishu-cli.md: portable Feishu/Lark CLI setup, profile selection, authorization, and data collection.
  • references/profile-schema.md: final report sections, core trait constraints, evidence ledger, save format, and infographic data contract.
  • references/infographic-generation.md: hand-drawn image, visual card, poster, infographic, or profile map generation requirements.

Chinese reference versions are available for maintainers and Chinese-speaking users:

  • references/zh-CN-overview.md: Chinese usage overview and workflow.
  • references/zh-CN-feishu-cli.md: Portable Feishu/Lark CLI setup and data collection guide.
  • references/zh-CN-output-spec.md: Chinese output schema, quality bar, and infographic prompt guide.

版本历史

共 1 个版本

  • v1.0.0 Initial release 当前
    2026-05-27 16:35 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

安全,无风险
查看报告

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