market-research-reports
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
session-management
Context preservation, tiered summarization, resumability
agentic-development
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
llm-patterns
AI-first application patterns, LLM testing, prompt management
pyats-topology
Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps. Use when mapping the network, building a diagram, discovering what is connected to what, or documenting device neighbors and links.
claudish-usage
CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
apple-on-device-ai
Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.
coreml
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
detect-metrics
Detect and list all metric functions in the yardstick package. Use when a user asks to find, list, or identify all metrics in the package.
table-filler
Fill `outline/tables_index.md` from `outline/table_schema.md` + evidence packs (NO PROSE in cells; citation-backed rows). **Trigger**: table filler, fill tables, evidence-first tables, index tables, 表格填充, 索引表. **Use when**: table schema exists and evidence packs are ready; you want a compact, citation-backed index table to support later writing and Appendix table curation. **Skip if**: `outline/tables_index.md` already exists and is refined (>=2 tables; citations in rows; no placeholders). **Network**: none. **Guardrail**: do not invent facts; every row must include citations; do not write paragraph cells.
extraction-form
Extract study data into a structured table (`papers/extraction_table.csv`) using the protocol’s extraction schema. **Trigger**: extraction form, extraction table, data extraction, 信息提取, 提取表. **Use when**: systematic review 在 screening 后进入 extraction（C3），需要把纳入论文按字段落到 CSV 以支持后续 synthesis。 **Skip if**: 还没有 `papers/screening_log.csv` 或 protocol 未锁定。 **Network**: none. **Guardrail**: 严格按 schema 填字段；不要在此阶段写 narrative synthesis（那是 `synthesis-writer`）。
schema-normalizer
Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins. **Trigger**: schema normalize, jsonl contract, interface drift, join drift, 字段不一致, schema 规范化. **Use when**: you have generated C2-C4 JSONL artifacts (outline/briefs/bindings/packs/anchors) and want deterministic, stable fields before self-loops/writing. **Skip if**: you are not using the survey pipelines, or the workspace already has a fresh PASS `output/SCHEMA_NORMALIZATION_REPORT.md` for the current artifacts. **Network**: none. **Guardrail**: NO PROSE; deterministic transforms only; do not invent evidence/claims; only fill missing ids/titles from `outline/outline.yml`.
idea-signal-mapper
Map paper notes + taxonomy into a signal table of tensions, missing pieces, and promising academic axes for brainstorm discussion. Writes `output/trace/IDEA_SIGNAL_TABLE.md`. **Trigger**: idea signal table, brainstorm signals, research tensions, signal map, 研究信号表, tension map. **Use when**: you have a core set, a taxonomy, and paper notes, and need a table-first bridge from literature evidence to possible directions. **Skip if**: `output/trace/IDEA_SIGNAL_TABLE.md` already exists and is refined. **Network**: none. **Guardrail**: no invented papers; every row must point to existing `paper_id`s.
major-task
Work heavyweight framework or library tasks with planning-first research, selective deep analysis, and rigorous handoff
