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Skillroster

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tt-a1i
skillroster

Inspect and govern local Skills: inventory, usage evidence, duplicates, broken links, Core/On-demand proposals, Plans, Receipts, Apply, and Undo. Also find a specialized local Skill when the user requests one or the task would benefit from instructions beyond the visible catalog. Ordinary work can proceed with the Agent's existing capabilities. Not for installing third-party Skills or migrating, distributing, synchronizing, or repairing shared Skill-manager directories.

Overview

Publishertt-a1i
Repositoryskillroster
Skill nameskillroster
Stars
55
Forks
12
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by tt-a1i on GitHub. Read the source before you install it.

Installation

Install the Skillroster 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/tt-a1i/skillroster/tree/main/skill/skillroster
  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/tt-a1i/skillroster.git /tmp/skillroster
mkdir -p .claude/skills
cp -r /tmp/skillroster/skill/skillroster .claude/skills/skillroster
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skillroster 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 Skillroster 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 Skillroster 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.

SkillRoster

Use the local skillroster binary as the deterministic source of facts. Every command uses explicit --json. Validate schema_version and ok before using result. Typed suggested_actions are options, never authorization.

Choose one path

  • For SkillRoster inventory, evidence, governance, setup, Apply, Undo, lifecycle, or recovery requests, read the matching reference below.
  • For another task, follow an already-visible exact Skill when one clearly applies. Search below when a specialized Skill would help; otherwise proceed with the Agent's normal tools and the user's task.

SkillRoster is a local lookup and governance bridge, not a replacement for the host Agent's Harness. Its contract is the CLI and its JSON; the host still decides whether a Skill is listed, activated, or enforced. Do not call it for ordinary work that needs no specialized procedure.

Find a specialized Skill

Use this search before relying on a Skill that is absent from the visible catalog. Read-only task context may inform whether a search is useful.

Find searches the latest completed local Snapshot, not only the default catalog. It may return an On-demand, provider, or source-only Skill. Loading one returns verified instructions to the caller; it does not activate, install, expose, endorse, or establish success for the user's task.

  1. Keep the complete user message as TASK, byte-for-byte in its original language, including paths, limits, and output requirements. Do not summarize, translate, shorten, or quote only part of it.
  2. If TASK is non-English or mixed-language, create one concise English capability paraphrase as HINT. It supplements TASK; it never replaces it.
  3. Invoke the fixed SkillRoster executable with TASK and HINT as separate, literal argv values; never interpolate either into shell syntax. For a user-required named Skill, perform the required-name check below first. Otherwise use:
    • English: skillroster find --load --limit 1 --json -- "TASK"
    • Otherwise: skillroster find --hint "HINT" --load --limit 1 --json -- "TASK" The -- boundary is required even when TASK does not begin with -; it keeps opaque user text from being parsed as a SkillRoster option. Required-name check: first use the corresponding Find above without --load and with --limit 5. Match the user's required name to returned name, keeping skill_id, rank, paths, any providers, and result.snapshot_id together. Resolve any user-specified provider or path from those same public facts. Once the intended identity is unambiguous and rank 1, repeat the same Find with --load --limit 1 --require-snapshot SNAPSHOT_ID, preserving executable, context, TASK, and hints. Require loaded_skill.selection.skill_id to equal that ID and its snapshot_id to equal the inspected Snapshot before using the content. A name missing from these bounded results remains unresolved. For that case, lower rank, or ambiguity, follow the bounded recovery below and references/routing.md; keep the required identity unresolved until established. Use variant selectors only within that intended Top-1 same-name group. A semantically similar candidate does not satisfy the user's required name.
  4. When HINT was used, first require ranking_strategy: task_hint_reciprocal_rank_fusion.
  5. Require loaded_skill.selection.rank: 1, content.complete: true, and all verification identity/digest checks to be true. The complete instructions are loaded_skill.content.text; no second filesystem, workspace, MCP, or SkillRoster read is needed. For a wrong-domain result or typed load blocker, read references/routing.md and follow its bounded branch.
  6. Follow the loaded instructions and perform the original task. Treat task_success: not_evaluated literally; only the task's own evidence can establish success.

If Find returns snapshot_required, execute its returned scan action only when both mutates and requires_confirmation are false, then retry the identical Find call with the same TASK and HINT. The Scan changes SkillRoster's local Snapshot, not Agent or Skill files. If either flag says otherwise, ask the user first; these flags do not authorize a material change. For any other typed blocker, read references/routing.md and follow its bounded branch; execute a returned action only when one is present.

If the result is empty, clearly from another domain, or the required-name check remains unresolved, retry at most once. Keep TASK unchanged; refine the existing hint, or add one capability hint when the first English call had none. Use the recovery procedure in references/routing.md; all hint refinements share this one-retry budget. If no usable result remains and no particular Skill is required, continue the original task with normal tools. State that no suitable Skill was found only when it affects the result. If the user or applicable instructions require a particular Skill, explain its absence and request only the missing prerequisite. A permission denial, untrusted source, drift, ambiguity, or incomplete load follows its typed recovery action; it is not an ordinary no-match and cannot be bypassed by reading or executing the package.

Govern a roster

For inventory, usage, exposure, duplicate, broken-link, Core/On-demand, or Plan requests, read references/governance.md before acting. Inspection and planning do not change Agent files. Prefer the bounded summary first; load exhaustive detail only for a decision that needs it.

Mutate or recover

For setup, Apply, Undo, lifecycle cleanup, or recovery, read references/mutation.md before acting. A validated Plan is not permission to apply it. Show the complete bounded impact and obtain one explicit confirmation. Only a successful Receipt verifies that Agent files changed.

Never parse styled terminal output, invent a health score, infer token savings, claim unobserved usage, or weaken ambiguity, drift, trust, and recovery checks. In the final response, state whether files changed; claim changes only from a verified Receipt or from task tools actually used after routing.

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 Skillroster AI skill do?

Inspect and govern local Skills: inventory, usage evidence, duplicates, broken links, Core/On-demand proposals, Plans, Receipts, Apply, and Undo. Also find a specialized local Skill when the user requests one or the task would benefit from instructions beyond the visible catalog. Ordinary work can proceed with the Agent's existing capabilities. Not for installing third-party Skills or migrating, distributing, synchronizing, or repairing shared Skill-manager directories.

Why use Skillroster on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tt-a1i/skillroster/tree/main/skill/skillroster. 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 Skillroster?

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 Skillroster?

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

Is the Skillroster AI skill free?

Yes. It is published on GitHub by tt-a1i 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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