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Skill Builder

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boshu2
skill-builder

Create, repair, audit or consolidate agent skills (SKILL.md packages). Use when: writing or fixing a skill, its description or structure. Not for one-off lessons; use Memory.

Overview

Publisherboshu2
Repositoryagentops
Skill nameskill-builder
Stars
450
Forks
42
Bundled files
31
LicenseApache-2.0
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.

  • 31 bundled files

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

  • Open source

    Published by boshu2 on GitHub. Read the source before you install it.

Installation

Install the Skill Builder 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/boshu2/agentops/tree/main/plugin/skills/skill-builder
  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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/skills/skill-builder .claude/skills/skill-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Builder 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 Skill Builder 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 Skill Builder 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 Builder

Create, repair, audit or export one canonical skill package, or turn supported expertise into a small authoring proposal.

Decide whether to create anything

Apply these before any creation step, including when the request already names the new skill or file it wants:

  1. Find the existing owner. Search skills, references, checklists and instruction files for one that already handles the behavior. Extend that owner rather than adding a root.
  2. Count independent occurrences. A new skill, gate, library or workflow needs three independently evidenced real occurrences and a successful reapplication to a source case without missing context. Fewer occurrences support only a narrow note in an existing owner; one incident is an observation, not a skill. An authoritative source supports only a faithful statement of that source.
  3. Name the consumer of every new artifact. A proposed process artifact (a report, ledger, counter, dashboard or tracker) needs a concrete consumer, the decision it informs, the observed defect it answers and a retirement condition. If any is missing, leave it out; code written only to consume it is no consumer.
  4. No action is a valid result, and so is a short addition to an existing owner. Say what the evidence supports before drafting, build only what the caller then authorizes, and return a proposal inline unless a durable one was requested.
  5. A built package proves no benefit. Route behavioral benefit to Skill Eval and acceptance judgment to Validate.

Choose the requested operation

NeedEntry point
Create a source packagescripts/build.sh with from-scratch, from-template or absorb-external
Check package structurescripts/heal.sh --check [--strict] skills/<slug>
Repair owned projectionsscripts/heal.sh --fix skills/<slug>
Inspect audit evidence`scripts/audit.sh [--profile canonical
Merge overlapping skillsConsolidate
Export to another platformConversion
Make repeated expertise reusableDistill expertise

Run only the selected operation. Skills remain optional tools within the native caller's authorized outcome; this skill does not add execution phases, own work, operate Git, validate a software candidate, or decide delivery and retries. Inputs, report destinations, exit codes and recovery from partial creation are in build and check mechanics.

Create and maintain

Treat external skills as structural signals only. Clean-room output must not copy their names, prose, prompts, scripts or examples. from-template reuses metadata defaults; absorb-external <slug> --from <path> verifies an input and creates a blank source package. Neither imports another skill's content.

scripts/build.sh creates one incomplete source package containing only SKILL.md with metadata.authoring_state: scaffold; helpers, references and assets are conditional on the actual behavior. Replace the placeholders and state applicability, inputs, authority, result, completion and failure in the layout that makes them clear. An ending such as "hand off" or "then stop" can end the caller's turn when the skill runs inline; read endings before writing one. Remove the scaffold state only after authoring the behavior; strict source checks reject it. Removing it is an author assertion, not proof of semantic completeness.

Before editing a skill after a miss or a review finding, read changing existing text; it decides when text stays unchanged, when a list stays explicit, and what to check before cutting a line.

Edit skills/<slug>/ as the source owner. Check the completed source with scripts/heal.sh --check --strict skills/<slug>, then regenerate its owned projections through the repository's owning commands. Inspect the generated diff; hand-edit no projection. A clean check says nothing about behavior: when an edit changes when the skill routes, what it asks, when it stops, what it commits or publishes or how it degrades, or cuts a line that recorded provenance protects, report that behavior as unmeasured unless a Skill Eval result measured that behavior, and name what was missing (a runner, a host or the caller's authorization). Typo, path and formatting edits need no such note.

Creation is staged, not atomic. If a later stage fails, keep the created source and any report, capture the exit and diagnostic, and report source creation separately from projection and check completion. Never call a partial result a completed package or delete it to rerun creation. Missing required scripts, references or runtime support block only their dependent operation; name that resource and continue work that does not depend on it.

Check mode is read-only; fix mode regenerates owned projections for explicit targets and invents no source behavior. The default audit reports static conformance, located effects, behavioral evidence and non-gating authoring suspicions separately, with no total, rating or aggregate verdict. Effects and behavior stay NOT_PROVEN because the audit runs no skill or trial. Exact checks, exit codes and the opt-in legacy schema live in audit checks, authoring doctrine and Codex parity.

Consolidate

Merge only when two skills own the same behavior; a shared passage or two is a cross-link, not a merge. Apply the decision rules above, then pick one owner: keep the skill with the stronger contract (clearer applicability, authority, result and failure path) and move into it only the rules the other has and it lacks. Before retiring the other name, search skills, references, goal prompts, workflows and docs for callers that invoke it by name and point each at the owner, then record the retired name and its new owner in docs/MIGRATION.md. Keep a skill that other skills or goals call by name until its retirement adds the slug to REMOVED_SKILLS in scripts/check-cathedral-cut-conformance.py, which fails if the skill comes back; never delete it without that tombstone. A merge can move routing, so report as unmeasured whether the requests the retired skill caught now reach the owner unless a routing or Skill Eval result measured it.

Conversion

Use bash skills/skill-builder/scripts/converter/convert.sh <skill-dir> <target> [output-dir] for an explicit out-of-tree export. Targets are codex, cursor and test; --all selects all source packages, and --codex-layout inline selects the legacy inline Codex layout. Read SkillBundle when format details matter. Parse the source once, render the target, then validate resource parity and target format. Report layout and any omitted Cursor references.

The default export is .agents/projections/converter/<target>/<skill-name>/. The exporter clean-writes its output directory, so use only the explicit derived target: refuse a source package, its ancestor, or the repository root. Preserve the source unchanged and fix the source or adapter instead of editing output. A parse, write, format or required-resource failure leaves an incomplete export. Every runtime, Codex included, loads skills/ directly; this ad-hoc exporter never produces a shipped tree.

Distill expertise

When the caller wants a reusable rule, apply the decision rules above, then begin with cited occurrences or a named authoritative source. State the trigger, desired behavior, inputs, outputs, negative example and limits. Preserve short source excerpts or command results with resolvable citations. Use Research's pattern mode when the claim needs exemplars and a holdout before packaging. Show a negative or holdout case and how the proposed rule returns the right decision. Minimal recovery state needs a named evidence-loss or corruption risk.

Respect Memory's source and destination rules for mined material. Evidence cannot publish itself as policy. A proposal-only request ends with the proposal. Repair ordinary known defects within existing authority; tool failures remain explicit facts for the native caller, not an automatic helper chain.

For an actual package edit, use the source template for required fields and context density guidance when deciding which prose earns a place. Neither requires adding a new skill.

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 Skill Builder AI skill do?

Create, repair, audit or consolidate agent skills (SKILL.md packages). Use when: writing or fixing a skill, its description or structure. Not for one-off lessons; use Memory.

Why use Skill Builder on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/plugin/skills/skill-builder. 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 Skill Builder?

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 Skill Builder?

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

Is the Skill Builder AI skill free?

Yes. It is published on GitHub by boshu2 under the Apache-2.0 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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