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概要と使いどころ
Execute read-only SQL queries against multiple MySQL databases. Use when: (1) querying MySQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
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Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
日本語の概要は準備中です。原文の説明を表示しています。
Build a sized Instagram hashtag set for a post using the 2026 reality that 3 to 5 well-chosen tags beat 30 random ones. Sizes each tag as niche (under 50k posts), mid (50k to 500k), or broad (500k+), then assembles a 2-3 niche, 1-2 mid, 0-1 broad mix that a small or mid account can actually rank in, matched to the post content and rotated to avoid spam patterns. Use to choose hashtags for a caption, carousel, or Reel. Not for writing the caption itself (use ig-caption-writer).
日本語の概要は準備中です。原文の説明を表示しています。
Repurpose existing content into a native Instagram post. Take a LinkedIn, blog, YouTube script, or X tweet or thread and rebuild it for Instagram: a long piece becomes a carousel or a caption, re-hooked before the 125-char fold, off-platform artifacts stripped (X @-handles, link in bio), published via Publora on approval. Use to adapt content across platforms into Instagram. Not for writing from scratch (use ig-caption-writer or ig-carousel-planner), not for auditing a draft (ig-humanizer --mode audit).
日本語の概要は準備中です。原文の説明を表示しています。
Remove the AI tells readers react to in an Instagram caption or carousel slide: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, emoji storms; caps em dashes. Includes --mode audit (first-125 hook, length, hashtags, emoji, CTA, media) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use ig-caption-writer or ig-carousel-planner). Keywords: humanize, de-AI caption, audit before posting.
日本語の概要は準備中です。原文の説明を表示しています。
Draft an Instagram caption with the hook in the first 125 characters, a skimmable body, and one clear CTA, using a 2026 hook formula chosen by goal (saves, shares, comments, follows). Runs the humanizer pass and, on approval, publishes with the image or video you supply via the Publora media flow. Use to caption a single image or a Reel. Not for slide-by-slide carousels (use ig-carousel-planner) or auditing a draft (use ig-humanizer --mode audit).
日本語の概要は準備中です。原文の説明を表示しています。
Reverse-engineer the hook from a viral Instagram Reel or carousel, given a URL or a pasted caption. Identifies which of the 10 canonical 2026 formulas it uses (number-first, contrarian, relatable, confession, listicle, before/after, myth-buster, framework, pattern-interrupt Reel, how-I Reel), explains why it worked, names the primary goal, and returns a blank template mapped to your topic. Use to learn from a post you admire. Not for writing your own (use ig-caption-writer or ig-carousel-planner).
日本語の概要は準備中です。原文の説明を表示しています。
Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.
日本語の概要は準備中です。原文の説明を表示しています。
ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.
日本語の概要は準備中です。原文の説明を表示しています。
Project-kickoff rubric for the self-host vs managed-cloud decision — when is running your own inference engine / LLM platform worth the ops cost vs paying per-token for a managed API? Decide on two axes — VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). Use at kickoff when choosing where to run inference, or when cost / data-residency pressure forces a re-evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.
日本語の概要は準備中です。原文の説明を表示しています。
Adds and tunes a reranker stage for RAG using the retrieve-wide, rerank-narrow pattern. Use when relevant documents appear in the initial top-N but are buried by noise, top-1 precision or MRR is low despite adequate recall, or too many marginal chunks consume context. Covers cross-encoder, API, and local rerankers; N-to-k selection; and latency/cost tradeoffs. Do not use when retrieval recall itself is failing.
日本語の概要は準備中です。原文の説明を表示しています。
Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.
日本語の概要は準備中です。原文の説明を表示しています。
Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".
日本語の概要は準備中です。原文の説明を表示しています。
Selects the correct CLAUDE.md or LESSONS.md template via decision trees. Use when starting a project or unsure which fits.
日本語の概要は準備中です。原文の説明を表示しています。
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past sessions", "extract lessons from all sessions", "save learnings", "update CLAUDE.md with what we learned", "capture session insights", "remember this for next time", "extract lessons", "update skills from session", or "what did we learn".
日本語の概要は準備中です。原文の説明を表示しています。
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past sessions", "extract lessons from all sessions", "save learnings", "update AGENTS.md with what we learned", "capture session insights", "remember this for next time", "extract lessons", "update skills from session", or "what did we learn".
日本語の概要は準備中です。原文の説明を表示しています。
Audit the turbo skill lessons that /self-improve writes into this repo's .turbo/handoff/ from other projects, apply the ones the user approves, delete the consumed handoffs, commit and push, and update the installed Turbo skills. Use when the user asks to "read .turbo/handoff and let me know what you think", "read the handoff files and let me know what you think", "review the lesson handoffs", "apply the self-improve handoffs", or "process the turbo handoffs".
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when drafting GitHub issues for the Positron repository. It provides workflows for searching duplicates, selecting appropriate labels, gathering complete context through questioning, and writing terse, fluff-free issues that precisely describe what is needed or wrong. The skill prepares issues for manual submission by the user. Use this skill when the user asks to draft or prepare an issue for Positron.
日本語の概要は準備中です。原文の説明を表示しています。
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
日本語の概要は準備中です。原文の説明を表示しています。
Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
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Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHIF/Slim links. Use pydicom-medical-imaging for local DICOM reading; histolab for whole-slide pathology preprocessing.
日本語の概要は準備中です。原文の説明を表示しています。