Anthropic
AICommonly used with
Skills using Anthropic (14779)
autoskill
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
sync-docs
Sync documentation with code. Use when user asks to update docs, check docs, fix stale documentation, update changelog, or after code changes.
career-ops
AI job search command center -- evaluate offers, generate CVs, scan portals, track applications. Use when the user pastes a job URL or JD, asks to scan portals, generate a CV/PDF, track applications, prepare for interviews, draft outreach/emails, or run any career-ops mode.
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
axolotl
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
data-scientist
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.
deep-research
Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
deepspeed
Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
huggingface-accelerate
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
knowledge-distillation
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
long-context
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.
model-merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
model-pruning
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
moe-training
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
nemo-curator
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
pytorch-lightning
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
ray-train
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
speculative-decoding
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
training-llms-megatron
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
agency-os
Notion-as-source-of-truth dispatch board for running your work like an AI agency. One Tasks database is the source of truth; tasks flow Suggestion through Discussion, To-Do, In Progress, and Done with subtasks, recurring cadences, dependencies, and template subtrees. Batch execution fans approved To-Do rows out to parallel agents with per-task model selection. Use when capturing chat to Notion, running the To-Do queue, suggesting, approving, or discussing tasks, or coordinating multi-task batches. Trigger with "/agency-os" subcommands or natural-language variants ("add a suggestion: …", "let's discuss X", "run the queue").
audit
Use when the user wants a code review on recent changes — quality, spec, security, or performance feedback. Triggers a multi-level (L1-L5) review with a standalone Reviewer; on NEEDS_FIX, offers to apply findings via /hyperflow:plan. Trigger with /hyperflow:audit, "review this change", "review my PR", "audit the diff", "code review".
background
Use when the user wants to see, inspect, cancel, or prune background agents fired during prior chain runs. Read/manage `.hyperflow/background/registry.json` and the per-agent output buffers at `.hyperflow/background/<id>.md`. Standalone — never auto-invoked. Trigger with /hyperflow:background, "list background agents", "what's running in background", "cancel background agent", "show background result".
bridge
Use when the user wants hyperflow's behavioral rules to apply outside the terminal CLI — in Claude Code Desktop, claude.ai web, or IDE extensions that don't load CLI plugins. Writes a managed doctrine block into the project's CLAUDE.md so autonomy + intent-routing + commit cadence + role separation + file-first rules carry over. Lossy (no slash commands, no actual skill dispatch) but useful. Trigger with /hyperflow:bridge, "make hyperflow work in desktop", "make hyperflow work in claude.ai", "embed hyperflow doctrine in CLAUDE.md", "portable hyperflow rules".
buzz-devrel
Developer relations playbook builder — produces a DevRel program design covering community platform, contributor program tiers, ambassador criteria, event strategy, and success metrics. Use when asked to "build a DevRel program", "design our developer community", "create a contributor program", "start a developer advocacy program", or "how do we grow our dev community".
buzz-hn
Hacker News post crafter — given a product, feature, or story produces a ready-to-post HN submission (title ≤80 chars, no marketing), honest body text with technical depth, no outbound links, predicted reception analysis, and comment-response templates for likely pushback. Use when asked to "write an HN post", "craft a Show HN", "prepare our Hacker News launch", or "help me post on HN".
buzz-outreach
Media and podcast outreach personalizer — takes a story angle and target journalist or host list and produces personalized pitch emails per target. Use when asked to "write media pitches", "pitch this story to journalists", "get us on podcasts", "write press outreach", or "personalize pitches for these contacts".
cache
Use when the user wants to view, search, add, edit, prune, archive, or clear hyperflow memory entries. CRUD interface for `.hyperflow/memory/` — never modifies source code, only memory files. Trigger with /hyperflow:cache, "show memory", "search memory for X", "clear memory", "what does hyperflow remember about Y".
deal-outreach
Cold outbound sequence builder — produces multi-touch email + LinkedIn sequences (5-7 touchpoints) personalized by persona type (technical buyer, economic buyer, champion). Use when asked to "write cold emails", "build an outbound sequence", "create prospecting emails", "write my LinkedIn outreach", or "design a cold email campaign".
deal-proposal
B2B proposal generator — takes deal context (ICP, pain, pricing tier, timeline) and produces a complete proposal document with executive summary, problem statement, solution, pricing table, implementation timeline, ROI case, and next steps. Use when asked to "write a proposal", "draft our deck for this deal", "build a proposal for this customer", or "generate a proposal".
deal-qualify
MEDDPICC-based deal qualification worksheet — guide Deal through structured qualification of any opportunity and produce a filled card + recommended next action. Use when asked to "qualify this deal", "run MEDDPICC on this opportunity", "should we pursue this", or "is this deal real".
deploy
Use when ready to ship — runs pre-push gates (lint, typecheck, build, tests, security sweep), commits, releases, and pushes. Standalone, never auto-invoked. Push always requires explicit confirmation. Trigger with /hyperflow:deploy, "ship it", "ready to push", "release", "cut a release", "deploy".
design
Use when the user wants the visual/experiential design of a product done systematically — a design system, a screen, a landing page, or a visual identity — grounded in researched real-world prior art and free of AI slop. Establishes/extends the design system, invokes the matching local taste skill, dispatches the designer specialist, and reviews for taste + accessibility. Standalone — ends with a handoff gate into the chain. Trigger with /hyperflow:design, "design the UI", "make a design system", "design this screen", "give this a visual identity", "redesign this".
dispatch
Use when a task file exists in .hyperflow/tasks/ and workers need dispatching. Fans out parallel workers under per-batch Reviewers, runs a final integration review, and commits per sub-task. Endpoint of the auto-chain — no auto-deploy. Trigger with /hyperflow:dispatch, "run the plan", "execute the task", "build it", "run the batches".
flush
Use when the user wants to manually flush a deferred-commit queue from a prior or interrupted chain. Reads .hyperflow/commits-queue/manifest.json, fast-forwards the staging branch onto the user's branch, deletes staging, clears the queue. Recovery interface when a chain crashed before its Step 4 auto-flush ran. Trigger with /hyperflow:flush, "flush pending commits", "flush queue", "apply staged commits", "where are my commits".
handoff
Use when managing a two-session handoff — inspecting, picking up, or reviewing a committed handoff package produced by a session=two scope run. The operator interface over the cross-environment handoff lifecycle (plan in one session, build in another, review back in the first). Trigger with /hyperflow:handoff, "list handoffs", "pick up the handoff", "review the handoff build".
hyperflow
Use when applying Hyperflow's orchestration doctrine in Codex, Antigravity, Grok, or another single-agent surface. Auto-invoke for non-trivial engineering work: build, implement, add, refactor, debug, fix, review, audit, plan, scope, design, brainstorm, ship, or deploy. Trigger with /hyperflow:hyperflow, "use hyperflow", "apply the doctrine", or automatically on any task-shaped message.
ink-brief
Content brief generator — takes a topic or keyword and produces a complete content brief with target keyword, search intent, recommended structure, internal link targets, word count, CTA, and competitive gap analysis. Use when asked to "write a content brief", "brief this blog post", "plan this article", or "what should we cover for [keyword]".
ink-cluster
Topic cluster architecture builder — takes a core topic and maps the full cluster with 1 pillar page, 6-10 supporting posts, internal linking map, keyword targets, and estimated monthly search volume per piece. Use when asked to "build a content cluster", "map our SEO cluster for [topic]", "create a topic cluster", or "what should our pillar page be about".
ink-distribute
Content distribution plan — takes a completed piece and produces a channel-by-channel plan covering HN, Reddit, LinkedIn, newsletter, and Twitter/X, with timing, per-channel framing, and a repurposing plan. Use when asked to "distribute this post", "how do we promote this article", "write distribution copy for this piece", or "where should we share this".
issue
Use when starting a chain from a GitHub issue — turning an issue URL or number into a triaged, planned, dispatched, and reviewed pull request. Classifies the thread (bug → root-cause discipline, feature → plan chain, question → drafted reply), synthesizes a spec from the issue's own acceptance criteria, then runs the standard chain with a PR exit. Trigger with /hyperflow:issue, "work on issue #N", "fix this issue <url>", "implement this issue", "triage issue #N and raise a PR".
keep-churn
Churn risk identification and intervention — scans health signals for at-risk accounts, classifies risk level (CRITICAL/HIGH/MEDIUM), and produces an intervention sequence per risk type. Use when asked to "find at-risk accounts", "who might churn", "build a churn prevention plan", "identify churn signals", or "rescue this account".
keep-qbr
Quarterly Business Review template generator — takes account info (ARR tier, adoption metrics, goals) and produces a complete QBR deck outline and talking points. Use when asked to "prepare a QBR", "build a quarterly review", "write our QBR agenda", or "create QBR talking points for this account".
keep-segment
Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".
pitch-copy
Landing page and marketing copy — write hero section, problem/solution blocks, proof points, and CTAs. Use when asked to "write landing page copy", "write the homepage", "marketing copy for this feature", "product page copy", "write the hero section", or "write copy for [surface]".
pitch-landing
Use when asked to structure a landing page for positioning, plan a conversion-optimized page layout, or design a launch page. Examples: "landing page for product launch", "conversion-optimized layout for SaaS"
pitch-launch
Produce an actual launch plan with announcement copy, channel sequence, and day-1 checklist. Use when asked to "plan a launch", "GTM strategy", "how do we announce this", "launch plan for [feature]", "go-to-market", "write our Product Hunt post", or "how do we get people to notice this".
pitch-message
Messaging framework — produce a full headline, subheadline, proof points, and CTA hierarchy for use across all surfaces. Use when asked to "write our messaging", "messaging framework", "what should our headline say", "copy hierarchy", "tagline and messaging", or "how do we talk about the product".
pitch-position
Produce a complete positioning document using the Dunford framework — competitive alternatives, unique attributes, value, best-fit customer, market category, positioning statement, and tagline. Use when asked to "write our positioning", "define our value prop", "positioning statement", "what market are we in", "how do we position against X", "what's our tagline", or "write our messaging foundation".
pitch-recon
Marketing and messaging reconnaissance — read existing landing pages, copy, positioning docs, and marketing materials to understand the current messaging state. Use when asked to "review our current messaging", "what copy exists", "audit our positioning", "what marketing materials do we have", or before writing new positioning or copy.
plan
Use when a request needs shaping before any code is written — a rough or vague prompt to sharpen, an ambiguous idea to design, or a clear-enough task to decompose. One chain-starter that amplifies the prompt, designs the approach, and decomposes it into a batched task file, skipping whichever phases the request doesn't need, then STOPS at a build-location gate (build here, hand off to another session, or just keep the plan). Plan never implements. Trigger with /hyperflow:plan, "design this", "plan this", "decompose this", "how should we", "what's the best way to", "break this down", "enhance this prompt".
pr
Use when reviewing an incoming GitHub pull request — runs the multi-level (L1-L5) audit against the PR's real diff range, posts findings as one batched review (inline, summary, or local-only), offers the standard fix chain on NEEDS_FIX, and optionally merges. The maintainer-side counterpart to /hyperflow:issue. Trigger with /hyperflow:pr, "review PR #N", "review this pull request <url>", "audit the PR", "check this contribution".
scaffold
Use when starting hyperflow in a new project, refreshing the .hyperflow/ cache, or installing auto-detection shims (AGENTS.md, CLAUDE.md). One-shot project setup; does not start the spec → scope → dispatch chain. Trigger with /hyperflow:scaffold, "init hyperflow", "set up hyperflow", "refresh hyperflow", "install hyperflow shims".
shipwright-pipeline
Autonomous app builder that converts plain-English descriptions into fully built, tested applications. Use when the user wants to build a new app, scaffold a project, generate a full-stack application, or create an app from a description. Trigger with "build me an app", "create a new app", "shipwright build", "scaffold a project", "generate an application".
status
Use when the user wants a one-screen view of current hyperflow project state — version, profile freshness, memory count, and live progress on every in-flight task. Read-only; never modifies state, never dispatches workers. Trigger with /hyperflow:status, "what is hyperflow doing", "show task progress", "where are we".
sticky
Use when the user wants to set auto-routing mode: on (every task-shaped message routes), auto (intent-verb messages route — default), or off (no auto-routing). Intent-detection runs by default; use this skill to expand to full sticky or disable entirely. Trigger with /hyperflow:sticky, "make hyperflow sticky", "stop using hyperflow", "is hyperflow sticky", "auto-route to hyperflow", "disable hyperflow auto-routing".
surge-activation
Use when asked to improve activation, map the growth funnel, identify growth levers, design a referral program, build a retention playbook, develop a PLG strategy, or find where to invest in growth. Examples: "how do we grow faster", "improve our activation rate", "design a referral program", "build a retention playbook", "what are our best growth levers", "map our growth funnel".
surge-experiment
Growth experiment design — structure a growth hypothesis, define metric, baseline, expected lift, and kill condition for a single experiment. Use when asked to "design a growth experiment", "test this growth idea", "experiment framework", "how do we test if this works", or "growth hypothesis".
trace
Use when encountering bugs, test failures, runtime errors, broken builds, or "this doesn't work" reports. Systematic root-cause analysis before any patch — never blind-patches symptoms. Standalone, ends with a final-integration review of the fix. Trigger with /hyperflow:trace, "debug this", "find the root cause", "why is this failing", "this test is broken".
workflow
Use when a task is too large for turn-by-turn orchestration and should run through the big-task workflow lane: system-wide changes, large migrations, repo-wide audits, high-confidence verification, or tasks explicitly asking to run a workflow. Claude Code uses native dynamic workflows; Codex, OpenCode, and Grok use the portable workflow adapter. Trigger with /hyperflow:workflow, "run a workflow", "dynamic workflow", "big task", "large migration", "repo-wide audit".
hns-lsel-applier
Local Self-Evolution Loop (LSEL) APPLY engine — the playback-only consumer of approved decision.json records that drives `.moai/hooks/lsel-apply.sh` for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001 M3). Reads an approved decision.json, validates the target against the frozen allowlist (.claude/lsel/frozen-allowlist.json), mechanically refuses execution-meta targets lacking a synchronous-approval marker, applies the referenced diff.patch, appends an apply-ledger.jsonl row, and commits one lsel-<proposal-id>-tagged Conventional Commit on the feature branch. M3 scope: APPLY bypass closure only (the frozen Go applier stays frozen — its write-flag at internal/harness/applier.go:22 stays false; REQ-LSEL-003).
hns-lsel-curator
Local Self-Evolution Loop (LSEL) curator — the CLUSTER + drain engine for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001). Companion-offset drain of .moai/lessons-inbox.jsonl with a drain-side severity filter that drops the ~65% Bash-timeout/sandbox noise, event_key clustering with a frequency gate, and a Generative-Agents-style 1-10 importance score. Candidates stage at .moai/state/lsel/clusters.json. M1 = drain only (NO PROPOSE, NO APPLY, NO memory/ writes).
hns-moaiadk-best-practices
moai-adk-go best-practices reference for the 4 harness specialists (cli-template-specialist, quality-specialist, workflow-specialist, hook-ci-specialist). Covers TRUST 5 gates, Go test isolation (t.TempDir, no OTEL env in parallel tests), hardcoding-prevention rules (env constants in envkeys.go, thresholds in defaults.go), the AskUserQuestion orchestrator-only boundary, the deferred-tool preload rule, the archived-agent rejection contract, and verification-claim integrity. Loaded by the specialists when authoring or reviewing moai-adk-go code.
hns-moaiadk-patterns
moai-adk-go domain-patterns reference for the 4 harness specialists (cli-template-specialist, quality-specialist, workflow-specialist, hook-ci-specialist). Covers the CLI/template/config/hook/spec subsystem architecture, key source paths, the Pipeline specialist delegation map, the Template-First build cycle, the namespace separation contract, and common add-a-template / add-a-hook / add-an-agent / add-a-SPEC workflows. Loaded by the specialists when working on moai-adk-go's own Go codebase and templates.
hns-oss-docs-i18n-rules
HARD i18n rules digest for the oss-docs harness specialists working on moai-adk-go README 4-locale set and the docs-site (adk.mo.ai.kr). Covers the canonical-locale chains, the 4-locale same-PR obligation, Mermaid TD-only, the no-emoji + icon-shortcode rule, emphasis-marker spacing, the URL blacklist, version SSOT, vercel.json redirect pattern, and the immutable Vercel binding. Loaded FIRST by every oss-docs specialist before any edit.
hns-oss-docs-readme-sync
README 4-file synchronization procedure for the oss-docs harness: Korean README.ko.md as primary source, en/ja/zh derivation, the shared language-switcher header contract, section-order parity checklist, and the manual verification recipe (no linter exists for READMEs). Loaded by the content-author and locale-translator specialists for any README work.
hns-oss-docs-structure-map
docs-site structure map for the oss-docs harness structure-curator: exact paths and schemas for hugo.toml, per-locale content/<locale>/_meta.yaml, data/menu/main.yaml (4-locale name maps + icon values), the icon-to-SVG-case coupling in layouts/partials/menu.html, shortcodes, the FROZEN moai-brand.css, vercel.json redirect examples, and the known design-vs-guides divergence. Loaded by the structure-curator before any navigation or config edit.
hns-oss-docs-verify
Mandatory verify recipe for the oss-docs harness — the runnable exit gate every specialist executes before returning: warning-free hugo build, sitemap existence, URL-blacklist grep, Mermaid LR/RL direction grep, 4-locale file-existence and section-count parity, README 4-file heading parity, and body-emoji scan. All checks are inlined here because docs-i18n-check.sh and gen_menu.py do not exist.
hns-workflow-ci-loop
Unified CI watch + auto-fix loop skill. Polls gh pr checks after /moai sync PR creation, classifies required vs auxiliary failures, attempts safe automated patches (max 3 iterations), and escalates semantic failures to the user. Use for CI loop workflow — NOT for general loop iteration patterns (see moai-workflow-loop).
moai-domain-backend
Backend development specialist covering API design, database integration, microservices architecture, and modern backend patterns. Use when designing APIs, implementing server logic, authentication, or authorization.
moai-domain-database
Database specialist covering PostgreSQL, MongoDB, Redis, Oracle, and cloud database platforms (Neon, Supabase, Firestore). Use for schema design, query optimization, indexing strategies, data modeling, or cloud database selection. Cloud vendor guide absorbed from moai-platform-database-cloud.
moai-domain-frontend
Frontend development specialist covering React 19, Next.js 16, Vue 3.5, and modern UI/UX patterns with component architecture. Use when building web UIs, implementing components, optimizing frontend performance, or integrating state management.
moai-domain-html-report
Markdown-to-single-file-HTML report renderer. Six modes (status, incident, plan, explainer, financial, pr) selected by report type, crossed with three audience tiers (expert, basic, learn) derived from the active output style. The basic and learn tiers enrich the HTML with mermaid flowcharts, worked examples, and plain-language primers; the expert tier stays dense. Zero external JS/CSS framework dependencies — inline SVG charts, a font-CDN exception for Korean readability, and a tier-gated mermaid-CDN exception. Self-contained output for email attachment, print, and offline viewing.
moai-domain-humanize
AI text humanization and 윤문 (post-editing) specialist that detects and removes AI tells while preserving meaning, facts, and figures. Covers Korean, English, Japanese, and Chinese with a shared severity model (S1/S2/S3), quality grades (A/B/C/D), and 30%/50% over-editing guardrails. Use to make AI-generated text read as human-authored without changing what it says (de-ai, naturalness pass).
moai-foundation-cc
Canonical Claude Code authoring kit covering Skills, sub-agents, plugins, slash commands, hooks, memory, settings, sandboxing, headless mode, and advanced agent patterns. Use when creating Claude Code extensions or configuring Claude Code features.
moai-foundation-core
Provides MoAI-ADK foundational principles including TRUST 5 quality framework, SPEC-First DDD methodology, delegation patterns, progressive disclosure, agent catalog reference, and token budget management (absorbed from moai-foundation-context). Use when referencing TRUST 5 gates, SPEC workflow, or context window optimization.
moai-foundation-quality
TRUST 5 quality principles and how MoAI enforces them through agents, the 3-level harness, /moai gate, and sync-auditor scoring. Use for code review, quality gate checks, coverage targets, or TRUST 5 compliance.
moai-foundation-thinking
Unified thinking toolkit: Creative frameworks (Critical Evaluation, Diverge-Converge, Deep Questioning), First Principles reasoning (absorbed from moai-foundation-philosopher), and Adaptive Thinking via the `ultrathink` keyword. Use for ideation, strategic analysis, architecture decisions, and deep reasoning on complex problems.
moai-harness-learner
Harness learning subsystem coordinator. Produces Tier 4 auto-update proposal payloads consumed by the orchestrator (which surfaces them via AskUserQuestion) and orchestrates Apply/Rollback flows. Triggers when harness learning proposals are pending or learning lifecycle management is needed.
moai-meta-harness
DEPRECATED — legacy 7-Phase meta-harness. Redirects to the v4 harness Builder (/moai:harness <natural-language request>) which replaces the static 7-Phase workflow with an orchestrator-direct 4-phase Builder (ANALYZE / PLAN / GENERATE / ACTIVATE) + a manifest-driven dynamic-workflow Runner. Retained as the redirect source for backward-compat invocation paths; the 7-Phase body below is preserved as historical reference, NOT for new harness creation.
moai-ref-api-patterns
REST/GraphQL API design patterns, error handling conventions, and input validation reference for backend development. Agent-extending skill that amplifies backend domain work (spawned via Agent(general-purpose) with backend instructions) with production-grade API patterns. Use when designing APIs, implementing endpoints, or reviewing backend code. NOT for: frontend development, DevOps, database schema design, security audits.
moai-ref-git-workflow
Git workflow patterns, branch strategies, conventional commits, and PR templates reference for git operations. Agent-extending skill that amplifies manager-git expertise with production-grade git workflow patterns. NOT for: code implementation, testing, architecture design, documentation content.
moai-ref-llm-security
AI/LLM defensive security reference: prompt-injection defense, OWASP LLM Top 10 defensive mapping, MCP and agentic tool-call hardening, training-data poisoning detection, model-output validation and guardrails, MITRE ATLAS defensive correlation, and NIST AI RMF governance. Agent-extending skill that amplifies backend, security, and AI-application engineering with production-grade defensive patterns for LLM-backed systems. NOT for: offensive techniques (jailbreak authoring, attack-payload crafting, red-team exploitation), model training or fine-tuning methodology, prompt optimization for capability, web-app OWASP Top 10 (see moai-ref-owasp-checklist), or general API design (see moai-ref-api-patterns).
moai-ref-owasp-checklist
OWASP Top 10 security checklist, authentication patterns, input validation, and HTTP security headers reference. Agent-extending skill that amplifies backend-implementation and security-audit workflows with production-grade security patterns. NOT for: frontend UI, DevOps deployment, performance optimization, testing strategy.
moai-ref-react-patterns
React/Next.js component design patterns, state management strategies, and project structure reference for frontend development. Agent-extending skill that amplifies frontend domain work (spawned via Agent(general-purpose) with frontend instructions) with production-grade React patterns. NOT for: backend API design, database modeling, DevOps, mobile apps.
moai-ref-secops
DevSecOps, container, and API operational defensive security reference: CI/CD pipeline hardening, secret scanning, IaC misconfiguration detection, SAST/DAST integration, container image scanning, Kubernetes RBAC hardening, container-escape defense, runtime threat detection, OWASP API Top 10 operational defense, WAF rule tuning, and GraphQL/REST depth and rate limiting. Agent-extending skill that amplifies backend, security, and platform-engineering work with production-grade defensive patterns for pipelines, containers, and running APIs. NOT for: offensive techniques (exploit execution, container-escape attack steps, privilege-escalation procedures, attack tooling), dev-time web-app OWASP Top 10 (see moai-ref-owasp-checklist), LLM/AI security (see moai-ref-llm-security), supply-chain provenance and signing (see moai-ref-supply-chain), or general API design (see moai-ref-api-patterns).
moai-ref-supply-chain
Software supply-chain defensive security reference: SBOM generation and verification (SPDX / CycloneDX), dependency-confusion defense, malicious-package triage playbook, SLSA provenance levels, Sigstore / cosign signing and verification, package-registry hardening, typosquatting defense, and transitive-dependency auditing. Agent-extending skill that amplifies backend, security, and release-engineering work with production-grade defensive patterns for the software supply chain. NOT for: offensive techniques (dependency-confusion attack execution, malicious package authoring, registry exploitation), LLM/AI-specific security (see moai-ref-llm-security), web-app OWASP Top 10 (see moai-ref-owasp-checklist), or general API design (see moai-ref-api-patterns).
moai-ref-testing-pyramid
Test pyramid strategy, coverage targets, test patterns, and quality metrics reference. Agent-extending skill that amplifies manager-develop test-creation and quality-validation work with production-grade testing patterns. NOT for: production code implementation, architecture design, DevOps, security audits.
moai-workflow-ci-loop
Unified CI watch + auto-fix loop skill. Polls gh pr checks after /moai sync PR creation, classifies required vs auxiliary failures, attempts safe automated patches (max 3 iterations), and escalates semantic failures to the user. Use for CI loop workflow — NOT for general loop iteration patterns (see moai-workflow-loop).
moai-workflow-ddd
Domain-Driven Development workflow specialist using ANALYZE-PRESERVE-IMPROVE cycle for behavior-preserving code transformation. Use when refactoring legacy code or reducing technical debt safely.
moai-workflow-loop
Ralph Engine - Automated feedback loop with LSP diagnostics and AST-grep integration for continuous code quality improvement. Use when implementing error-driven development, automated fixing, or continuous quality validation workflows.
moai-workflow-spec
SPEC workflow orchestration with EARS format requirements, acceptance criteria, and Plan-Run-Sync integration for MoAI-ADK development. Use when creating SPEC documents or defining acceptance criteria.
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