Claude Code Skills · page 22

Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground `ui.*` operation call, including single-shot prompt rewrites.

>

>-

>

Migrate or upgrade TypeScript tooling in the Phoenix monorepo. Use when upgrading TypeScript versions, switching tools (ESLint to oxlint, Prettier to oxfmt), upgrading bundlers (Vite, esbuild), or making major dependency upgrades. Triggers on requests to migrate, upgrade, or replace TypeScript/JavaScript tooling.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

An advanced skill for L3 autonomous loops. When the token budget nears exhaustion, the agent analyzes its ROI and autonomously drafts a negotiation request for a budget increase rather than silently failing.

>

>

>

>

>

>

Check token budget and run-log spend before and after a loop run. Enforces early exit when over budget or when there is no actionable work.

>

>

>

>

>

Build a new holaOS app using @holaboss/app-builder-sdk (5 backend primitives + optional shadcn dashboard UI). The canonical path for vibe-coded apps — integration modules AND dashboard apps both live here.

Analyze your audience demographics, behavior, and preferences for targeted content.

Use when working in the embedded browser and you want the cheapest reliable interaction loop.

Use when validating or debugging a workflow in the embedded browser and you need a reproducible, evidence-first loop.

Build the visual layer of a holaOS dashboard app — TanStack Start + @holaboss/ui + workspace tokens. Use when an app has SDK primitives wired (via app-builder-sdk) AND needs a `src/client/` UI surface. NOT for marketing pages, NOT for snapshot HTML reports.

Guide for assembling a workspace capability (skills + integrations). Use when the user wants to create a capability.

Research competitors and produce a weekly digest of their positioning, launches, and messaging.

Plan and organize your content calendar with AI-driven scheduling and topic suggestions.

Generate high-quality written content for any platform with AI-powered drafting and editing.

Provision a production-ready teammate only after its stable responsibilities, prerequisites, and reusable operating guidance are understood.

Guide for modifying an already-installed workspace capability — adding or editing its MCP servers, skills, integrations, or agent guidance. Use when the user wants to customize, change, or extend an existing capability.

Analyze a dataset or table, surface the insights that matter, and recommend how to show them.

Draft clear, well-structured emails — replies, outreach, and follow-ups — in the right tone.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Brainstorm creative content ideas tailored to your niche, audience, and goals.

Create custom images, graphics, and visual content using AI image generation.

This skill is for interface design — dashboards, admin panels, apps, tools, and interactive products. NOT for marketing design (landing pages, marketing sites, campaigns).

Draft a LinkedIn post in the user's voice from a topic, note, or rough idea.

Add or update workspace MCP servers using holaOS mcp_registry syntax.

Turn raw notes or a transcript into a clean summary with decisions and action items.

Generate comprehensive performance reports with insights and actionable recommendations.

Turn a feature idea into a clear PRD — problem, users, requirements, and success metrics.

Draft client proposals and statements of work that scope, price, and win the project.

Turn one source piece (blog, newsletter, transcript) into platform-native posts for X and LinkedIn.

Guide for creating effective skills. Use when creating or updating a skill.

Install workspace skills from curated sources or import open-source skills from GitHub.

Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.

Condense long articles, reports, and documents into clear, concise summaries.

Transform content tone to match any audience — professional, casual, witty, or formal.

Translate content across languages while preserving brand voice and cultural nuance.

Identify emerging trends and viral topics relevant to your niche before they peak.

Create short videos and motion clips from a text description using AI video generation.

Research topics across the web and compile structured insights for content creation.

>

Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action plan.

Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.

>

Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

AI crawler access analysis. Checks robots.txt, meta tags, and HTTP headers to determine which AI crawlers can access the site. Provides a complete access map and recommendations for maximizing AI visibility while maintaining appropriate control.

Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

Platform-specific AI search optimization — audit and optimize for Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot individually

>

>

Generate a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions

Generate a professional PDF report from a GEO audit using pandoc + Chrome headless. Converts GEO-AUDIT-REPORT.md into a styled, client-ready PDF with a cover page, color-coded score tables, severity-tagged findings, and a 90-day roadmap.

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

Technical SEO audit with GEO-specific checks — crawlability, indexability, security, performance, SSR, and AI crawler access

Pull the latest GEO-SEO skill updates from the upstream repository. Compares installed files against the latest release, shows what changed, and updates all skills, agents, scripts, and schema templates in place.

SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.

Required before any hive-browser CLI command. The browser is driven from the terminal by running `hive-browser <command> ... --json` via terminal_exec — not via MCP tools. Teaches the browser lifecycle rules (the bridge attaches to the USER'S running Chrome — never kill or launch browser processes; timeouts are transport issues, not crashes), the screenshot + coordinate workflow (hive-browser interact with a fractional coordinate) that reaches shadow-DOM inputs selectors can't see, the viewport-fraction coordinate rule (not pixels), rich-text editor quirks ("send button stays disabled" failures), and CSP gotchas. Covers Chrome via CDP through the GCU Beeline extension.

Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel charts, missing downloads, and the agent writing markdown image links by hand instead of letting chart_render drive the UI.

Claim tasks, record step progress, and verify SOP gates in the colony SQLite queue. Applies when your spawn message includes a db_path field.

Proactively extract critical values from tool results into working notes before automatic context pruning destroys them.

Follow a structured recovery decision tree when tool calls fail instead of blindly retrying or giving up.

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.

Maintain a free-form scratchpad of decisions, extracted values, and open questions so context pruning doesn't lose anything you still need.

Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF, password-protect a PDF, or render PDF pages as images.

Periodically self-assess output quality to catch degradation before the judge does.

Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept replies. Use when the user asks to "set up Slack notifications", "get pinged on Slack", "connect Slack for alerts", "set up Sentinel on Slack", or clicks the in-app "Set this up with the agent" button on the Slack channel step. Requires hive.browser-automation.

Set up a Telegram notification channel (Sentinel) for a colony by driving the browser — create a bot via @BotFather in Telegram Web, store its token, detect the chat to notify, and turn Sentinel on so the colony can ping the user on Telegram and accept replies. Use when the user asks to "set up Telegram notifications", "get pinged on Telegram", "connect Telegram for alerts", "set up Sentinel on Telegram", or clicks the in-app "Set this up with the agent" button on the Telegram step. Requires hive.browser-automation.

Required reading whenever any shell_* tool is available. Teaches the foreground/background dichotomy (terminal_exec auto-promotes past 30s, returns a job_id you poll with terminal_job_logs), the standard envelope shape (exit_code, stdout, stdout_truncated_bytes, output_handle, semantic_status, warning, auto_backgrounded, job_id), output handle pagination via terminal_output_get, when to read semantic_status instead of raw exit_code (grep/rg/find/diff/test exit 1 is NOT an error), the destructive-warning surface (rm -rf, git push --force, DROP TABLE), tool preference (the terminal handles ALL file read/write/edit/search; use gcu-tools / hive_tools where they fit), and the bash-only-on-macOS policy. Skipping this leads to "tool returned no output" surprises, orphaned jobs, and panic over benign grep exit codes.

Use terminal_rg / terminal_glob for all filesystem search — your project tree as well as system configs, /var/log, /etc, archive contents. Teaches the rg vs glob vs terminal_exec("find/ls/du/tree") split, common rg flag combos for code/logs/configs, glob patterns for finding files by name, the rule that mtime/size/type predicate queries drop to terminal_exec("find ..."), and that for tree views or single-file stat info you should just use terminal_exec instead of inventing a tool. Read before reaching for raw shell to grep or find anything.

Use when launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, the `wait_until_exit=True` blocking-poll idiom, the truncated_bytes_dropped resumption signal, the merge_stderr decision, the SIGINT→SIGTERM→SIGKILL escalation ladder via terminal_job_manage, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start, or right after terminal_exec auto-backgrounded.

Use when you need state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

Concrete patterns for breaking colony work into parallel worker jobs via run_playbook — when fan-out helps, how to model the goal as a tracker table, write the worker skill, author the playbook, pilot, and let convergence retry/resume the gap.

Author a new Agent Skill for a Hive agent that conforms to the Agent Skills specification (SKILL.md with YAML frontmatter, optional scripts/references/assets directories). Use when the user asks to create, scaffold, add, or package a new skill for a Hive agent.

Read before automating X / Twitter with browser_* tools. Verified flows for post, reply, delete, search-and-engage, plus the Draft.js compose quirks that silently disable the send button. Includes the daily-reply and job-market-reply playbooks. Requires hive.browser-automation for the underlying screenshot + coordinate workflow. Verified 2026-04-11.

Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.

First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.

Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.

Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for save_plan, output_iframe, or slack_attach_html.

当任务需要写代码实际获取A股数据时使用——拉取行情/K线(mootdx+腾讯+百度)、研报(东财+同花顺+iwencai)、信号(热点/北向/龙虎榜/解禁/行业)、资金面(融资融券/大宗/股东户数/分红/资金流)、新闻、财务三表/F10、公告(巨潮)、打板(涨停池/连板/炸板率/重点监控池/日内异动)、ETF期权(T型报价/希腊字母/IV)、舆情互动(互动易/热榜/人气榜)、筹码分布(获利比例/成本区间)、复权因子、估值历史(PE/PB/PS+换手率+ST)、申万行业变迁史、宏观(社融/PMI)等真实数据。十一层数据源·54端点(含3官方备胎)·内嵌全部可运行代码，自包含零依赖外部文件；优先用通达信(mootdx)/腾讯(不封IP)，东财接口已内置限流防封，主源被封可查「备用源速查」降级。仅在需要调用数据接口取数时使用：A股概念解释、投资观点讨论、策略问答等无需取数的话题不要加载本skill。

Spin up a live local Omnigent server + runner and exercise the native Antigravity (agy) TUI harness (antigravity-native) end-to-end — launch the real `agy` CLI via `omnigent antigravity`, drive turns through the web UI, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity-native harness (omnigent/inner/antigravity_native_executor.py, omnigent/antigravity_native.py, antigravity_native_bridge.py, antigravity_native_rpc.py, antigravity_native_reader.py, antigravity_native_launch.py) or its agy launch / RPC mirror / tmux delivery / OAuth / MCP-relay behavior. NOT the in-process `antigravity` Gemini SDK harness.

Spin up a live local Omnigent server and exercise the Antigravity (Gemini) SDK harness end-to-end — build antigravity agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity harness (omnigent/inner/antigravity_executor.py, antigravity_harness.py, omnigent/onboarding/antigravity_auth.py) or its auth / model / tool-bridge behavior.

Document a module or public API surface (functions, classes, CLI commands, endpoints) from the code itself. Use when the user asks for API reference, to document a module, or to write usage docs for a public interface.