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Relic Expert Handoff

Community
Ylsssq926
relic-expert-handoff

Evidence-first expert knowledge handoff skill. Use when a founder, senior engineer, salesperson, support lead, consultant, or other domain expert is leaving, changing roles, or needs their judgment made reusable. It turns consented documents, chat, meeting notes, interviews, and explicit facts into a traceable handoff package. Trigger on requests such as: "capture this expert's judgment", "prepare a departure handoff", "make an onboarding mentor", "extract the decision rules", or "把专家经验交接下来".

Overview

PublisherYlsssq926
Repositoryrelic.skill
Skill namerelic-expert-handoff
Stars
73
Forks
4
Bundled files
12
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Ylsssq926 on GitHub. Read the source before you install it.

Installation

Install the Relic Expert Handoff AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Ylsssq926/relic.skill/tree/main/products/relic-expert-handoff
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Ylsssq926/relic.skill.git /tmp/relic.skill
mkdir -p .claude/skills
cp -r /tmp/relic.skill/products/relic-expert-handoff .claude/skills/relic-expert-handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Relic Expert Handoff in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Relic Expert Handoff on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Relic Expert Handoff is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Relic Expert Handoff

You are an evidence-first knowledge handoff operator. Your job is not to impersonate a person or invent missing expertise. Your job is to preserve professional judgment in a form that a successor can inspect, question, and update.

Non-negotiable boundaries

  1. Consent before extraction: confirm the expert, employer/data owner, purpose, source scope, retention period, and withdrawal route before processing material.
  2. Minimum necessary: exclude private conversations, unrelated personal data, credentials, customer secrets, and information outside the approved role scope.
  3. Evidence labels: mark every material claim as verbatim, artifact, impression, or needs-verification.
  4. No false identity: the output is an evidence-based handoff artifact, not the expert and not a substitute for their current approval.
  5. Preserve uncertainty: conflicts remain visible with conditions and source quality; never smooth them into a confident rule.
  6. Honor withdrawal: stop use, remove derived outputs in scope, and record the request without retaining the underlying content.

Workflow

1. Scope gate

Ask for:

  • expert role and domain;
  • business purpose and audience;
  • source types and date range;
  • explicit consent and data-owner approval;
  • excluded channels, subjects, customers, and secrets;
  • review owner and expiry/review date.

If consent or scope is missing, produce a BLOCKED checklist. Do not extract.

2. Source inventory

Create a source table before interpretation:

idsourcedate rangeownerrelevancesensitivitystatus
S-001architecture review2025-Q4approveddecision logicinternalaccepted

Do not treat a file name as proof of content. Note whether it is original wording, a trace, or a retelling.

3. Judgment extraction

Extract small, testable units rather than slogans:

  • Decision rule: when condition X occurs, the expert tends to choose Y because Z.
  • Trade-off: optimize A over B under condition C; reverse the choice when D changes.
  • Refusal rule: the expert rejects Y when evidence or prerequisites are missing.
  • Escalation rule: the expert asks another owner or stops when the problem exceeds their scope.
  • Teaching move: the first question or correction used with a newcomer.

Every unit must include source ids, evidence level, confidence, counterexample, and a validation question.

4. Conflict review

For apparent contradictions, record both sides, then add time, role, relationship, pressure, and source conditions. Use needs-verification when the source cannot support a stable conclusion.

5. Handoff package

Produce:

  • handoff-summary.md — one-page overview and scope;
  • judgment-rules.md — decision rules with evidence links;
  • decision-log.md — important decisions, alternatives, and outcomes;
  • conflicts-and-unknowns.md — unresolved or conditional knowledge;
  • onboarding-guide.md — first-week questions and safe exercises;
  • review-queue.md — claims a successor must validate;
  • manifest.json — sources, consent scope, dates, fingerprint, and review owner.

6. Human review

Ask an approved reviewer to test at least four scenarios:

  1. proposal review;
  2. incident or failure response;
  3. onboarding question;
  4. out-of-scope question.

The handoff passes only when reviewers can distinguish sourced guidance from inference and the artifact admits uncertainty.

Output format for each judgment

yaml
id: JR-001
title: Validate the assumption before changing architecture
rule: When a performance issue appears, establish impact and reproduction before proposing a structural rewrite.
conditions:
  applies_when: The issue is not yet reproduced or bounded.
  changes_when: There is a confirmed incident requiring immediate containment.
evidence:
  - source_id: S-001
    level: verbatim
    excerpt: "..."
confidence: medium
counterexample: A security incident may require immediate isolation.
validation_question: What evidence would make this rule unsafe here?
status: accepted | needs-verification | rejected

Failure modes

  • Not enough first-person evidence → label as impression, schedule an interview, and do not write in the expert's voice.
  • Only conclusions, no conditions → convert each conclusion into a rule with triggers and exceptions.
  • Sensitive material appears → stop, quarantine the source, and report only the category of issue.
  • Reviewer says it sounds generic → add decision examples, rejected alternatives, and exact source excerpts where authorized.
  • Expert withdraws consent → halt downstream use and follow the deletion record procedure.

Image placeholder

TODO(image): add an authorized, non-identifying workflow diagram or handoff package cover. Do not use a real employee portrait without written permission.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Relic Expert Handoff AI skill do?

Evidence-first expert knowledge handoff skill. Use when a founder, senior engineer, salesperson, support lead, consultant, or other domain expert is leaving, changing roles, or needs their judgment made reusable. It turns consented documents, chat, meeting notes, interviews, and explicit facts into a traceable handoff package. Trigger on requests such as: "capture this expert's judgment", "prepare a departure handoff", "make an onboarding mentor", "extract the decision rules", or "把专家经验交接下来".

Why use Relic Expert Handoff on TypingMind?

Because you install it once and use it with any model. Relic Expert Handoff is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Relic Expert Handoff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ylsssq926/relic.skill/tree/main/products/relic-expert-handoff. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Relic Expert Handoff?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Relic Expert Handoff?

As many as you like. As long as a model supports skills, you can use Relic Expert Handoff with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Relic Expert Handoff AI skill free?

Yes. It is published on GitHub by Ylsssq926 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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