By meaningfy-ws
The design / modelling role: system architecture (C4, ArchiMate/UML, ADRs, contracts) and the living conceptual model (LinkML → typed artefacts)
Reviews code for quality, security, architecture conformance, and test coverage before a PR. Use proactively after implementation, before opening a PR. Read-only — does not modify code. Thin wrapper — the review criteria live in the meaningfy-code-review skill.
Shapes an EPIC from human seeds and derives its clarity-gated PLAN, driving the OpenSpec /opsx build-tier flow. The EPIC is the OpenSpec proposal.md (Shape-Up work shape); the PLAN is design.md + tasks.md, scored by the clarity gate (≥9/10). Use when starting or refining an epic. Asks many questions, makes no assumptions. Thin wrapper — the procedure lives in the epic-planning skill; the living-spec lifecycle lives in spec-stewardship.
Implements an EPIC task as production code following the documentation-first loop. Use when an EPIC task is ready (EPIC.md exists, clarity gate passed, Gherkin written). Thin wrapper — all engineering knowledge lives in the loaded skills.
System-level solution architecture — C4 levels (Context, Container, Component, Code), ArchiMate and UML notations, ADRs, and contracts (OpenAPI/AsyncAPI/LinkML). Use for system and solution design, architecture documentation, and architectural decision-making. Distinct from code structure inside a service (see the cosmic-python skill).
Write BDD Gherkin feature files and fabricate test data from a specification. Use to turn acceptance criteria or an EPIC into business-language `.feature` scenarios — Scenario Outline with Examples, explicit edge cases, no implementation detail. Trigger on "write Gherkin", "write feature files", "BDD scenarios for this acceptance criterion", "fabricate test data".
Standardise the application-repo Continuous Delivery side of Meaningfy systems — the deploy trigger, the reusable deploy mechanism, and the release/image standard. Use to set up a CD/deploy workflow, release and push a versioned Docker image to a registry (recommended GHCR, tagged by semver + git sha), standardise or migrate the deploy trigger, kill the duplicated SSH/bastion/rsync/.env deploy block by consuming the canonical reusable workflow, or understand the three-repo deploy model. CI (build, test, lint, coverage, architecture, docs publish) is NOT here — that is owned by project-setup; this skill owns only CD + release + the delivery contract. Trigger on "set up CD / deploy workflow", "release and push a versioned image", "GHCR image build", "standardise the deploy trigger", "migrate the duplicated deploy block", "how do we deploy this repo".
Pre-ingestion quality gate for specifications and documents before they drive implementation. Use to score a spec/EPIC against a 13-item checklist (6-criterion rubric, must reach ≥9/10), surfacing hidden assumptions and ungrounded claims. Trigger on "score this spec", "is this spec ready", "run the clarity gate", "check this EPIC before implementation". A lightweight variant applies to routine docs.
Build and evolve a living, representation-agnostic conceptual model for a product (programming) project, and generate code/contracts from it deterministically. Use to model the domain, write a LinkML schema, set up a conceptual model, generate Pydantic/JSON Schema/OWL/SHACL from the model (`make generate-models`), manage ontology URIs/IRIs and vocabulary reuse, or run terminology/definitions management. Trigger on "model the domain", "write a LinkML schema", "generate models", "generate Pydantic/OWL/SHACL from the model", "set up conceptual model", "ontology / terminology management", "ubiquitous language glossary". Conditional: applies to product-development repos that build software; a doc-only/non-product repo does not need it.
Uses power tools
Uses Bash, Write, or Edit tools
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The company-wide home for working with LLM agents consistently: reusable Claude Code skills, thin agents, human methodology & standards docs, and the binding templates that wire them into any project.
A curated, self-validating catalogue. Skills carry reusable knowledge; agents are thin
execution wrappers; docs/ is the human canon (method, engineering standards, philosophy);
prompts/ holds the agentic-file templates (CLAUDE-canonical: CLAUDE.md is the agentic file,
AGENTS.md a symlink to it). It also carries the spine — the durable, traceable spec backbone
(an OpenSpec instance + a forked meaningfy schema under
openspec/ and spine/) — that threads a requirement all the way to a commit.
External skills (superpowers, stream-coding, …) are referenced, not copied.
Bundles are organised by the role (hat) you wear — install meaningfy-core plus your role(s):
18 skills in 4 role bundles — every skill lives in exactly one bundle (no duplication):
| Bundle | Skills | Install if you… |
|---|---|---|
| meaningfy-core | technical-writing · meaningfy-git-workflow · guardrails | …do anything (cross-cutting basics) |
| meaningfy-consulting | semantic-consulting-coach · decision-package · proposal-writing · estimation · executive-communication | …do advisory / front-of-funnel work |
| meaningfy-architecture | architecture · conceptual-modelling | …design systems or model a domain |
| meaningfy-building | epic-planning · spec-stewardship · clarity-gate · bdd-gherkin · meaningfy-code-review · cosmic-python · project-setup · ci-cd-delivery · meaningfy-release | …build software with the spine |
Thin agent wrappers live in agents/ — epic-planner, implementer,
code-reviewer — they pin a model + tools and load the skills above.
The spine is a capability, not a bundle.
meaningfy-buildingcarries the skills that drive it; the durable spine assets (openspec/+ the forked schema +spine/docs) are projected into your repo by theproject-setupskill. A spine repo reuses OpenSpec-native artifacts: an EPIC is the OpenSpecproposal.md; a PLAN isdesign.md+tasks.md.
Prerequisites
@fission-ai/openspec), the spine engine.1. Add the marketplace and install the bundle(s) for your role
/plugin marketplace add meaningfy-ws/skillery
/plugin install meaningfy-core # everyone
/plugin install meaningfy-building # builders
/plugin install meaningfy-architecture # architects / modellers
/plugin install meaningfy-consulting # consultants
2. External dependencies (referenced by the skills; install separately)
| Component | Why | Install | |
|---|---|---|---|
| Mandatory | superpowers | TDD, debugging, brainstorming, verification disciplines | /plugin install superpowers@claude-plugins-official |
stream-coding | the documentation-first build method | external skill (see env-setup) | |
ponytail | YAGNI / minimal-code discipline | /plugin marketplace add DietrichGebert/ponytail && /plugin install ponytail@ponytail | |
| OpenSpec | the spine engine + /opsx:* commands | npm i -g @fission-ai/openspec then openspec init per repo | |
| Optional | commit-commands | commit/push/PR mechanics (git-workflow delegates here) | /plugin install commit-commands@claude-plugins-official |
code-review | runs a read-only PR review (pairs with meaningfy-code-review) | /plugin install code-review@claude-plugins-official | |
gitnexus · context7 | code-intelligence · live library docs | external plugins |
3. User-level vs project-level. Install the bundles + external skills once at the
user/machine level, and keep your durable coding standards (the constitution) in the global
~/.claude/CLAUDE.md. Per repo, pin the bundles that repo uses, wire the spine with
project-setup, and keep the repo operating manual in ./CLAUDE.md. Full detail (every dependency,
the exact split): docs/environment-setup.md.
npx claudepluginhub meaningfy-ws/skillery --plugin meaningfy-architectureUltra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Comprehensive UI/UX design plugin for mobile (iOS, Android, React Native) and web applications with design systems, accessibility, and modern patterns
Multi-model consensus engine integrating OpenAI Codex CLI, Gemini CLI, and Claude CLI for collaborative code review and problem-solving.
Curate auto-memory, promote learnings to CLAUDE.md and rules, extract proven patterns into reusable skills.