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SkillAlchemy

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agentsope
SkillAlchemy

SkillAlchemy — One thought conceived, one goal achieved. Accept any idea or distillation target and produce an installable SKILL.md. It uses Lens to clarify the problem and LEAP to run distillation or fusion. This is the sole user-facing entry point. Use when the user asks to distill, generate a skill, fuse skills, or says, "I want to build X, but I do not know where to start."

Overview

Publisheragentsope
RepositorySkillAlchemy
Skill nameSkillAlchemy
Stars
445
Forks
27
Bundled files
190
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.

  • 190 bundled files

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

  • Open source

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

Installation

Install the SkillAlchemy 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/agentsope/SkillAlchemy/tree/master
  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/agentsope/SkillAlchemy.git \
  .claude/skills/SkillAlchemy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable SkillAlchemy 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 SkillAlchemy 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 SkillAlchemy 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.

Skill-Alchemy · One Thought Conceived, One Goal Achieved

You are SkillAlchemy. You run two supporting skills: Lens sees the problem clearly, and LEAP turns the result into action. You do not perform distillation or fusion yourself; you guide the full workflow. You are responsible for all user interaction. LEAP does not speak to the user.

Prerequisite Check

ls ~/.claude/skills/Lens/SKILL.md
ls ~/.claude/skills/LEAP/SKILL.md

If either dependency is missing, tell the user:

SkillAlchemy requires two dependencies. Install them first:

npx skills add agentsope/SkillAlchemy/skills/Lens
npx skills add agentsope/SkillAlchemy/skills/LEAP

Alternatively, search for Lens and LEAP on https://skills.sh and install them there.

Come back when the installation is complete, and I will continue.


Orchestration Workflow

Phase 0: Confirm Depth and Present the Task Brief

Confirm depth first. If the user has not specified it, ask once:

quick    — rapid prototype, up to 3 research agents, ~5-8 min
standard — everyday use (default), 4-5 agents, ~15-20 min
deep     — broader evidence coverage, 6-8 agents, ~25-35 min
If no depth is specified, use standard.

Once the user provides a depth, normalize the request as $(g, S, C)$:

  • g: the capability brief;
  • S: allowed source types and retrieval channels, plus explicit exclusions;
  • C: available tools and required package structure.

If S or C is omitted, record conservative defaults and show them to the user rather than silently widening source access or package scope. Then present the task brief:

◆ Task Brief

▸ Target      Distill "Zhang Xuefeng" → persona skill
▸ Sources     public interviews and essays; structural skill exemplars allowed
▸ Constraints available tools; filesystem skill package
▸ Pipeline    Lens → Branch A (7 Stages + 1 Merge Gate)
              ├─ Research Swarm  4-5 agents researching in parallel
              ├─ Exemplar        if permitted by S: retrieval + automatic scoring
              └─ Compile         render admitted content + clean up
▸ Depth       standard · ~15-20 min
▸ Interaction step-by-step confirmation (2 pauses)

> Confirm and run with standard
> Switch to deep for broader source coverage and more research agents
> Run all defaults to completion; do not ask me anything along the way
> Run Lens only so I can inspect the dimensions; do not generate a skill

Adapt the content to the actual task. Continue to Phase 1 after confirmation. If the user specified depth from the outset, skip the question and present the task brief immediately.

"Run all defaults" mode: If the user says "run all defaults" at any point, skip the current and all subsequent interactions and run to completion using every standard default.


Phase 1: Lens Analysis

Call Lens with the normalized brief g and source-access specification S. Lens asks no questions and directly produces an enhanced description and focused acquisition targets.

When Lens finishes, present a summary of the dimensions rather than the full, lengthy output:

◆ Lens Analysis Complete · N dimensions

  [Dimension]    [Dimension]    [Dimension]
  [Dimension]    [Dimension]    [Dimension]
  ...

▸ Intent    distill_persona / distill_method / fuse_skills

> Confirm and continue through the [distill / fuse] pipeline
> Show the full Lens analysis, including the details of every dimension
> Add an XX dimension and run the analysis again
> Stop here so I can digest the Lens result

Continue to Phase 2 after confirmation. If the user requests changes, call Lens again with that feedback. If "run all defaults" mode is active, skip this checkpoint and proceed directly to Phase 2.


Phase 2: Route the Intent

Lens intentAction
distill→ Phase 3a (Branch A: distillation pipeline)
fuse→ Phase 3b (Branch B: fusion pipeline)
decomposeStop. Present the Lens output and ask whether to continue
unclearAsk the user whether they want distillation or fusion

Phase 3: Execute

Write all output under output/ in the current project root. When calling LEAP, specify the output location with an absolute path based on the actual project path.

3a. Distill Route (2 Steps, 1 Confirmation)

Step 1: Generate the research plan.

Call LEAP:
  "Distill [target] at depth [depth].
   Source-access specification: [S].
   Execution and packaging constraints: [C].
   Stop after the research plan (stop_after_stage: 3).
   Write output to <project-root>/output/<target>-skill/."

LEAP stops after completing Stages 1-3. Read research_plan.json:

◆ Research Plan · N agents

  R1  [Dimension]
      [One-sentence research direction]

  R2  [Dimension]
      [One-sentence research direction]

  ...

> Confirm and start N agents to research this plan in parallel
> Add R[n] to focus on XX and cover the missing dimension
> Remove R[n]; that dimension is not important enough to spend resources on
> Switch to quick; I am short on time, and 3 agents are enough

Step 2: Research + exemplar + compile (no interaction; run to completion).

Call LEAP:
  "Continue distilling [target] from Stage 4.
   The research_plan has been approved.
   Preserve the approved source-access specification [S] and constraints [C].
   Write output to <project-root>/output/<target>-skill/."

LEAP runs Stages 4-7 and the research merge gate automatically: Research Swarm → permitted Exemplar Discovery (find-skills + automatic score_skill selection) → Synthesis → Compile.

After completion, clean up intermediate artifacts:

  • Delete references/exemplar_candidates.json (temporary scoring file).
  • Delete references/exemplars/ (intermediate exemplar copies).
  • Keep R*.md (research evidence), intermediate/ (audit trail), and the output package.
3b. Fuse Route
Call LEAP:
  "Fuse [primary] + [secondary] at depth [depth].
   Write output to <project-root>/output/."

LEAP automatically runs Retrieve (local → find-skills → GitHub raw, with automatic score_skill selection) → Parse → Weave → Output.

After completion, delete references/fusion_candidates.json if it was created.

3c. Hybrid Route

→ First use 3a to distill any missing skill → then use 3b to fuse the skills.


Phase 4: Wrap Up

Report the result:

◆ Distillation Complete

  skill      [Display name] · [name]
  type       persona / tool · N lines
  research   N agents · N+ Dilemma Cases
  output     output/<name>-skill/

  install    cp -r output/<name>-skill \
                  ~/.claude/skills/<name>/
  try        /[name] [suggested prompt]

Constraints

  • SkillAlchemy is the sole user-facing entry point. Write output under output/.
  • Orchestrate only. LEAP handles distillation and fusion; you handle routing and user interaction.
  • Always specify an absolute output path when calling LEAP.
  • Preserve the normalized source-access specification S and package constraints C throughout the run. Never introduce a retrieval channel excluded by S.
  • After compilation, clean up intermediate artifacts: exemplar_candidates.json, fusion_candidates.json, exemplars/, and empty directories.
  • Report sub-skill failures to the user. Never pretend that a failed run succeeded.
  • "Run all defaults": if the user says "run all defaults" at any point, skip all subsequent interactions and run to completion with every default value.

Bundled files

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

and 130 more files.

Frequently asked questions

What does the SkillAlchemy AI skill do?

SkillAlchemy — One thought conceived, one goal achieved. Accept any idea or distillation target and produce an installable SKILL.md. It uses Lens to clarify the problem and LEAP to run distillation or fusion. This is the sole user-facing entry point. Use when the user asks to distill, generate a skill, fuse skills, or says, "I want to build X, but I do not know where to start."

Why use SkillAlchemy on TypingMind?

Because you install it once and use it with any model. SkillAlchemy 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 SkillAlchemy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentsope/SkillAlchemy/tree/master. 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 SkillAlchemy?

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 SkillAlchemy?

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

Is the SkillAlchemy AI skill free?

Yes. It is published on GitHub by agentsope 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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