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Manage Skills

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xingkongliang
manage-skills

Manage the user's shared agent-skill library via skills-manager-cli — install, update, remove, deploy or undeploy skills per agent, manage presets, organize tags, search, and adopt existing skills. Use this whenever the user wants Claude Code, Codex, Cursor, or another agent to gain or lose a skill, wants to organize the central library, or asks what is installed or deployed. Prefer this over direct agent-folder installs because Skills Manager preserves source metadata, preset membership, updates, and cross-agent deployment state.

Overview

Publisherxingkongliang
Repositoryskills-manager
Skill namemanage-skills
Stars
4.8K
Forks
412
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Manage Skills 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.
  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/xingkongliang/skills-manager.git /tmp/skills-manager
mkdir -p .claude/skills
cp -r /tmp/skills-manager/skills/manage-skills .claude/skills/manage-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Skills 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 Manage Skills 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 Manage Skills 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.

Before doing anything

  1. Resolve the CLI first, then use the path it prints. Run this once (POSIX shell):

    bash
    D="$HOME/.skills-manager/bin"
    B="$D/skills-manager-cli"; [ -e "$B" ] || B="$B.exe"   # .exe on Windows
    if [ -s "$D/.version" ] && [ -x "$B" ]; then
      echo "$B"
    elif [ -s "$D/.version" ] || [ -e "$B" ]; then
      echo BRIDGE_BROKEN
    else
      P="$(command -v skills-manager-cli 2>/dev/null || true)"
      [ -x "$P" ] && echo "$P"
    fi

    Substitute the printed path into every command below, wherever the examples write $SM. Do not carry $SM as a shell variable: each command you run is a new shell, so an assignment made here is gone by the next one.

    The three outcomes:

    • A path under ~/.skills-manager/bin — the desktop app published this copy, and the .version stamp appears only after it has been verified, so it always matches the app the user is running. Use it.
    • BRIDGE_BROKEN — something the app left behind is here but does not add up: an unstamped binary, or a stamp with no binary beside it. Either is what a copy that failed half-way leaves. Stop. Do not go looking for another CLI: that binary may predate a safety fix, and the machine has a desktop app whose version nothing here can match. Ask the user to open the Skills Manager app once, which republishes it.
    • A path from PATH — nothing was ever published here, so there is no stale copy to worry about: this is a CLI-only machine (a server install, a standalone download, a hand-built binary). Use it, but note it can be older than a desktop app if one is also installed.

    If nothing is printed at all, this skill doesn't apply — fall back to find-skills, or tell the user to install Skills Manager.

  2. Always pass --json when you parse output yourself. Pretty-printed output is for the user; JSON is for you. Errors include ok=false, a stable code, and message on stderr with a non-zero exit code.

bash
"$SM" --json skills list

When a deployment is refused

A deploy that would overwrite something that is not ours is refused outright — nothing at those paths is deleted, and nothing else in the batch is applied. That failure is machine-readable, so report the actual paths rather than the sentence:

json
{"ok": false, "code": "TARGET_CONFLICT", "kind": "target_conflict",
 "message": "Refusing to deploy: 1 of 2 target(s) …",
 "details": {"conflicts": [{"path": "/Users/me/.claude/skills/db",
                            "reason": "is not a managed deployment"}]}}

Tell the user which path is in the way, that its contents are untouched, and offer the two ways out: adopt it into the library (skills adopt), or move it aside and retry. Never delete it for them.

Mental model

There's one central library at ~/.skills-manager/skills/ that all agents share. Each skill has source metadata, preset membership, tags, and zero or more real deployments in agent directories. A preset is a reusable group; several presets may be deployed at the same time.

Keep these three states separate:

  • Library: install/remove controls whether Skills Manager owns the skill.
  • Preset membership: presets add-skill/remove-skill organizes the library only.
  • Deployment: skills deploy/undeploy and presets deploy/undeploy control what an agent can actually see.

Internally, presets are still stored as scenarios for backward-compatible Git Backup. The CLI and UI call them presets.

Install

bash
# From skills.sh marketplace
"$SM" skills install vercel-labs/agent-skills@react-best-practices

# Any git URL (use /tree/branch/subpath form when the skill lives in a sub-directory)
"$SM" skills install https://github.com/anthropics/skills.git
"$SM" skills install https://github.com/foo/bar/tree/main/skills/baz

# Local folder
"$SM" skills install ./my-skill

# Force a source type when the ref is ambiguous
"$SM" skills install foo/bar --skillssh
"$SM" skills install ./looks-like/owner-repo --local

Default is library-only — the skill enters the DB but doesn't appear in any agent yet. Prefer an explicit follow-up deployment so scope is unambiguous:

bash
"$SM" skills deploy <skill> --agent claude_code --agent codex

--sync and --sync-preset remain legacy shortcuts for the exclusive active-preset workflow.

Ref resolution is deterministic, no path-existence guessing:

  1. Starts with ./, ../, /, or ~/ → local path
  2. Contains ://, ends in .git, or starts with git@ → git URL
  3. Matches owner/repo, owner/repo/skill, or owner/repo@skill → skillssh
  4. Otherwise → error; pass --local / --git / --skillssh to disambiguate

Always verify after install with skills list or skills show <name> so you can confirm the skill landed and report the preset / sync state back to the user.

Search

bash
"$SM" --json skills search "react performance" --limit 5

Each result has install_ref (paste straight into skills install), installs (popularity proxy), and skills_sh_url. Show the top 1–3 with install counts before installing — anything with 10K+ installs is battle-tested; anything under 100 needs a careful look at the source repo.

Update / Check

bash
# Re-fetch one skill (git/skillssh re-clones, local/import re-imports source dir)
"$SM" skills update <skill-name-or-id>

# Re-fetch all eligible skills
"$SM" skills update --all

# Just probe remote revisions, don't touch files
"$SM" skills check --all

check is the dry-run partner of update. Local-only skills (no git source) are reported as skipped: true.

An update replaces the skill's directory wholesale, so anything written inside it that the new version does not have would be destroyed. When the CLI detects that, it applies nothing and reports the paths instead:

jsonc
{ "name": "ppt-master", "refreshed": false,
  "held_back_removals": ["library: templates/mine.pptx"] }

The field is omitted entirely when nothing is held back, so test for its presence rather than for an empty array. refreshed: false with held_back_removals is not a failure and not something to retry — the skill is untouched and still on its old version. Show the user the listed paths and ask. There is no CLI flag to override this; only the desktop app can confirm and proceed, because only a person can say those files are expendable. The paths are prefixed with where they live (library, or an agent key for a deployed copy).

Note what this does not cover: a file the user edited that the new version also ships is reported as surviving, because its path survives — the update overwrites their edits silently. Warn anyone keeping local modifications inside a skill folder.

Remove

bash
# Always preview first when removing more than one
"$SM" skills remove <skill> --dry-run

# --yes is required for the actual delete; --json mode does NOT auto-confirm
"$SM" skills remove <skill> --yes

Remove deletes the central-library copy, all synced targets across agents, and the DB row. It's not reversible without re-installing.

Deploy / Undeploy

bash
"$SM" skills deploy <skill> --agent claude_code
"$SM" skills undeploy <skill> --agent codex
"$SM" skills deploy <skill-a> <skill-b> --agent codex --dry-run
"$SM" skills deploy <skill> --agent claude_code --agent codex
"$SM" --json skills status <skill>

These commands change real managed deployments without deleting the central-library copy or changing preset membership. skills enable/disable are deprecated compatibility commands and do not change deployment; never use them.

skills deploy and skills undeploy always require at least one explicit --agent, whether the command names one skill or several. skills status also reports target rows left by a custom agent that is no longer registered, so stale deployments stay visible and can be cleaned with an explicit undeploy while the row exists.

Legacy exclusive sync

bash
# Sync current active preset to all enabled agents
"$SM" skills sync

# Preview the target list — safe, no writes
"$SM" skills sync --dry-run

# Switch the one legacy active preset, then sync
"$SM" skills sync --preset "Web Dev"

# Only sync to a single agent (useful when one agent's directory got out of sync)
"$SM" skills sync --tool claude_code

Adopt skills installed elsewhere

When skills already live in an agent's directory (e.g. installed via npx skills add or manual git clone) but aren't in the central library, pull them in:

bash
# Dry-run scan first — lists candidates without writing
"$SM" skills adopt ~/.claude/skills --dry-run

# Adopt everything found — each becomes source_type=local (can't auto-update from git)
"$SM" skills adopt ~/.claude/skills

# Adopt a single skill and pin it to a git source so `update` works later
"$SM" skills adopt ~/.claude/skills/react-best-practices \
  --git-url https://github.com/vercel-labs/agent-skills/tree/main/react-best-practices

# Or pass --git-subpath explicitly when the URL is just the repo root
"$SM" skills adopt ~/.claude/skills/react-best-practices \
  --git-url https://github.com/vercel-labs/agent-skills \
  --git-subpath react-best-practices

# Skill lives at the repo root? Pass an empty subpath
"$SM" skills adopt ~/.claude/skills/my-skill \
  --git-url https://github.com/me/my-skill --git-subpath ""

adopt auto-excludes anything already in the DB or already a sync target, so it's safe to re-run. --git-url requires either a URL with a subpath (/tree/branch/path) or an explicit --git-subpath — without that, future update would re-clone the wrong directory, so the CLI refuses to guess.

--git-url only applies at the moment of adoption, while the directory is still unmanaged. Once a skill is in the library, use set-source below.

Re-point a skill at a git source

bash
# Preview: resolves the source and reports whether content differs. It clones to
# a temp dir, but writes nothing to the library or the DB.
"$SM" --json skills set-source <skill> --git-url you/skills --subpath my-skill --dry-run

# A GitHub /tree/ URL carries the branch and subpath already
"$SM" skills set-source <skill> --git-url https://github.com/you/skills/tree/main/my-skill

This is how a local skill becomes git-backed so update works, and how a skill pointed at the wrong repo gets corrected. It updates the row in place, so the skill id survives and the tags, preset membership and per-agent deployments keyed to it all stay intact.

  • The flag is --subpath here, not --git-subpath — that one belongs to adopt. Pass --subpath "" when the skill is at the repo root, which must itself hold a SKILL.md.
  • --branch overrides a branch encoded in the URL.
  • The report carries content_changed — a single boolean, not a file list. It compares the new source against the hash currently recorded for the library copy, not a fresh hash of the directory on disk, so edits made inside the central copy afterwards do not register as a difference. When it is false the library copy is left untouched and those edits survive; copy-mode deployments are re-synced either way.

--force is destructive, and nothing stands between it and the user's files. A content difference is refused without it. With it, the whole skill directory is replaced — staged, swapped in, and the old copy deleted — so anything in the library copy that the new source does not ship is gone. Unlike skills update, this path has no held_back_removals check: nothing is withheld, and nothing asks. --dry-run cannot tell you which files are at stake, only that something differs. Never pass --force on the user's behalf — report content_changed: true, say that proceeding overwrites the library copy wholesale, and let them decide.

Tag

bash
"$SM" skills tag add <skill> web frontend
"$SM" skills tag remove <skill> frontend
"$SM" skills tag set <skill> web frontend
"$SM" skills tag rename frontend web
"$SM" skills tag delete obsolete --dry-run
"$SM" skills tag delete obsolete --yes
"$SM" skills tag list <skill>   # tags on one skill
"$SM" skills tag list           # all distinct tags

Useful organization queries:

bash
"$SM" --json skills list --untagged
"$SM" --json skills list --no-preset
"$SM" --json skills list --tag frontend
"$SM" --json skills list --preset "Web Dev"
"$SM" --json skills list --deployed-to codex

Presets

bash
"$SM" presets list
"$SM" presets current
"$SM" presets show "Web Dev"
"$SM" presets create "Web Dev" --description "Frontend work"
"$SM" presets update "Web Dev" --name "Frontend"
"$SM" presets delete "Old" --dry-run
"$SM" presets delete "Old" --yes

"$SM" presets add-skill <preset> <skill>...
"$SM" presets remove-skill <preset> <skill>...

"$SM" presets deploy <preset>                  # all enabled coding agents
"$SM" presets deploy <preset> --agent codex
"$SM" presets undeploy <preset> --agent claude_code
"$SM" presets undeploy <preset>                # every agent with target rows for this preset
"$SM" --json presets status <preset>

deploy/undeploy are additive and match the app's Preset pills. Explicit presets apply/deactivate commands remain for the legacy exclusive active-preset model; do not use them for normal "turn this preset on/off" requests.

The no---agent defaults intentionally differ: deploy targets all installed, enabled coding agents; undeploy discovers the preset's actual target rows and removes them even when an agent is now disabled, uninstalled, or no longer registered. Use the no-agent undeploy for "turn this preset off everywhere."

Preset create/update/delete and add-skill/remove-skill are organization-only CLI operations. They never deploy or undeploy agent files implicitly.

Health check

When sync misbehaves or a command errors in a confusing way:

bash
"$SM" --json repo status   # base dir, skill / preset counts, active preset
"$SM" --json agents list  # detected agents and their target paths
"$SM" agents enable codex
"$SM" agents disable claude_code

repo status and agents list are read-only and are the first checks for "why isn't this skill showing up in Cursor" questions. agents disable is a real mutation: it removes every managed deployment for that agent. agents enable makes the agent globally available again and re-syncs the legacy active preset, if one exists; use explicit skill or preset deployment afterward when the requested state is additive.

Use agents disable <agent> when the user wants the whole Agent integration turned off or wants every managed skill removed from it. If they only want one skill or preset removed while keeping the Agent available for future deployments, use skills undeploy or presets undeploy instead.

Typical workflows

"Find me a skill for X" / "Install a skill that does X"

  1. skills search "X" --limit 5 — show the top 1–3 hits with install counts and source.
  2. If a clear winner: skills install <install_ref>.
  3. If ambiguous: ask the user to pick.
  4. Deploy it to the agent(s) the user requested with skills deploy.
  5. skills status <name> to confirm the library and deployment state.

"What skills do I have?"

bash
"$SM" --json skills list

The preset_ids, presets, deployed_to, tags, and source_type fields are usually the most informative. The legacy enabled field is not deployment state.

"Pull in the skills already installed in my agent directories"

  1. skills adopt ~/.claude/skills --dry-run (and any other agent dirs the user mentions) — show the candidate list.
  2. After user confirms: skills adopt ~/.claude/skills.
  3. For any adopted skill where the user knows the original repo, restore the update link with skills set-source <skill> --git-url ... --subpath ....

"Update everything"

bash
"$SM" skills check --all     # see what has upstream changes
"$SM" skills update --all    # apply

Report which skills actually refreshed (refreshed: true in the JSON) vs which were already up-to-date.

Pitfalls

  • Install succeeded but skill doesn't appear in the agent → install defaults to library-only. Use skills deploy <skill> --agent <key>.
  • Preset membership changed but agent files did not → membership is organization only. Follow with presets deploy or skills deploy when the user also asked to make it visible.
  • No active preset only affects legacy skills sync / presets apply; additive deploy commands do not require one.
  • Adopted skills can't be updated from gitnpx skills add and manual git clone don't leave source metadata, so adopt has to treat them as local. Re-point them with skills set-source. Do not reach for adopt --git-url here: adopt only ever creates new library entries, and it fails late--dry-run returns ok: true with the skill sitting in skipped, and only the real run errors with --git-url requires exactly one adoptable skill, found 0. Do not remove-then-reinstall either — that drops the skill id, and with it the tags, preset membership and every per-agent deployment.
  • Use --dry-run before bulk remove, tag delete, preset delete, deploy, or undeploy operations. Use check before update.

Frequently asked questions

What does the Manage Skills AI skill do?

Manage the user's shared agent-skill library via skills-manager-cli — install, update, remove, deploy or undeploy skills per agent, manage presets, organize tags, search, and adopt existing skills. Use this whenever the user wants Claude Code, Codex, Cursor, or another agent to gain or lose a skill, wants to organize the central library, or asks what is installed or deployed. Prefer this over direct agent-folder installs because Skills Manager preserves source metadata, preset membership, updates, and cross-agent deployment state.

Why use Manage Skills on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xingkongliang/skills-manager/tree/main/skills/manage-skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Manage Skills?

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 Manage Skills?

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

Is the Manage Skills AI skill free?

Yes. It is published on GitHub by xingkongliang 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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