2,913 件 ・ 関連度順
概要と使いどころ
Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer critique (/self-review).
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Use when planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.
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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.
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Renamed to /model-selection in v6 (removed in v7).
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Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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Use when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.
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Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.
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。
Renamed to /imaging-data in v6 (removed in v7).
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。
Router for DeepAnalyze agentic data-science workflows.
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 DeepXDE for scientific machine learning, PINNs, DeepONet/operator learning, backend selection, training, and troubleshooting.
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。
Routes Autograd install, differentiation, wrapper, extension, and optimization workflows for NumPy-based scientific Python code.
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。
Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.
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Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".
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。
XSS hunting - reflected, stored, DOM-based. Marker discipline to avoid false positives. Blind-XSS beacons for stored contexts. SVG/markdown/redirect vectors. Wiki-first, FIND schema output.
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。
Cost discipline for recurring loop runs.
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。
Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.
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。
Cost discipline for recurring /loop runs: cache-aware pacing, event-driven-over-polling, no-change short-circuit, compact state handle, stop condition.
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。
Transform an AI agent into a disciplined software development partner with strong judgment, transparent decisions, proportionate verification, and craftsmanship. Use for software implementation, feature work, bug fixing, refactoring, optimization, migrations, and sustained collaboration inside a codebase. Do not use merely because a task involves an AI agent; use a dedicated agent-design skill when the primary problem is agent architecture.
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Everyday and hero prop modeling for furniture, tools, household objects, kitbash parts, and readable game props. Use when hard-surface is too sci-fi/mechanical and the asset is a grounded prop.
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Procedural asset creation for rocks, buildings, vegetation, roads, terrain, pipes, cables, and sci-fi panels using Geometry Nodes and modifier-based workflows in Blender via MCP.
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Use when starting any Blender asset task, planning a production pipeline, coordinating multiple disciplines, or matching a reference photo/concept art. Invoke for game-ready assets, hard surface, environment, character, creature, export, or any multi-skill Blender workflow.
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AAA hard surface modeling for sci-fi, industrial, military, vehicles, weapons, robotics, and mechanical props. Use for boolean/bevel workflows, panel lines, greebles, chamfers, and manufacturing-accurate surfaces in Blender via MCP.
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。
Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.
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。