Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Proactively add a rule to `.chief/_rules/`. Counterpart to `/chief-retro` (which proposes rules reactively from observed patterns). Use when the user has a rule in mind and wants to capture it now. Interviews the user (what / why / when / examples), classifies the rule into a category (_standard / _contract / _goal / _verification), shows a draft, and writes after confirmation. One rule per invocation.
Use Codex CLI subtasks to conduct multi-source deep research and synthesize a report with sources. Use for research that genuinely requires gathering evidence from multiple angles, not for a single verification or summarizing material that is already provided.
Run a grilling interview on a local browser page instead of the terminal. Every question is laid out with its recommendation, answerable in any order, with a per-question discussion thread and one "Send to Agent" button. Use when the user says "grill with ui", invokes /grill-with-ui with a topic, or says "/grill-with-ui resume".
Clone ChatGPT saved memory into Letta, then optionally enrich it with broader conversation history. Designed for a slick onboarding flow that extracts hidden saved-memory/context blocks, builds Letta-ready previews, and only asks questions at meaningful checkpoints.
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
Use when a recurring cross-session digest should be produced and delivered via Hermes cron — aggregates session activity, cron job outputs, memory changes, and tool usage stats into a compact summary for delivery via messaging platforms. Surfaces outstanding tasks and cross-session context that's invisible on chat platforms.
Personalized 7-phase onboarding wizard that scans the codebase, detects tech stack, recommends skills and MCP servers, and generates an improvement plan with readiness score. Includes safety checks and project-scoped configuration. Use when setting up OrchestKit for a new project or rescanning after major changes.
SkillOpt-flavored offline training loop for any SKILL.md. Treats accumulated learn-rule corrections as training trajectories, proposes bounded patches via an optimizer LLM, gates each candidate against a held-out validation set built from the user's own past corrections, and ships only candidates that demonstrably improve the score. Inspired by Microsoft SkillOpt's ReflACT pipeline (rollout → reflect → aggregate → select → update → evaluate) adapted to pro-workflow's SQLite store. Use when a skill has accumulated 8+ learn-rule rows and the user wants the skill itself to get better, not just longer.
Use when writing nodes to Искрон—phenomena, kriyas, vimarshas, contours, and roles. Triggers: “запиши”, “зафиксируй”, “добавь узел”, “создай крию”, “положи в граф”, create phenomenon, add to graph, iskron_add—and always before iskron_add_*, iskron_batch, and iskron_update when appending to a body. Also use when it's unclear which node type, given_as, or modes to choose. Even for a single entry, one wrong type propagates across the entire graph. Distinct from design (paths from goals), weaving (repairing existing structure), and methodology-work (evolving the methodology itself).
Build multi-step AI content creation pipelines combining image, video, audio, and text. Workflow examples: generate image -> animate -> add voiceover -> merge with music. Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling. Use for: YouTube videos, social media content, marketing materials, automated content. Triggers: content pipeline, ai workflow, content creation, multi-step ai, content automation, ai video workflow, generate and edit, ai content factory, automated content creation, ai production pipeline, media pipeline, content at scale
Create, migrate, and optimize skills for this alicloud-skills repository. Use whenever users ask to add a new skill, import an external skill, refactor skill structure, improve trigger descriptions, add smoke tests under tests/**, or benchmark skill quality before merge.
Master Archon MCP for strategic project management, task tracking, and knowledge base operations. The strategic layer (WHAT/WHEN) that coordinates with Skills (HOW). Use when managing projects, tracking tasks, querying knowledge bases, or implementing the Archon+Skills two-layer architecture.
Design exploration using parallel agents through a 7-phase process: topic analysis, memory context, divergent ideation (10+ ideas), feasibility filtering, evaluation with devil's advocate scoring (0-10 across 7 dimensions), synthesis of top approaches, and trade-off comparison. Supports open exploration, constrained design, comparison, quick ideation, and iterative optimization modes. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Reach sites that BLOCK ordinary automation — the same navigate/inspect/interact/report loop as the `browse` skill, but on browse's camoufox runtime: a hardened Firefox with C++-level fingerprint spoofing, geoip, and humanized timing that passes Cloudflare Turnstile, Google "unusual traffic", DataDome, and PerimeterX, with named profiles, authenticated sessions, and proxy rotation. Honest about its limit: fingerprint spoofing alone will not defeat IP-reputation blocks — pair it with a residential proxy. Use when a page challenges or 403s a normal browser.
On-demand pattern extraction from a specific GitHub codebase, given a focused query — "how does shadcn/ui implement the design system", "how does opencode use effect-ts", "how does base-ui handle composition" — when no pre-distilled static rule pack exists yet. Distills the generic pattern-extraction moves — classify the query before grepping (component / composition / state / effect / error / build / routing), grep before reading whole files, treat tests and examples/ as canonical intent, follow imports outward for the public surface, follow usages inward for variants, filter boilerplate / legacy / test scaffolding to surface load-bearing code, and capture findings to /knowledge/libraries/ for reuse. Dynamic light sibling of static code-atlas skills (opencode-ts, openai-codex-rust-patterns, nextjs-ppr-patterns). Triggers on "show me how <library> implements X", "find the <pattern> in <repo>", "distill <library>", and ad-hoc /distill-<library>-style invocations.
How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and write reports into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the reports it wrote or edited, reading the follow-up checks still measuring a report, and assessing a scout's health and performance over time (cadence, success rate, report rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/report nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".
Use lasso, the web UI and MCP server over herdr, to spawn and manage coding agents across machines, notify the human who runs it, show them a file, and drive the shared browser they watch live. Use when you have lasso's MCP tools (create_agent, list_agents, notify, open_file, shared_browser, ...) or the `lasso` CLI, when asked to "spin up an agent", "start a worktree agent", "close yourself", "ping me", "open it for me", "show me in the browser", or when $HERDR_PANE_ID is set inside a lasso-created pane.
Create, validate, and consume Google Open Knowledge Format (OKF) v0.1 bundles — YAML-frontmatter Markdown knowledge atoms for portable, interoperable AI-agent sharing. Routes: use `okf-agent-memory` for the separate OKF v0.2 Go CLI/MCP project-memory runtime, `llm-wiki` for raw source capture and vault maintenance, `obsidian` for vault automation, `graphify` for durable committed graphs, and `scrapling` for web-content extraction. Triggers on: Google OKF v0.1, open knowledge format, knowledge bundle, OKF document, LLM wiki standard, knowledge atom, agent context format, OKF frontmatter, OKF bundle, knowledge interoperability
Product orchestrator for Prodig Suites that guides product planning, routes work to specialists, and coordinates execution across the product lifecycle. Use when the user is shaping a digital product, deciding next steps, or needs help choosing the right Prodig specialist or workflow. Triggers: 'prodig', 'digital product', 'product idea', 'product planning', 'which product agent should I use', 'SaaS planning', 'course creation', 'template pack', 'productized service'.
Deep retro of retros - investigation pass that surfaces improvements across older retros and the current ax setup. External AI agent drives the reasoning with high thinking enabled. Triggers when user says "deep retro", "investigate ax", "what should I fix in my setup", "retro of retros", or invokes /ax:retro-meta. Use after `ax:retro` if proposals are sparse or you want broader exploration.
Teaches agents to recognize and avoid security threats during normal activity. Covers phishing detection, credential protection, domain verification, and social engineering defense. Use when building or operating agents that access email, credential vaults, web browsers, or sensitive data.
Coordinate multiple terminal coding agents through the Superset CLI by creating isolated workspaces, launching workers, sending follow-ups, reading terminal output, tracking dependencies, and collecting structured results. Use when asked to delegate or parallelize coding work, coordinate agents across workspaces or hosts, hand work between agents, monitor worker progress, or run a multi-agent workflow with Superset. Do not use for ordinary single-agent workspace, task, or automation management.
Use when the user explicitly asks for vibe brainstorming, creative implementation ideas, implicit expected behavior, or convention checks, and when an implementation task is creative or convention-dependent. Do not use for obvious mechanical edits.
Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent, agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop
Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automation script, ai orchestration
Personal long-term memory and first-use onboarding. Use when personal background, preferences, or history may affect the answer, including food and travel recommendations, personal plans, writing emails to match the user's habits, and continuing previous tasks; the user need not say “remember” or name Bailian. Also read this Skill when the user shares a long-term fact, corrects a preference (“be more natural from now on”), confirms a lasting decision (“let’s choose the second option and do that from now on”), or asks to remember, recall, view, forget, pause, or resume; use the preceding context, not just the last sentence. Check local state first; guide setup if unconfigured, then retrieve and extract life/work memories after authorization. Host-provided memory does not replace this Skill. Also handles explicit bl memory resource operations. Do not trigger for pure calculations, general knowledge, or translation/writing without personalization; do not upload before authorization.
Connect the Vercel AI SDK to iMessage. Build AI agents and assistants that communicate over iMessage using chat-adapter-imessage, the official Photon adapter. Local mode (runs on your Mac) and remote mode (Photon's production infrastructure). Covers createiMessageAdapter, postMessage, editMessage, deleteMessage, react, startGatewayListener, types, and integration with the Chat SDK. Keywords: vercel ai sdk, imessage, chat adapter, ai agent, chatbot, conversational ai, messaging, apple messages, vercel, nextjs, ai assistant, chat sdk, real-time, macos.
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.
Multi-engine long-term memory for game AI agents. Deep Godot 4.7+ (99 skills, 27 genre blueprints) + Bevy docs-first parity — audited patterns, persona orchestration (Analyst · Auditor · Builder), production-grade engineering.
Author-then-adversarial-review authoring pass for a single OpenSpec artifact — a subagent drafts it, `hrt-artifact-lint` clears the deterministic structure checks, then two subagents review it concurrently in fresh isolated contexts (a Destructive Critic for content/logic, a Structural Auditor for the structural judgement calls a linter cannot make), findings resolved before finalizing. Parameterized by artifact (specs or design). Use during the tempa-spec schema's `specs` and `design` phases.
Use when the context window fills too fast, per-turn input token cost is too high, or the aggregate input token bill needs explaining and fixing. Two audits: (1) overhead — tool schemas, skills index, system fixed text, user profile, memory recall measured live, ranked, reduced; (2) cost accounting — total input token volume across sessions, cron fires, and tool traffic, with cache-aware interpretation, pre-checked config levers, and scheduled delta checks. scripts/audit_overheads.py for per-turn overhead, scripts/audit.py for totals.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.