Claude Code Skills · page 135

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Corezoid marketplace pre-publication validator. Standalone skill — no external skill dependencies required. Use this skill whenever the user wants to publish, release, or submit a project or folder to the Corezoid marketplace, or asks to check if a project is ready to publish. Activate on phrases: \"publish to marketplace\", \"готово до публікації\", \"можна публікувати\", \"перевір перед публікацією\", \"validate for marketplace\", \"pre-publish check\", \"marketplace validation\", \"publication readiness\", \"check before publish\", \"submit to marketplace\", \"перевірка публікації\". Also activate when the user asks \"what's blocking publication\" or \"why can't I publish this\".

Turn Markdown outlines, notes, docs, or plans into an interactive mind map — a single self-contained HTML file that opens offline anywhere (markmap.js, white-on-dark, zoom/expand/export toolbar, search that keeps matches in context). Use whenever the user asks for a mind map, markmap, or concept map, wants to visualize or diagram the structure of a document or topic, summarize notes as a navigable tree, make an outline clickable or explorable, or embed such a map in Streamlit — even when they don't literally say 'mind map'.

Project release helper for corezoid-ai-plugin. Prepares a new tagged release end-to-end. Use this skill whenever the user says "release", "релиз", "новый релиз", "сделай релиз", "выпусти версию", "bump version", "обновить версию", "tag a release", "/release", or anything that implies cutting a new version of this plugin. Walks the user through six explicit phases: (1) compare `main` with the latest git tag and summarise what changed, (2) ask which new version to publish, (3) draft a CHANGELOG.md entry in the existing format, (4) sync that version across all four manifest files (`.claude-plugin/marketplace.json`, `plugins/corezoid/.claude-plugin/plugin.json`, `plugins/corezoid/.codex-plugin/plugin.json`, `.agents/plugins/marketplace.json`), (5) show the user the full proposed change set and wait for explicit confirmation, (6) commit on the current branch and create the matching `vX.Y.Z` tag. Always use this skill instead of running release steps manually — it keeps the four manifests in lock-step, formats the changelog consistently, and prevents partial releases.

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Maintain compatibility between openskills-runtime and language bindings (TypeScript, Python), including feature flags, build configuration, and smoke verification.

Route OpenSkills development tasks to the right project skill or subagent, including sequencing rules for debugging, feature work, regression checks, and release readiness.

Run deterministic OpenSkills end-to-end validation across runtime tests and example agents, then report tool calls, activation behavior, and regressions.

Enforce clean separation between core openskills-runtime and optional WASM build plugins so plugin compilation does not break runtime consumers or language bindings.

Prepare and validate OpenSkills release readiness across runtime, bindings, examples, and regression gates with a deterministic checklist and go/no-go outcome.

Diagnose openskills-runtime execution failures in sandboxed paths (Landlock, seatbelt, native script execution, wasm execution) and produce root-cause-first findings with minimal-risk remediation steps.

Create and refine OpenSkills-compatible skills (SKILL.md + optional resources) with strong metadata, clear activation triggers, and reliable execution guidance.

Reviews code for quality, best practices, and potential issues. Use when asked to review, audit, or check code for problems.

Explains code clearly and thoroughly. Use when asked to explain, clarify, or teach about code snippets, functions, or concepts.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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Analyzes a Delphi / Object Pascal codebase to extract unit-level `uses` dependencies. Use when the user uploads a zip / archive of a Delphi project (or a folder of .pas / .dpr / .dpk files) and asks for a dependency graph, architecture map, cycle detection, fan-in / fan-out coupling analysis, or wants to know how units relate. Produces a Mermaid diagram, a Graphviz DOT file, an SVG when graphviz is available, a JSON dump of the parsed graph, and a markdown report listing cycles, hotspots and orphan units.

Fleet infrastructure mechanics - member management, permissions, onboarding, provider awareness, and tool usage patterns

Project Manager — plans, executes, monitors, and resumes multi-step work across fleet members. Delegates to members, tracks progress, drives reviews and deploys. Never writes code directly.

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Creates and switches to a new, conventionally named branch — derives the name from an inline description, conversation context, or local git diffs. Preserves all local changes. Never commits or pushes. Use when you want a properly named branch for new or in-progress work.

Iterative auto-fix code review — runs `/optimus:code-review` in a fresh subagent context per iteration, applies fixes, runs tests, bisects failures, and continues until convergence or the iteration cap (default 8, hard cap 20). Each iteration runs in an isolated subagent so context does not accumulate. Requires a test command in .claude/CLAUDE.md. Use when single-pass review leaves issues or for thorough cleanup before a release.

Reviews local changes, PRs/MRs, or branch diffs against project coding guidelines using 5 to 7 parallel review agents (bug detection, security/logic, guideline compliance x2, code simplification, test coverage, contract quality). Use before committing, on open PRs/MRs, or to review any branch diff. HIGH SIGNAL only: real bugs, logic errors, security concerns, and guideline violations. For iterative auto-fix in a loop, use `/optimus:code-review-deep`.

Suggests conventional commit messages by analyzing staged, unstaged, and untracked git changes — read-only, never commits. Use when a commit message suggestion is needed without actually committing.

Stages, commits, and optionally pushes local changes with a conventional commit message — analyzes diffs, generates the message, confirms with the user, and commits. On protected branches, offers to create a feature branch automatically. Multi-repo aware. Use when ready to commit work in one step.

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Prepares a project for Claude Code — generates CLAUDE.md with progressive disclosure docs, auto-format hooks, and test infrastructure (framework, coverage tooling, testing docs). Detects empty directories and offers new-project scaffolding via official stack tooling before setup. Also audits and syncs existing documentation against source code. Replaces /init. Supports single projects, monorepos, and multi-repo workspaces (separate git repos under a shared parent directory). Use to bootstrap a new or existing project for Claude Code, or re-run to update an outdated setup.

Fetches and optimizes context from a JIRA issue for AI-assisted development. Searches assigned issues or fetches by key. Distills title, description, acceptance criteria, sprint context, and comments into a structured task description. Analyzes the codebase to surface missing criteria, scope, and risks. Optionally enriches the JIRA issue with a structured analysis comment, and for Complex-scope work can spawn implementation tickets in JIRA. Re-running on the same key refreshes the local task with the latest JIRA state instead of overwriting prior enrichment. Use before /optimus:tdd, /optimus:brainstorm, or /optimus:branch to pull task context from JIRA, or to refresh existing context after JIRA edits.

Configures Claude Code permissions for safe agent autonomy. Creates settings.json with allow/deny rules and a path-restriction hook. Use after /optimus:init to enable autonomous agent workflows, or standalone to lock down a project's permission boundaries.

Creates or updates a pull request (GitHub) or merge request (GitLab) for the current branch using the Conventional PR format — intent, summary, changes, rationale, and test plan. Captures the implementation conversation's intent into the PR description when run in the same session. Use when a branch is ready for review, or to update an existing PR/MR description.

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Iterative project-wide refactoring — runs `/optimus:refactor` in a fresh subagent context per iteration, applies fixes, runs tests, bisects failures, and continues until convergence or the iteration cap (default 8, hard cap 20). Supports `testability` or `guidelines` focus to prioritize finding categories. Each iteration runs in an isolated subagent so context does not accumulate. Requires a test command in .claude/CLAUDE.md. Use for thorough guideline alignment or testability cleanup before /optimus:unit-test.

Refactors existing code for guideline compliance and testability using 4 parallel analysis agents (guideline compliance, testability barriers, duplication/consistency, code-simplifier). Two goals — align code with project guidelines AND make untestable code testable so /optimus:unit-test can safely increase coverage. Use after /optimus:init to align existing code, before /optimus:unit-test to remove testability barriers, or periodically to prevent tech debt. Supports "testability" focus (after unit-test flags untestable code) or "guidelines" focus (after init establishes rules) to prioritize finding categories, and flexible scoping. For iterative refactor in a loop, use `/optimus:refactor-deep`.

Removes files installed by /optimus:init and /optimus:permissions from the project. Compares each file against plugin templates and classifies as unmodified, likely generated, or user-modified. Always asks before deleting. Git-tracked files are noted as recoverable. Tests are never touched. Monorepo and multi-repo aware. Use for clean reinstall or to stop using optimus.

Use when starting a new project or product and you want a docs-first plan before writing code — scaffolds an empty, product-neutral spec-driven-development cascade (product vision, MVP PRD, target tech-stack) for a human to fill, then hands off to brainstorm. Emits skeletons only; it never authors product content and never overwrites existing docs.

Guides test-driven development — decompose a feature or bug fix into behaviors, then cycle through Red (failing test) → Green (minimal implementation) → Refactor for each one. Requires /optimus:init and working test infrastructure. Use when starting a new feature or bug fix with test-first discipline.

Iterative test-coverage improvement loop — dispatches `/optimus:unit-test` (unit-test phase) and `/optimus:refactor` with testability focus (refactor phase) into fresh subagent contexts per cycle, applies tests, runs the test suite, bisects refactor failures, and continues until coverage plateaus or the cycle cap (default 5, hard cap 10). Use to drive coverage up on a codebase that has untestable barriers — the loop alternates between writing tests and unblocking testability so a single skill cannot stall.

Implements an approved spec by having Claude design and run its own Claude Code dynamic workflow (real parallel subagents) — you hand it the goal and constraints, it chooses the orchestration. Test-first is enforced as a quality bar (tests accompany or precede code and the suite is left green), not as supervised Red-Green-Refactor. A peer of /optimus:tdd for spec implementation; prefer it for large or parallelizable specs where one linear pass is slow. Requires /optimus:init and a spec (auto-detects docs/specs/ or docs/jira/, or pass a path). Uses meaningfully more tokens than a normal session. Use when a spec is ready to build and you want fan-out implementation instead of turn-by-turn TDD cycles.

Creates a git worktree for isolated parallel development — new branch in a separate directory with project setup and test baseline. Enables multiple Claude Code sessions on different tasks simultaneously. Multi-repo aware. Use when you need to work on something else without disturbing current work.

Set up a self-optimizing multi-vendor AI agent team using the Polis Protocol — a markdown-based "small city" where each agent is a citizen with a content-hashed capability card, tasks are structured contracts routed to whoever has the strongest track record by a learning bandit, settled contracts produce lessons that compound into team memory, and citizens can ratify amendments to the protocol itself. Use whenever several AI agents (Anthropic, OpenAI, Google, or any vendor) collaborate on one project and "who should do this" is a real question; when you want work routed to whichever model is best at it; when you want a team that measurably gets better over time; or to set up a new polis, register or join as a citizen, open, claim, or settle a contract, file a lesson, propose or vote on an amendment, run a chavruta review before a high-stakes irreversible action, troubleshoot a stalled contract or router pathology, override a routing recommendation, or migrate from a simpler shared-vault setup (e.g. agent-vault) to add routing and self-improvement. Also trigger on "_polis folder", "polis", "capability card", "task contract", "chronicle.md", "chavruta review", "bandit routing", "agent routing", "cross-vendor coordination", "self-improving agent team", "register a citizen", "ratify an amendment", "CONSTITUTION.md", even when the user does not explicitly name the protocol. Pick this when optimization and team development matter, not only communication; for pure note-passing between agents without routing, prefer agent-vault.

Use early when nontrivial, fresh-thread, handoff, old-decision, correction, preference, life-wide, risky, repeated, high-cost, or continuity-sensitive work could change after source-backed continuity orientation. Also use for recovering old wording, clean source, ambient recall hooks, indexes, registry, sync, MCP access, and long Desktop session audits.

Swap or bridge tokens through Ophis, an intent-based DEX aggregator. Parse a natural-language request, get a best-execution quote, build an EIP-712 order, sign it in the user's own wallet, and submit it. Non-custodial: the agent never holds keys or funds.

This skill should be used when the user wants to check how readable a website is to AI agents (ChatGPT, Claude, Perplexity, Google Gemini, MCP clients). Activates for "scan this site for AI readability", "check my llms.txt", "audit our agent-readability", "is my MCP server card valid", "what's our Vercel Agent Readability score", "fix my agents.json", and similar audit-style requests against a public URL. Does not edit the target site — pair with a code-editing MCP tool to apply the fixes the scanner surfaces.

Professional frontend standards for building, scaffolding, extending, or reviewing any UI or frontend project — new or existing — even when standards aren't explicitly asked for. Keeps generated code consistent, reusable, secure, and production-quality. Framework-agnostic: React, Vue, Angular, Svelte, plain JS.

Look up concert setlists and live-music history via setlist.fm. Use when the user asks what songs an artist played at a show, their tour setlists, what was performed at a venue or on a date, or wants to find concerts by artist, venue, city, or year. Triggers on phrases like "what did Radiohead play at...", "Phish setlist for...", "shows at Red Rocks", "what songs were played on this tour", or any request about concert setlists, gigs, tours, or live performances. Requires setlist-mcp installed and the setlist server registered (see Setup below).

Use throughout long sessions to keep the context window small — archive large tool outputs to LETHE and recall them on demand, saving tokens.

Search Etix events, venues, and performers and pull event/venue details via MCP. Triggers on phrases like "find events on etix", "etix tickets for", "what's playing at <venue> on etix", "etix event details for", "search etix for <artist>", or any request involving Etix events, venues, performers, or showtimes. Requires etix-mcp installed and the fetchproxy extension active with an open etix.com tab (see Setup below).

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Generate and edit images with Google Gemini image models via MCP. Use when the user asks to generate, create, or edit images using Gemini or Nano Banana models, wants to produce a consistent set of images from a prompt, or needs to compose/blend multiple images. Triggers on phrases like "generate an image of", "edit this image with Gemini", "create a set of consistent images", "use Nano Banana to make", or any request to produce images via the Gemini image API. Requires the @chrischall/gemini-mcp package installed and the gemini server registered (see Setup below).

The engagement marketplace for the agent economy — AI agents and communities fund quests (follows, reposts, replies, quotes, original posts); verified-X humans complete them and are paid per approved action on Base. Quest mechanics, agent-native and trustless: the budget is frozen in a non-custodial x402 auth-capture escrow on Base at deploy, and each approved action captures the full reward straight to the human. Real engagement from real people, never bots. Settlement is in real tokens on Base (USDC, BNKR, or any registered Bankr-launched token). Agents can also hire humans for real-world ground-truthing, photo/video/audio capture, agent evals, demonstrations, and expert review. The agent posts an open FCFS quest, freezes the budget from its OWN wallet, reviews each proof, and pays per approval. Works headless: one command onboards a wallet + API key.

Publish a skill to the npm registry (or private registry).

Add an idea to the backlog parking lot (999.x numbering)

Add phase to end of current milestone in roadmap

Generate tests for a completed phase based on UAT criteria and implementation

Capture idea or task as todo from current conversation context

Audit milestone completion against original intent before archiving

Cross-phase audit of all outstanding UAT and verification items

Run all remaining phases autonomously — discuss→plan→execute per phase

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

List pending todos and select one to work on

Archive accumulated phase directories from completed milestones

Archive completed milestone and prepare for next version

Systematic debugging with persistent state across context resets

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Gather phase context through adaptive questioning before planning. Use --auto to skip interactive questions (the agent picks recommended defaults).

Route freeform text to the right MindForge command automatically

Execute all plans in a phase with wave-based parallelization

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Execute a trivial task inline — no subagents, no planning overhead

Post-mortem investigation for failed MindForge workflows — analyzes git history, artifacts, and state to diagnose what went wrong

Diagnose planning directory health and optionally repair issues

Show available MindForge commands and usage guide

Insert urgent work as decimal phase (e.g., 72.1) between existing phases

Join the MindForge Discord community

Surface the agent's assumptions about a phase approach before planning

List active MindForge workspaces and their status

Interactive command center for managing multiple phases from one terminal

Analyze codebase with parallel mapper agents to produce .planning/codebase/ documents

Generate a comprehensive project summary from milestone artifacts for team onboarding and review

Use when starting any conversation - establishes how to find and use skills, requiring MindForge Skill tool invocation before ANY response including clarifying questions