Claude Code Skills · page 124

Reviews Phoenix code for controller patterns, context boundaries, routing, and plugs. Use when reviewing Phoenix apps, checking controllers, routers, or context modules.

Comprehensive Elixir/Phoenix code review with optional parallel agents

Reviews BubbleTea TUI code for proper Elm architecture, model/update/view patterns, and Lipgloss styling. Use when reviewing terminal UI code using charmbracelet/bubbletea.

Go application architecture with net/http 1.22+ routing, project structure patterns, graceful shutdown, and dependency injection. Use when building Go web servers, designing project layout, or structuring application dependencies.

Reviews Go code for idiomatic patterns, error handling, concurrency safety, and common mistakes. Use when reviewing .go files, checking error handling, goroutine usage, or interface design. Covers generics (Go 1.18+), errors.Join and slog (Go 1.21+), and Go 1.22 loop variable semantics.

Go concurrency patterns for high-throughput web applications including worker pools, rate limiting, race detection, and safe shared state management. Use when implementing background task processing, rate limiters, or concurrent request handling.

Data persistence patterns in Go covering raw SQL with sqlx/pgx, ORMs like Ent and GORM, connection pooling, migrations with golang-migrate, and transaction management. Use when implementing database access, designing repositories, or managing schema migrations.

Idiomatic Go HTTP middleware patterns with context propagation, structured logging via slog, centralized error handling, and panic recovery. Use when writing middleware, adding request tracing, or implementing cross-cutting concerns.

Reviews Go test code for proper table-driven tests, assertions, and coverage patterns. Use when reviewing *_test.go files.

Comprehensive Go web development persona enforcing zero global state, explicit error handling, input validation, testability, and documentation conventions. Use when building Go web applications to ensure production-quality code from the start.

Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

Comprehensive Go backend code review with optional parallel review areas. Use when reviewing changed Go files; detects BubbleTea, Wish SSH, and Prometheus and loads the matching review skills.

Comprehensive BubbleTea TUI code review for terminal applications. Use when reviewing charmbracelet/bubbletea, lipgloss, bubbles, or Wish SSH code; optionally reviews each area concurrently.

Use when you need a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: \"quick plan\", \"plan this out\", \"plan what we just discussed\", \"turn this into an implementation plan\", \"plan this without a spec\", \"I don't have a spec, just plan it\", \"write-plan but no spec\". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say \"plan it\" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.

Fast-path AI visibility — get a brand's 0–100 Akii Visibility Score (computed by an open-source LLM judge against the brand's public footprint) with four-dimension breakdown AND a per-engine proxy map for ChatGPT, Claude, Gemini, Perplexity, Copilot, and Google AI Overviews based on FirstPageSage signal weights. Single-turn fast path. This is the default for any AI-visibility question. Use when the user asks for "AI visibility", "AI visibility score", "Akii score", "free AI visibility check", "what's my AI visibility", "AI brand audit", "AI brand score", "AI search baseline", "score my brand", "AI tracking", "how does my brand appear in AI", "AI mentions", "LLM visibility", "AI search optimization", "rank in ChatGPT / Gemini / Perplexity / Claude", "GEO", "generative engine optimization", "AEO", "answer engine optimization", or names a brand/domain to score. Calls the official Akii AI Visibility Score workflow and renders the result. **Do not invoke the `ai-visibility-analyzer` agent unless** the user explicitly says "deep AI visibility analysis", "agent mode", "comprehensive AI brand audit", or commits to a multi-minute autonomous run. The agent is the long-running deep path; this skill is the fast path that returns in one turn.

Find and fix broken links — three modes with different scale ceilings. `local` audits any local repo (HTML/MD/MDX, no size limit) via Glob/Grep + per-link verification. `page` checks one live URL deeply (~30-100 outbound links per run). `site` requires the Ahrefs site-audit MCP for true multi-page crawl — without it, refuses and tells the user how to get there. Use when the user asks to "check broken links", "find dead links", "fix 404s", "link checker", "broken link audit", "redirect chains", "find dead URLs", "link validation", "mixed content". Accepts a mode modifier (`--mode=local|page|site`); auto-detects from the target if unspecified.

Fast-path competitor intelligence — produces a side-by-side SEO + AEO + GEO + AI visibility scorecard plus ranked counter-move plan. Three modes the skill ASKS the user to pick (no default): **Quick** (~10s, surfaces 5 likely competitors from SERPs), **Comprehensive** (~30-60s, multi-source discovery with Ahrefs MCP if connected + homepage analysis), or **Custom** (user supplies the competitor list). Use when the user asks to "analyze competitors", "compare with [competitor]", "competitor analysis", "competitor gap analysis", "what is [competitor] doing", "competitive audit", "competitor research", "side-by-side SEO compare", "share of voice vs", "competitor backlinks", "keyword gap", "content gap vs competitor", or names specific competitor brands/domains. **Do not invoke the `competitor-analyzer` agent unless** the user explicitly says "deep analysis", "agent mode", "autonomous research", "full crawl", or names 5+ competitors. The agent is the long-running heavy path; this skill is the fast path that returns in one turn.

Generate a detailed content brief for a specific article or page. Use when the user asks to "create a content brief", "write a brief", "article outline", "blog brief", "writing brief", "content outline", "SEO brief", "AEO brief", "GEO brief", or wants a structured plan before writing.

Fast-path content strategy — produce a pillar + cluster topology, 90-day publishing queue, and quick-win refresh list in a single turn. This is the default for any content-planning question. Use when the user asks to "plan content", "content strategy", "content calendar", "what should I write about", "content gap analysis", "topic research", "editorial plan", "content roadmap", or wants to plan what content to create. **Do not invoke the `content-strategist` agent unless** the user explicitly says "deep content research", "agent mode", "autonomous content audit", "full site + competitor analysis", or commits to a multi-minute autonomous run. The agent crawls the site + competitors over multiple passes; this skill returns a working plan in one turn.

Translate and localize content for international SEO. Use when the user asks to "translate content", "localize my site", "multilingual SEO", "translate to <language>", "international SEO", "hreflang", "multi-language website", "global content strategy", or wants to expand to new markets.

Analyze and improve internal linking strategy. Use when the user asks about "internal links", "link structure", "site architecture", "link strategy", "orphan pages", "link equity", "page authority distribution", "anchor text", "topical clusters", "siloing", or wants to improve how pages connect.

Cluster and organize keywords into topical groups for SEO. Use when the user asks to "cluster keywords", "group keywords", "organize keywords", "keyword mapping", "topic clusters", "keyword grouping", "build a content plan", "pillar pages", "topical authority", "semantic clustering", or pastes a list of keywords.

Generate and maintain llms.txt and llms-full.txt — the emerging standard telling LLM-powered crawlers what content matters most on your site. Use when the user asks "llms.txt", "llms-full.txt", "LLM-friendly file", "tell AI crawlers about my site", "AI-readable manifest", "site summary for LLMs", "llms file", "generative AI sitemap", or wants their site optimized for AI crawler ingestion.

Comprehensive single-page optimization across all three layers — traditional SEO (title / meta / H1 / internal links), AEO (chunk quality, direct-answer leads, FAQ extraction), and GEO rewrites using the tactics published by the Princeton/IIT Delhi GEO study (citation integration, expert quotes, statistics enrichment, fluency optimization, authoritative tone). Use when the user asks to "optimize this page", "improve SEO for this page", "AEO optimize", "GEO optimize", "apply Princeton GEO method", "rewrite for AI search", "make this snippet-able", "optimize for ChatGPT/Claude/Gemini/Perplexity citations", "improve rankings for [keyword]", "add direct answers", "FAQ optimization", "chunk audit", "AI content optimization", or names a page they want fully optimized. Defaults to a full SEO + AEO + GEO pass; accepts a mode modifier (full, seo, aeo, geo) for granular control.

Fast-path schema generator — produce, audit, and validate JSON-LD structured data for a single page or file in one turn. This is the default for any schema question. Use when the user asks to "generate schema", "add JSON-LD", "structured data", "schema markup", "rich snippets", "add schema.org", "LocalBusiness schema", "FAQ schema", "Product schema", "Article schema", "HowTo schema", "Organization schema", "BreadcrumbList schema", "audit schema", "fix schema errors", "Recipe schema". **Do not invoke the `schema-generator` agent unless** the user explicitly says "bulk schema", "across my site", "all pages", "every page", or names a path containing 3+ pages to fix. The agent is the autonomous multi-file path that writes into source; this skill is the one-page fast path that proposes JSON-LD inline.

Single audit entry point — surface-level scorecard or deep infrastructure dive depending on the requested mode. Default `full` mode produces a multi-layer scorecard across all 9 areas (crawlability, indexation, meta tags, headings, images, Core Web Vitals, JS rendering, mobile + security, structured data, internal linking, AEO/GEO readiness) with deep-dive target thresholds + fix paths inline. `quick` mode = scorecard only, surface-level pass/fail. `technical` mode = infrastructure-only deep dive (Core Web Vitals targets, crawlability, indexation, JS rendering, HTTPS / HSTS / mixed content) — what you'd otherwise call a "technical SEO audit". Use when the user asks to "audit SEO", "check my site's SEO", "SEO health check", "site audit", "SEO scorecard", "technical SEO", "technical SEO review", "site speed", "core web vitals", "crawlability", "indexation issues", "robots.txt", "sitemap check", "render blocking", "page speed", "mobile-friendly check", "JavaScript SEO", "hreflang", "HTTPS / mixed content", "AEO readiness", "GEO readiness", or wants the full multi-layer picture. Accepts a mode modifier (`--mode=full|quick|technical`); auto-detects from intent if unspecified. **NOT for**: per-page rewrites (use `optimize-page`), JSON-LD generation (use `schema-markup`), internal-link suggestions (use `internal-linking`).

Run one iteration of proteus memory-summary evolution. Called by meta_harness.py.

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Use this skill when reverse-engineering Apple platforms with the ipsw CLI — analyzing iOS/macOS Mach-O binaries, disassembling functions inside dyld_shared_cache (DSC), dumping Objective-C/Swift headers from private frameworks, extracting kernelcaches, KEXTs, SEP, iBoot, or DeviceTree from IPSWs/OTAs, decompiling/querying sandbox profiles (SBPL), querying entitlements, symbolicating crashes/panics, diffing two firmware versions, mounting IPSW DMGs, or downloading Apple firmware. Triggers on iOS/macOS internals, kernel research, dyld_shared_cache, KEXT diffing, sandbox/Seatbelt profile analysis (SBPL/SBASM/sandboxd), capability/IOKit queries, entitlement lookup, class-dump, IMG4/AEA handling, or vulnerability research on Apple platforms.

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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\".

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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Creates git commits following Conventional Commits format with type/scope/subject. Use when user wants to commit changes, create commit, save work, or stage and commit. Enforces project-specific conventions from CLAUDE.md.

Creates GitHub Pull Requests with automated validation and task tracking. Use when user wants to create PR, open pull request, submit for review, or check if ready for PR. Analyzes commits, validates task completion, generates Conventional Commits title and description, suggests labels. NOTE - for merging existing PRs, use github-pr-merge instead.

Merges GitHub Pull Requests after validating pre-merge checklist. Use when user wants to merge PR, close PR, finalize PR, complete merge, approve and merge, or execute merge. Runs pre-merge validation (tests, lint, CI, comments), confirms with user, merges with proper format, handles post-merge cleanup.

Handles PR review comments and feedback resolution. Use when user wants to resolve PR comments, handle review feedback, fix review comments, address PR review, check review status, respond to reviewer, verify PR readiness, review PR comments, analyze review feedback, evaluate PR comments, assess review suggestions, or triage PR comments. Fetches comments via GitHub CLI, classifies by severity, applies fixes with user confirmation, commits with proper format, replies to threads.

Guide for creating Claude Code skills following Anthropic's official best practices. Use when user wants to create a new skill, build a skill, write SKILL.md, update an existing skill, or needs skill creation guidelines. Provides structure, frontmatter fields, naming conventions, and new features like dynamic context injection and subagent execution.

Syncs agent daily memory and MEMORY.md to an Obsidian vault so notes are human-browsable. Use nightly or on demand.

Structured ideation before any implementation. Use when starting any non-trivial task.

Scaffolds and validates new superpowers skills. Use when creating a new skill for this repository.

Executes plans task-by-task with verification. Use when implementing a plan.

Triggers a secondary verification pass for any agent output containing factual claims, numbers, dates, or named entities before the output is acted on

Crawls a new codebase to infer stack, conventions, and key invariants, then generates a PROJECT.md context file for the agent

Handles PR review feedback by fetching comments, grouping issues, fixing one group at a time, and verifying before replies.

Detects skill name shadowing and description-overlap conflicts that cause OpenClaw to trigger the wrong skill or silently ignore one when two skills compete for the same intent.

Reviews whether a skill will trigger reliably, guide useful behavior, avoid overlap, and produce testable outcomes.

Validates that a skill's companion scripts declare their OS and binary dependencies correctly, and checks whether those dependencies are actually present on the current machine.

Scores a skill's description field against sample user prompts to predict whether OpenClaw will correctly trigger it — before you publish or install.

Reviews a ClawHub skill's source code for security risks before installation. Use before installing any new skill.

Parallel subagent execution for complex tasks. Use when a task has independent parallel workstreams.

4-phase root cause process before any fix. Use whenever you encounter an error.

Red-green-refactor discipline. Use when writing new functionality.

Bootstrap skill — teaches the agent how to find and invoke skills. Use when starting any new task or session.

Ensures tasks are actually done, not just attempted. Use before declaring any task complete.

Creates clear, reviewable implementation plans before executing. Use after brainstorming and before writing any code.

Detects when the agent is stuck in a loop and escapes systematically. Use when you notice repeated failures or loss of direction.

Writes a resumé card at session end so context survives channel switches — and auto-injects it as a primer at the start of any new session

Searches Reddit communities for OpenClaw pain points and feature requests, scores them by signal strength, and writes a prioritized PROPOSALS.md for you to review and act on.

Monitors memory compaction for failures and enforces a three-level fallback chain — normal, aggressive, deterministic truncation — ensuring compaction always makes forward progress.

Scans OpenClaw config directories for plaintext API keys, tokens, and secrets in unencrypted files — flags exposure risks and suggests encryption or environment variable migration.

Scores how well the current context represents the full conversation — detects information blind spots, stale summaries, and coverage gaps that cause the agent to forget critical details.

Proactively monitors estimated token usage during long sessions and triggers context-window-management before overflow, not after. Use at the start of any session expected to last more than 30 minutes.

Prevents context overflow on long-running OpenClaw sessions. Use when approaching context limits.

Audits cron-scheduled skills for session mode, token waste, and cost efficiency — and enforces concise-reply constraints on cron contexts

Walks the memory DAG to recall detailed context on demand — query, expand, and assemble cited answers from hierarchical summaries without re-reading raw transcripts.

End-of-day structured summary and next-session prep. Use at the end of each working day or significant work block.

Intercepts irreversible or destructive actions and requires explicit user confirmation before proceeding

YAML-based delegation grant ledger — issues, validates, and tracks scoped permission grants for sub-agent expansions with token budgets and auto-expiry.

Enforces per-skill execution budgets for scheduled cron skills — pauses runaway skills that exceed their token or wall-clock budget before they drain your monthly API allowance.

Weekly audit of all installed third-party and community skills for malicious patterns, stale credentials, and drift from last-known-good state.

Detects oversized files that would blow the context window, generates structural exploration summaries, and stores compact references — preventing a single paste from consuming the entire budget.

Breaks multi-hour tasks into checkpointed stages with resume capability. Use when a task is expected to take more than 30 minutes or multiple sessions.

Detects infinite tool-call retry loops from deterministic errors and breaks them before they drain context or budget

Monitors MCP server connections for health, latency, and availability — detects stale connections, timeouts, and unreachable servers before they cause silent tool failures.

Builds hierarchical summary DAGs from MEMORY.md with depth-aware prompts — leaf summaries preserve detail, higher depths condense to durable arcs, preventing information loss during compaction.

Parses OpenClaw's flat MEMORY.md into a structured knowledge graph — detects duplicates, contradictions, and stale entries, then builds a compressed memory digest optimized for system prompt injection.

Validates memory summary DAGs for structural integrity — detects orphan nodes, circular references, token inflation, broken lineage, and stale summaries that corrupt the agent's memory.

Compiles a daily morning briefing from active tasks, priorities, and pending items. Use at the start of each working day.

Orchestrates multiple parallel OpenClaw agents — tracks health, detects timeouts, reconciles conflicting outputs, and manages structured handoffs

Diagnoses OpenClaw provider, fallback, channel, MCP, and gateway config issues with read-only scans and stateful summaries.

Keeps OpenClaw's memory store clean, structured, and useful. Use at session end and during periodic maintenance.

Detects and intercepts prompt injection attempts in external content before the agent acts on them

Tracks required validation gates, records pass/fail/waived results, and reports readiness before task completion.

Audits which skills have access to secrets, flags stale or unrotated credentials, and prompts rotation. Use weekly to keep credentials clean.

Imports OpenClaw session transcripts into a local SQLite database with FTS5 full-text search — the agent never loses a message, even after context compaction or session rollover.