This is a concept tool and research prototype, not a polished production registry. It indexes configured folders for SKILL.md files, records git provenance in SQLite, installs only approved skill artifacts, and supports full-text or embedding search.

Motivation

Agent skills can teach an agent to use tools, run commands, write files, call APIs, and automate real workflows. Installing a third-party skill is therefore closer to adding a dependency than copying a prompt.

This project treats skills as versioned, reviewable software artifacts. The intended flow is: stage sources locally, run skills update, inspect exact versions, approve trusted locations, sources, or skill IDs, then install approved artifacts into a project or global agent profile.

Compared with Vercel's `skills` CLI

Vercel Labs' skills (skills.sh) makes the open skill ecosystem easy to browse and install directly from remote or local sources.

This tool takes a more conservative path:

Local intake: skills are indexed from configured folders; remote sources should be staged first.
Approval gates: locations, source snapshots, and exact artifacts can be approved before installation.
Deterministic identity: each skill version is packaged as a deterministic ZIP and identified by its SHA-256 hash.
Local search: full-text search works from the local SQLite catalog; embeddings add semantic search after update --embed.
Review signals: approvals can carry status, rating, tags, and notes; skills virustotal <skill_id> can store VirusTotal analysis.

Requirements

Bun
git, used to record remote, branch, commit, and date when available

Install

bun add -g @andreas-timm/skills

Quick Start

skills --help
skills location set main ~/data/skills
skills update
skills search "react"
skills show "SKILL_NAME"

By default, skills install <skill_id> writes to the current project's .agents/skills/<skill_name> folder. Use --global, -g, or an agent name such as --global codex for user-level installs. Use skills ls --node-modules to inspect package-provided skills, then skills install -m <skill_id> to install one from node_modules; add -s to install it as a symlink.

Every install is recorded in the SQLite catalog so you can later see what was installed where, when, and from which project. Run skills installs to list the recorded state:

skills installs

Each record keeps the skill id and name, the install location (target_dir) and scope (local or the global agent name), the install timestamp, and the project directory with its git remote, branch, and commit. Re-installing to the same location refreshes the existing record, and skills rm <skill_ref> clears it.

To build the optional semantic search index, run update with embeddings enabled:

skills update --embed
skills search "skills for reviewing a pull request" --embed

Embedding search uses the optional @huggingface/transformers peer dependency. Base installs skip it; install that package alongside the CLI before using --embed.

Core Concepts

Skill file: the SKILL.md entrypoint.
Skill folder: the folder containing SKILL.md.
Skill name: the frontmatter name value.
Location: a named configured root folder under skills.locations, or a known user-level agent skills folder such as ~/.codex/skills when it exists.
Source: the nearest git repo under the location, otherwise the first top-level folder or the location name.
Bundle: a planned grouping layer for related skills from a source; not implemented yet.

Source Ignores

Each location can define source-specific ignore globs. The source key must match the indexed source display name: owner/repo for GitHub remotes, otherwise the source folder or location name.

[skills.locations.packages]
dir = "~/data/skills"

[skills.locations.packages.source."owner/repo"]
ignore = [
    "~/data/skills/repo/drafts/**",
    "~/data/skills/repo/tmp/**/SKILL.md",
]

During skills update, each discovered full path is matched against the inferred source's ignore list using Bun glob syntax. Matches are skipped before indexing.

Skill References

Commands that accept <skill_ref> or [skill_ref] resolve references in this order:

Full deterministic ZIP SHA-256 id.
Stored short_id, when unambiguous.
name@version, using frontmatter version or a generated label such as v3 or 3.
name, resolving to the latest indexed version for that name.

name@version is a convenience lookup, not a secure artifact identity. Exact operations should use <skill_id>, either the full id or an unambiguous short_id. This shared resolution is used by show <skill_ref> and versions [skill_ref].

skills zip <skill_id>
skills approve skill <skill_id>

skills zip <skill_id> also accepts a direct path to a skill folder. Indexed ZIPs are rebuilt and hash-checked before success is reported.

Versions and Duplicates

version is the public version field: frontmatter version when present, otherwise a generated v<version_order> label. version_order is internal metadata used to sort versions, pick the latest version, and generate fallback labels.

duplicate is the count of extra indexed locations for the same concrete skill version. skills versions [skill_ref] --all shows every indexed occurrence, while skills show [skill_ref] shows the selected version's primary location plus duplicates.

Approval Model

Approval has three scopes:

Location approval: approved = true under [skills.locations.<name>], or skills location set <name> <dir> --approved, supplies effective approval for skills under that location without writing per-row flags.
Source approval: skills approve source <source-id> --status approved marks the current indexed source snapshot and its current skill rows as approved.
Skill approval: skills approve skill <skill_id> --status approved marks one exact indexed artifact as approved.

skills install <skill_id> uses the effective approval check. Direct skill statuses such as approved or ignore win; location approval only fills in when the skill row has no direct status. Unapproved or ignored skills are blocked unless --force is passed. skills install -m <skill_id> resolves against local node_modules skills instead of the indexed approval database.

Embedding Search

Text search is the default. Embedding search is optional semantic search for queries where exact words are not enough:

skills update --embed
skills search "browser automation for localhost screenshots" --embed

The packaged embedding config is:

[embed]
model = "nomic-ai/nomic-embed-text-v1.5"
models_dir = "~/.local/share/skills/models"
dim = 768
batch_size = 32
chunk_tokens = 512
chunk_overlap = 64
device = "webgpu"
dtype = "fp32"

The model is loaded through @huggingface/transformers, cached in models_dir, and run locally. The first run may download model files if they are not cached yet.

device selects the inference backend. The default webgpu runs on the GPU (Metal on Apple Silicon) and is the fastest option here; cpu is the portable fallback. Avoid coreml: it recompiles per input shape and stalls on this batched workload. dtype selects numeric precision; fp32 works on every backend, and GPU backends can also try fp16. Override either for a single run with skills update --device <device> and --dtype <dtype> — both flags imply --embed.

skills update --embed embeds the skill name, description, and SKILL.md body chunks. Unchanged chunks are reused on later runs. If you override model or dim in ~/.config/skills/config.toml or local.toml, run skills update --embed again; old chunks are cleared when the stored model or dimension changes.

Update Pipeline

skills update runs an extract -> transform -> load ETL pipeline in src/features/update. It scans configured locations plus the known user-level agent skill directories listed in src/features/agent/skills-dir.ts, including all supported skill subdirectories such as skills and disabled_skills; missing agent directories are skipped.

Extract: extract.ts walks each location with native filesystem APIs to find SKILL.md.
Transform: transform.ts infers source, resolves cached git info, applies ignore globs, and parses frontmatter with gray-matter.
Load: load.ts writes sources and skills in one deterministic transaction.

Deterministic ZIPs

Skill IDs are based on a deterministic ZIP of each skill folder, implemented in src/features/zip/deterministic-zip.ts, plus a SHA-256 hash of that archive.

Symlinks are resolved while packaging: entry names keep the symlink path, bytes come from the resolved target, symlinked directories are traversed under that path, and invalid targets are rejected.

This produces stable content identities without mutating source repositories.