「fair」の検索結果

Compliant, retrieval-grounded research over investor-State dispute settlement (ISDS) awards and decisions. Use when the user asks about ICSID / investment-treaty arbitration cases, awards, or doctrines (fair and equitable treatment, expropriation, jurisdiction, costs, annulment, etc.) and wants answers grounded in the actual document text with pinpoint citations. Identifies the correct document on the case page, confirms it against the PDF's own first pages, retrieves primary documents on demand from ICSID, PCA, etc., never scrapes or hosts a corpus in violation of applicable terms, and cites only retrieved text.

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Practitioner skill for advising on EU Regulation 2023/2854 (Data Act). Covers Chapters II-VII (IoT data access, mandatory B2B sharing, unfair contract terms, public-sector exceptional need, cloud switching, third-country governmental access) and Chapter VIII (interoperability and smart contracts, gate-only). Use when the user asks about Data Act rights or obligations, drafts a Data Act notice or letter, reviews a data-sharing or cloud-switching contract under the Data Act, runs a Data Act gap analysis, or asks how the Data Act interacts with GDPR, the DMA, the Trade Secrets Directive, or sectoral law. Triggers include "Data Act", "Datengesetz", "Regulation (EU) 2023/2854", "Art. 4(1) request", "Art. 5(1) third-party request", "trade-secret handbrake", "cloud switching obligations", "Chapter VI", "Ch V exceptional need", and references to specific Data Act articles or recitals.

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Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation mapping.

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Méthodologie « verification-first » pour la Cour Pénale Spéciale de Centrafrique (cour hybride, siège à Bangui). Toute citation est vérifiée (JusticeInfo, FIDH, HRW, legal-tools.org ; les sites officiels renvoient souvent une erreur 403). Couvre l'affaire Paoua, le « notamment » de l'article 3, et la complémentarité avec la CPI. Aide à la recherche, pas un conseil juridique. Fait partie de la bibliothèque open source « Skills for International Justice » — méthodologie : github.com/jeannesulzer/international-criminal-tribunals-skills

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Use when auditing or recalibrating game balance: build a source-driven Monte Carlo balance lab (Rust + rayon) that extracts live game data, simulates human playstyles (AFK→pro), emits win-rate/economy verdicts with confidence intervals, and bruteforce-tunes parameters. Trigger on unfair levels, unreachable shops, farm exploits, interest-curve cliffs, post-content recalibration, or CI balance JSON diffs. Keywords: balance lab, Monte Carlo, win rate, difficulty curve, economy career, playstyle simulation, Resource extraction, GDScript parser, bruteforce tuning.

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SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

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Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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EU 사용자 대상 서비스용 Privacy Notice·Terms of Service·Consent Modal·Cookie Banner 자동 생성. GDPR (Regulation 2016/679) + ePrivacy Directive + Consumer Rights Directive 2011/83 + Digital Services Act + Digital Content Directive + Unfair Terms Directive 반영. 영문 인터뷰로 진행.

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Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.

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Trigger for: cost-reimbursement IGCE, CR cost estimate, CPFF, CPAF, CPIF, cost-plus estimate, BAA estimate, fixed-fee analysis, award-fee analysis, incentive-fee analysis, proposed CR rate validation, cost-pool buildup, share-ratio scenario, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable CR estimates using BLS OEWS wages, fringe/overhead/G&A/FCCM pools, contract-type-specific fee, GSA CALC+ positioning, and GSA Per Diem travel. Do NOT use for FFP, LH/T&M, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Trigger for: Labor-Hour IGCE, LH estimate, Time-and-Materials IGCE, T&M estimate, burdened hourly rate, burden multiplier, labor-category ceiling hours, materials estimate, proposed LH/T&M rate validation, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable LH/T&M estimates using BLS OEWS wages, explicit burden multipliers, GSA CALC+ positioning, GSA Per Diem travel, and separately priced T&M materials. Do NOT use for FFP, cost-reimbursement, grants, or cooperative agreements. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.

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Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.

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Review IEEE conference, journal, Transactions, Letters, or magazine manuscripts from a technical reviewer perspective using routed IEEE review gates. Use when evaluating scope fit, novelty, validity, data, clarity, compliance, advancement, engineering significance, method soundness, experiment sufficiency, baseline fairness, figure/table quality, formatting risks, rejection risk, major revision strategy, pre-submission readiness, or the quality and specificity of reviewer comments that may be generic, careless, contradictory, or possibly AI-generated.

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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.

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Respond to Google, Yelp, and industry reviews in the owner's voice -- gracious on the 5-stars, masterful on the 1-stars. Handles the angry customer, the unfair review, the fake review, and the one that mentions a legal or health issue. Every response written for the thousand future customers reading it.

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Use DIG (Dive into Graphs) to load graph-learning datasets, run graph generation, self-supervised learning, GNN explainability, 3D graph learning, GOOD OOD datasets, graph augmentation, fair graph learning, and large-scale graph workflows.

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Use the fair-esm repository skill for protein language-model embeddings, ESMFold structure prediction, ESM-IF1 inverse folding, and zero-shot variant-effect scoring.

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Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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This skill should be used when the user asks for a "sensitivity reader", "authenticity reader", "cultural review", "is this portrayal okay", "real people in my novel", "defamation", "can I use song lyrics", "epigraph permission", "permissions", "quote permission", "fair use", "AI disclosure", "do I need to disclose AI", "send to my editor", "editorial round", "Word file for my editor", "editor review copy", "co-author", "collaborate on a book", "shared world", "back up my book", "does this echo my source", "similarity check", "check overlap with my earlier books", or wants to run human editorial, ethics, permissions, or collaboration workflows around a story project. NOT for contracts or selling rights (use publishing), or reader feedback rounds and review copies for readers, including the GitHub review-copy setup (use feedback-triage).

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This skill should be used when the user asks about "mystery", "fair play", "clue", "red herring", "romance beats", "HEA", "thriller", "ticking clock", "horror", "dread", "MG", "YA", "middle grade", "young adult", "science fiction", "sci-fi", "fantasy", "quest", "chosen one", "historical fiction", "period voice", "anachronism", "comedy", "humour", "humor", "romcom", "farce", "satire", "running gag", "serial", "episodic", "web serial", "genre conventions", or wants genre-specific structural craft for a story project. NOT for a clue check as a revision pass (use revision-continuity).

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This skill should be used when the user asks to "validate", "reindex", "repair registries", "check links", "run the continuity, pacing, clue, voice, or name checks", "count words", "summarize a story project", "import an existing manuscript", "export a manuscript", "run a build", "generate a diagram", "record revision-pass status with `story passes`", "run the story CLI", or wants deterministic maintenance on a Story Skills markdown project. It runs the CLI and reads its output; NOT for judging a finding or revising the story to fix it (use revision-continuity for continuity errors and revision passes, plot-structure for pacing, voice-style for voices and prose, genre-craft for clues and fair play), or for sharing a review copy with readers (use feedback-triage).

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。