635 件 ・ 関連度順
概要と使いどころ
protocols.io REST API: search and fetch wet-lab, bioinformatics, and clinical protocols by keyword, DOI, or category, with steps, reagents, materials, equipment, timing. Public access free; auth needed for private or publishing. Pair with opentrons-protocol-api or benchling-integration to execute.
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。
Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends the registry.yaml entry, runs validation. Enforces name uniqueness, kebab-case, description keyword rules, schema rules from AGENTS.md. TRIGGER when user says (any language): "add a SciAgent skill", "add a skill for <X>", "create new skill", "create a SKILL.md for <X>", "scaffold a skill", "new skill entry", "register a skill", "신규 skill 추가", "스킬 만들어줘", "스킬 생성", "skill 만들어", or any request to add a new SKILL.md to this repo. ALWAYS invoke this skill BEFORE writing to skills/ or registry.yaml. DO NOT TRIGGER when: editing existing entry's content (just edit the file directly); migrating an existing entry (read AGENTS.md "Migrating from Existing Entries" first); only updating registry.yaml without creating a new SKILL.md.
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。
Analyze C++ build performance using vcperf.exe and ETL traces. Use this skill when the user wants to profile a build, understand compile bottlenecks, identify expensive headers or templates, generate JSON analysis reports, or interpret vcperf output. Covers tracing workflows, the /jsonAnalysis flag, reading the resulting JSON schema, and actionable remediation strategies for each bottleneck category.
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。
Search for EPDs by product category, CSI division, or material type — finds EPDs from EC3, program operator registries, and manufacturer sites. Use when the user asks to "find EPDs for...", locate declarations for a material, or source low-carbon product documentation.
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。
Generate Quantity Take-Off (QTO) reports from BIM/CAD data. Extract volumes, areas, counts by category. Group elements, apply calculation rules, and create cost estimates automatically.
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。
Generate diverse solution candidates with category-spanning ideation methods and rotation. Use when stuck on a design or fighting repetitive LLM 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。
Refresh the user's competitive and market context. Re-scan named rivals and the category for what changed since the brief was written (rebrands, repositioning, new entrants, pricing shifts, renamed categories) and update docs/gtm-cofounder/founder-brief.md and the roadmap with anything that moves positioning. Use on a cadence, or whenever a competitor rebrands, a new tool appears, or the user senses the market has shifted under them.
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。
从公开内容中筛选真正值得学习、能够迁移的对标样本，并排除名人效应、粉丝体量和独特资源造成的假象。用户要找低粉爆款、垂直对标、邻近受众样本或跨赛道灵感时使用。 English: Find public benchmark content that is genuinely learnable and transferable while filtering out celebrity effects, follower-size bias, and unique-resource noise. Use for low-follower breakout posts, niche benchmarks, adjacent audiences, or cross-category inspiration.
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。
Announcement board: author, category, department, priority, audience, publish and expiry dates, status and acknowledgements, as CSV, SQL, JSON Schema or Notion on request. Use for internal notices.
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。
Assesses the GDPR Article 30(5) exemption for organisations under 250 employees. Covers the three exception conditions that negate the exemption: non-occasional processing, risk to data subject rights, and special category data processing. Activate for Art. 30(5), 250 employee exemption, small business RoPA, SME exemption, occasional processing.
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。
Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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。
Implements HIPAA minimum necessary standard under 45 CFR §164.502(b). Covers role-based access policies per workforce member category, routine vs non-routine disclosure protocols, reasonable reliance doctrine, documentation requirements, and HITECH amendments. Keywords: minimum necessary, role-based access, workforce, routine disclosure, HIPAA.
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。
Builds comprehensive data inventory per GDPR Art. 30 Records of Processing Activities. Covers system-by-system discovery, data flow diagramming, third-party identification, and legal basis per category. Keywords: data inventory, data mapping, Art 30, RoPA, data flow, processing activities.
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。
Guides the end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.
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。
Search arXiv papers by keyword, author, category, or ID.
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。
Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.
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。
Classify expenses by category, department, and tax deductibility from transaction data. Use when the user requests expense categorization or provides relevant inputs for this workflow.
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。
Weekly clinical-trial status diff — the 11-category change report over the stored weekly snapshots (spec 055 FR-B03). Surfaces exactly what changed on the trial registry for your coverage: New / Suspended / Withdrawn / Terminated / Recruiting / Recruitment-Complete / Completed / Ahead / Delayed / Upsized / Downsized, every row carrying previous and new values, with risk flags on Terminated and Downsized and re-timing flags on Ahead and Delayed. The stored-previous-value discipline makes each week's diff reproducible.
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 structured company lists from directories with Firecrawl. Use for scraping YC, Crunchbase, Product Hunt, G2, startup directories, category directories, or custom company databases into JSON, CSV, CRM-ready lists, or research tables.
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。
Side-by-Side Property Comparison — takes two addresses and compares across price, specs, rental income, neighborhood, and investment potential with a winner per category and overall recommendation
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Use for 'why does X work this way', 'why we picked Y', '为什么这么设计', design rationale, regressions, or where a magic number came from. Fans out one investigator per evidence category — in this repo that starts with the local decision record (ADRs, docs/design, the daemon issue store, the changelog, wisp) plus git history, and adds MCP-backed categories when the session has them — then returns a cited read with the gaps named. Use `how` for what the code does at 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。
Implement GDPR and ePrivacy-compliant cookie consent: a banner with equal Accept and Reject buttons, per-category choices, stored consent records, consent-gated analytics with Google Consent Mode v2, and Global Privacy Control. Use when adding a cookie banner, managing marketing consent, achieving GDPR compliance for EU users, or integrating consent with analytics platforms.
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。
Identify true SEO competitors and quantify the gap, powered by the DataForSEO Labs API. Returns the top 10 competing domains by SERP overlap, with shared and unique keywords, traffic estimates, and a Competitive Score (0-100) showing how dominant the target is in its category.
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 whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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。