「fair」の検索結果

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

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Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.

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[omh] Company hiring or HR process to structure: turn hiring and people context into a fair, structured recruiting or people-operations brief. Use when the user says: recruiting plan, hiring scorecard, interview scorecard, candidate debrief, 채용 계획, 면접 평가표, 후보자 비교.

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This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

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Use when determining author order on research manuscripts, assigning CRediT contributor roles for transparency, documenting individual contributions to collaborative projects, or resolving authorship disputes in multi-institutional research. Generates fair and transparent auth...

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Use when evaluating Prem Watsa / Fairfax-style value investing: insurance float, contrarian allocation, special situations, and conservative downside-focused capital allocation.

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。

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

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。

Stress-test a decision or claim through a sharp, fair Q&A — the questions a good lawyer or skeptical friend would ask before you commit. Use when asked to cross-examine me, ask me hard questions about this, interrogate my plan, or make me defend this. Produces a sequenced line of probing questions (from clarifying to challenging to the killer question), space to answer, and a debrief on where your answers were strong, evasive, or exposed a gap — so weaknesses surface in private before they surface in public.

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Decode a brand deal or UGC contract before signing — usage rights, exclusivity windows, whitelisting, payment terms, and kill clauses ranked 🔴🟡🟢 by what they can cost a creator, plus the counter-ask email. Use when a creator says 'is this brand deal fair', 'what does perpetual usage mean', 'they sent me a contract', or 'should I sign this collab agreement'. Produces a clause-by-clause decode, a money-math check on the rate, and a ready-to-send negotiation email. Not legal advice.

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Write a fair, enforceable community moderation policy. Use when standing up or overhauling moderation for a forum, Discord, Slack, subreddit, or any user community. Produces a clear code of conduct with examples, a graduated enforcement ladder tied to specific triggers, an appeals process, moderator guidelines, and the handling for the severe cases (threats, doxxing, brigading) that need immediate action. Governs member conduct in a user community — distinct from [[community-management-playbook]], which manages a brand's own social-media channels (comments, DMs, tone, response templates).

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Plan a co-marketing partnership — two brands reaching each other's audiences for mutual gain. Use when asked to plan a partnership, joint campaign, co-branded content/webinar, integration launch, or partner outreach. Produces the partner fit rationale, a fair value exchange, the joint campaign plan, the partner pitch, and how success is split and measured.

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Organize care for an aging or ill family member across multiple helpers — the shared care map, a fair rotation with backup rules, the information binder, and family-meeting agendas that prevent the one-sibling-does-everything spiral. Use when asked help me coordinate care for my mom, my siblings and I need to split caregiving, organize care for a sick family member, or set up a care schedule. Produces the care map of needs and coverage, the rotation schedule with escalation rules, the binder outline, and the family meeting agenda.

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Organize the people and information involved in caring for someone — family, doctors, helpers — so care doesn't fall through the cracks or all land on one person. Use when asked help me coordinate care for, organize caregiving for my parent, set up a care system, or I'm drowning coordinating everyone. Produces a map of who does what (medical, daily, financial, emotional), a shared-information system so everyone's on the same page, a way to divide tasks fairly among family, a communication rhythm, and how to keep the key information (meds, contacts, wishes) in one accessible place — turning caregiving chaos into a coordinated effort.

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Build a realistic recovery plan for burnout — address the causes, not just the symptoms — with changes you can actually make at work and outside it. Use when asked to help with burnout, I'm burnt out, how to recover from burnout, or I'm exhausted and dread work. Produces a read on what's driving the burnout (load, control, reward, fairness, values, community), immediate relief steps, the boundary and workload changes that address the root, a recovery timeline with realistic expectations, and a flag that severe or persistent burnout/depression warrants a professional. Not medical advice.

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Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.

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Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

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Use when targeting Global Biogeochemical Cycles (GBC) or deciding whether a biogeochemical-cycling manuscript fits this venue. Encodes the journal's system-scale fluxes/budgets/feedbacks fit, the quantitative biogeochemistry evidence bar, AGU FAIR data-deposition expectations, house style, official-submission re-check, and desk-reject heuristics.

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Use when appraising the cumulative evidence and ensuring balance in an Academy of Management Annals (Annals) review — weighing conflicting findings by credibility, steelmanning rival schools, and handling the author's own work even-handedly. Audits evidence quality and fairness; it does not design the framework (amann-organizing-framework) or build exhibits (amann-tables-figures).

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Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).

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EU AI Act (Regulation EU 2024/1689) compliance specialist. Use for AI system risk classification, provider/deployer obligations, GPAI model compliance, conformity assessments, bias and fairness testing, and AI governance programs.

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Audit Terms of Service, user agreements, and privacy policies for consumer risks, producing a structured report that flags unfair clauses, data traps, and liability issues. Trigger when a user asks to review, audit, or analyze a ToS, privacy policy, or user agreement, or mentions specific concerns like auto-renewal or data authorization.

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>Ce skill génère des sujets d'entraînement pour les deux épreuves écrites de consultation du CRFPA : l'épreuve de droit des obligations (tronc commun) et l'épreuve de spécialité (civil, affaires, social, pénal, administratif, international et européen, fiscal). Chaque sujet est accompagné d'une grille de notation détaillée sur 20 points. Vous ciblez un thème précis, ou vous laissez le skill procéder à un tirage au sort couvrant l'ensemble du programme. Connexion aux MCP OpenLegi et GoodLegal requise : toutes les références d'articles et d'arrêts sont vérifiées sur les sources officielles, sans citation inventée

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Recherche, cartographie et analyse des thèses de doctorat françaises via theses.fr (API ABES) et TEL (HAL). Spécialisée droit avec filtrage par sous-disciplines juridiques. Ouverte sur d'autres domaines universitaires. Capacités : (1) recherche multi-critères — sujet, directeur, jury, établissement, école doctorale, période, statut soutenue/en préparation, discipline ; (2) cartographie académique — qui dirige sur quel sujet, qui siège dans quels jurys, réseaux de co-direction, écoles doctorales productives ; (3) analyses temporelles — évolution d'un sujet, détection de saturation, calendrier des soutenances à venir ; (4) filtrage sous-discipline juridique — droit privé, public, social, affaires, international, etc. Déclencher dès que l'utilisateur mentionne thèse, doctorat, directeur de thèse, jury de thèse, école doctorale, soutenance, doctorant, ou demande une analyse d'écosystème doctoral. Pour articles ou ouvrages de doctrine, utiliser plutôt recherche-doctrine.

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Post-hoc verification and trust audit of AI-generated factual and interpretive claims. Classifies claims by type and salience, routes them to domain-appropriate sources, scores trustworthiness on a tiered scale with an Interpolated verdict for plausible-but-unsupported detail, and assesses rhetorical fairness on interpretive claims. Designed for clinical-negligence and healthcare-law practice in England and Wales, with general applicability beyond.

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。