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Agent skill

Ss Learn

by bitjaru in bitjaru/styleseed

Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or…

MITAuto-check passedFrontend & Design

Install Ss Learn

skills CLI
$ npx skills add bitjaru/styleseed --skill ss-learn -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bitjaru/styleseed ss-learn --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bitjaru/styleseed.git skills-src && mkdir -p .claude/skills && cp -r skills-src/extensions/learning/skills/ss-learn .claude/skills/ss-learn && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ss-learn
GitHub stars
970
Token cost
~1.3k tokens
SKILL.md length
529 words
Files
11 (incl. scripts, references)
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or…

  • Works in 6 steps: Initialize local learning → Draft a candidate → Human review → …
  • A person asks StyleSeed to remember
  • SKILL.md covers When not to use, 1. Initialize local learning, 2. Draft a candidate and 3. Human review, plus 4 more sections
  • Runs JavaScript scripts from its folder; calls node

What it does

Ss Learn is an agent skill from bitjaru/styleseed. Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule from an accepted design correction.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/candidate-record.schema.json` and `references/candidate.schema.json`).

It sits in Frontend & Design, covering UI design and Design systems. It works with shadcn/ui, Next.js, React and TypeScript. The repository describes itself as: Open-source design-method engine for Claude Code, Codex & Cursor. 23 agent skills for fixed design judgment, multiple grammars, semantic palettes, reference compilation, and… The licence is MIT.

When your agent uses it

  • A person asks StyleSeed to remember
  • Prepare a reusable rule from an accepted design correction

Example prompts

  • “/ss-learn”

Requirements

  • Node.js

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Initialize local learning
  2. Draft a candidate
  3. Human review
  4. Prepare an opt-in share package
  5. Grant one MCP read
  6. Promotion boundary

What it can do on your machine

Read from SKILL.md and the folder at commit 257ec70. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ss Learn loads about 1.3k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 529 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from bitjaru/styleseed at commit 257ec70, republished under its MIT licence (© bitjaru). 529 words, ~1,267 tokens.

Download SKILL.mdSave it as .claude/skills/ss-learn/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
ss-learn
description
Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule from an accepted design correction.

Learn from project design decisions

ss-learn turns a specific human-approved correction into a generalized candidate rule. It does not train a model, scrape a repository, or upload telemetry. The CLI is local-only. An optional plugin MCP bridge can expose one exact package to its connected client/model only after a separate one-time human grant.

Read references/privacy-contract.md before using this skill.

When not to use

  • The user did not explicitly ask to capture or share a lesson.
  • The change was accepted only by the agent, not a person.
  • The lesson cannot be expressed without client/product identity, source code, a screenshot, proprietary tokens, or user content.
  • A score or visual pass was not actually measured. Record it as null or not-run; never infer.
  • The observation belongs only to one project's taste. Keep it in STYLESEED.md instead.

1. Initialize local learning

After explicit user approval:

bash
node <installed-ss-learn>/scripts/learning.mjs init --project-root .

This creates .styleseed/learning/config.json with sharing disabled and all raw-material collection disabled. It performs no network request.

2. Draft a candidate

Use references/candidate.schema.json. Generalize the lesson:

  • problem: what design failure was observed;
  • intervention: what bounded change the person accepted;
  • rationale: why it improved the product job;
  • appliesWhen: conditions where the judgment should transfer;
  • avoidWhen: counterexamples and failure boundaries;
  • evidence: only measured scores, verification status, and optional SHA-256 artifact hashes.

Do not include project names, URLs, paths, emails, source snippets, prompts, screenshots, colors, font names, or component names. Then capture it:

bash
node <installed-ss-learn>/scripts/learning.mjs capture \
  --project-root . \
  --input /path/to/candidate.json

The CLI validates maintained context IDs, exact fields, privacy patterns, and evidence honesty. It writes an immutable draft ID under .styleseed/learning/candidates/.

3. Human review

Show the full candidate to the user. Only after their explicit accept/reject decision run:

bash
node <installed-ss-learn>/scripts/learning.mjs review \
  --project-root . \
  --id <candidate-id> \
  --decision accepted \
  --reviewer <local-alias> \
  --reason "<why this generalizes>" \
  --attestation APPROVE_LOCAL_REVIEW

Use --decision rejected for a counterexample. Never accept on the user's behalf. A candidate is content-addressed and receives one final local decision; revise the source lesson and capture a new candidate instead of rewriting an accepted or rejected record.

Show full SKILL.md (222 more words)Show less

4. Prepare an opt-in share package

Only an accepted candidate can be packaged. Show the sanitized payload and ask separately whether the user approves export for team-registry or community-candidate:

bash
node <installed-ss-learn>/scripts/learning.mjs prepare-share \
  --project-root . \
  --id <candidate-id> \
  --purpose team-registry \
  --attestation APPROVE_LOCAL_EXPORT

This writes .styleseed/learning/share/<id>.<purpose>.json. It strips reviewer identity and local paths, binds the payload to the engine revision, and records a content hash. It does not send the file anywhere.

5. Grant one MCP read

Only when the user separately approves exposing the prepared package to the connected MCP client and its model, run:

bash
node <installed-ss-learn>/scripts/learning.mjs grant-mcp-read \
  --project-root . \
  --package .styleseed/learning/share/<package.json> \
  --attestation APPROVE_MCP_READ

The grant is bound to the package hash and accepted local review. The MCP bridge consumes it before returning the package, so retries fail closed. This is client/model exposure even though the MCP server itself performs no network request. Never describe it as remaining local after consumption.

6. Promotion boundary

A share package is evidence, not a StyleSeed rule. Central or team promotion requires multiple independent projects, counterexamples, accessibility and grammar regression checks, benchmark evidence, and named maintainer approval. Never edit core rules automatically from local learning.

Completion report

Report separately:

  • local candidate: captured | not captured;
  • human review: accepted | rejected | pending;
  • visual evidence: verified | failed | not run;
  • share package: prepared locally | not prepared;
  • MCP grant: absent | available once | consumed;
  • client/model exposure: not performed | performed after one-time approval;
  • external registry or community transmission: not performed by the CLI or MCP bridge.

© bitjaru, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in extensions/learning/skills/ss-learn of bitjaru/styleseed.

  • SKILL.md
  • agents/openai.yaml
  • references/candidate-record.schema.json
  • references/candidate.schema.json
  • references/privacy-contract.md
  • references/share-package.schema.json
  • scripts/learning-contract.mjs
  • scripts/learning-package.mjs
  • scripts/learning.mjs
  • scripts/privacy-scan.mjs
  • scripts/secure-fs.mjs

Open the folder on GitHubat commit 257ec70

Compare with similar skills

Ss Learn next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ss Learn compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ss Learn this skillbitjaru/styleseed970—~1.3kAutomated safety check: PassMIT
UI Design Systemtry-works/role-model117—~5kAutomated safety check: PassMIT
Creative Tim UI Blockscreativetimofficial/ui12k—~2.1kAutomated safety check: NotesMIT
Shadcn Tailwind UILiarMTTT/TavernWeave148—~1.7kAutomated safety check: PassCustom licence
Oma Frontendfirst-fluke/oh-my-agent1.3k—~2.5kAutomated safety check: PassMIT
Shadcn UIeinverne/dotfiles121—~6.2kAutomated safety check: PassMIT

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More from bitjaru/styleseed

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Works with

Categories

Questions about Ss Learn

What does Ss Learn do?

Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or…. Ss Learn is an agent skill from bitjaru/styleseed. Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data.

When should I use Ss Learn?

Ss Learn fits situations like: A person asks StyleSeed to remember; prepare a reusable rule from an accepted design correction.

How do I install Ss Learn in Claude Code?

Run `npx skills add bitjaru/styleseed --skill ss-learn -a claude-code`. Or copy the skill folder (extensions/learning/skills/ss-learn in bitjaru/styleseed) into .claude/skills/ss-learn in your project. Claude Code loads it when a task matches its description.

How do I install Ss Learn in Codex?

Run `npx skills add bitjaru/styleseed --skill ss-learn -a codex`. Or copy the skill folder (extensions/learning/skills/ss-learn in bitjaru/styleseed) into .agents/skills/ss-learn in your project. Codex loads it when a task matches its description.

Can I use Ss Learn in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bitjaru/styleseed --skill ss-learn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ss-learn, .gemini/skills/ss-learn, .github/skills/ss-learn and .opencode/skills/ss-learn in your project.

What does Ss Learn need to run?

Going by SKILL.md and its folder, Ss Learn needs JavaScript for the scripts in its folder and the command-line tools its instructions call (node). Our summary lists: Node.js.

Does Ss Learn access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ss Learn safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Ss Learn use?

Ss Learn is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ss Learn use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Ss Learn?

Skills that share tags, products or a category with Ss Learn: UI Design System (try-works/role-model, 117 stars), Creative Tim UI Blocks (creativetimofficial/ui, 12k stars), Shadcn Tailwind UI (LiarMTTT/TavernWeave, 148 stars) and Oma Frontend (first-fluke/oh-my-agent, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ss Learn?

bitjaru (a GitHub user) maintains it in bitjaru/styleseed, which has 970 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 1, 2026.

Source: bitjaru/styleseed on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.