「ci」の検索結果

Schedule and manage social media posts across Instagram, X (Twitter), Bluesky, TikTok, Threads, LinkedIn, Facebook, Pinterest, and YouTube using the AdaptlyPost API. Use when the user wants to schedule social media posts, manage social media content, upload media for social posting, list connected social accounts, check post status, cross-post content to multiple platforms, or automate their social media workflow. AdaptlyPost is a SaaS tool — no self-hosting required.

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Use when starting work in a repository under repositories/ that may lack CI configuration. Detects missing CI workflows (GitHub Actions, GitLab CI, CircleCI) and alerts the user to add one. Skips repos marked as research-only.

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Systematic literature review methodology for social science research. Use when conducting systematic reviews, research synthesis, meta-analyses, scoping reviews, or comprehensive literature searches across economics, political science, sociology, education, public health, and development. Produces structured notebook cells with search strategy, evidence table, thematic synthesis, and gap analysis.

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Council — 5 independent AI perspectives + mutual critique + synthesis (adapted from karpathy/llm-council for Claude Code). For high-stakes decisions where the answer does NOT follow unambiguously from the data — go/no-go before real consequences, critiquing a methodology, choosing between competing approaches, pressure-testing a conclusion you're attached to, designing risk controls. NOT for log/data analysis (the council has no data access) and not for factual lookups. Triggers — "convene the council", "run it past the council", "go/no-go", "pressure-test this".

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Use this skill when authoring or debugging GitLab CI/CD pipelines (.gitlab-ci.yml) — e.g. "add a CI pipeline for this Drupal project", "run phpcs/phpstan/phpunit in GitLab CI", "deploy with drush from a pipeline", "why is my job not running", "cache composer dependencies", or anything involving stages, rules, artifacts, environments, or GitLab runners.

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Revisa una migración de base de datos antes de desplegarla para asegurar que sea segura — reversible, no destructiva y compatible hacia atrás — siguiendo las convenciones de base de datos del proyecto. Úsala siempre que crees, revises o apruebes una migración de esquema o de datos (p. ej. "revisa esta migración", "¿es seguro desplegar esta migración?").

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Researches state-specific charitable solicitation filing requirements keyed to gross revenue, producing a citation-backed state-by-state compliance matrix of audit thresholds, financial-statement tiers, filing fees, and due dates. Use when building charity compliance matrices, researching nonprofit filing thresholds, charitable solicitation registration renewals, comparing state audit requirements for 501(c)(3) organizations, or when the user mentions audit threshold, review threshold, compilation requirement, charity filing deadline, or multi-state nonprofit compliance.

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Prepares jurisdiction-specific charitable solicitation registration packages for U.S. nonprofits. Covers multi-state nexus analysis, exemption eligibility, URS filing strategy, financial disclosures, and professional fundraiser documentation. Use when a nonprofit plans fundraising campaigns, online donation drives, direct mail, telephone solicitation, or any cross-state charitable solicitation.

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Citation format and verification protocol for legal documents that draw on Bates-stamped source records. Defines the bracket citation format, multi-source citation rules, quotation handling, the verification pass that runs before output, and the [UNVERIFIED] flag protocol. Use this skill any time another skill produces a legal work product that cites Bates-stamped pages, including medical chronologies, deposition summaries, demand letters, case briefs, and discovery responses. Also use any time the user asks how to format a citation or verify that a claim is supported by the source.

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Decision-making patterns in incident command and emergency management leadership contexts

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Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Assists clinicians in determining whether to proceed with social transition for prepubertal youth experiencing gender dysphoria or gender incongruence. Triggers include prepubertal youth with GD/gender incongruence and discussions about initiating social transition.

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The skill suggests adding a calcimimetic for hypercalcemia of malignancy due to parathyroid carcinoma that persists after intravenous bisphosphonate or denosumab therapy. It is triggered when a clinician notes refractory HCM despite antiresorptive therapy and considers adding a calcimimetic.

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Recommends adding intravenous bisphosphonate or denosumab when hypercalcemia due to parathyroid carcinoma is not adequately controlled with a calcimimetic. Consider this adjunct when a clinician notes persistent HCM despite calcimimetic therapy and asks about next-step therapy.

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Calculates salmon calcitonin dose based on patient weight for rapid calcium lowering in hypercalcemia of malignancy. Triggered when a clinician considers rapid calcium lowering and asks about weight‑based calcitonin dosing.

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Decide when to order coronary artery calcium (CAC) scoring in a primary-prevention patient and interpret the result per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers the Class 1 indication (men ≥40 / women ≥45, borderline / intermediate PREVENT-ASCVD risk, uncertainty about LLT), CAC score interpretation (0, 1–99, 100–299, ≥300, ≥1000), how CAC modifies LDL and non-HDL goals, when to repeat CAC, and pitfalls (incidental CAC on non-cardiac CT, radiation, cost). Use when a clinician asks should I order a CAC score, CAC indications, what does CAC 150 mean, treatment goals with CAC score, repeat CAC frequency, CAC in a smoker, CAC vs CCTA, is CAC useful in intermediate risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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Investigate and fix failing CI. Takes a PR reference, one or more CI run/job URLs, or freeform text describing where the failures are, then enumerates failing workflows/jobs/steps, determines the root cause (often one shared cause behind several red checks), proposes a fix plan, applies code changes, commits, pushes, watches the new runs, and repeats until checks pass. Use when the user says "fix CI", "CI is red", "checks are failing", "the build is broken on my PR", "fix the failing jobs", or pastes a failing CI run URL. Do NOT use for authoring new workflows, or for read-only diagnosis with no fix intent — use gha-log-reader for that.

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Calcule la vélocité des sprints terminés (Σ points des tickets feuilles livrés) et recommande la capacité du prochain sprint. À lancer en fin de sprint. Thin wrapper bash sur sprint_velocity.sh, zéro LLM lourd. Usage: /velocity [<titre-sprint>]

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Quando o usuário quer aplicar princípios psicológicos, modelos mentais ou ciências comportamentais ao marketing. Também use quando o usuário mencionar 'psicologia', 'modelos mentais', 'viés cognitivo', 'persuasão', 'ciência comportamental', 'por que as pessoas compram', 'tomada de decisão' ou 'comportamento do consumidor'. Esta skill fornece 70+ modelos mentais organizados para aplicação em marketing.

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Agentes de voz representam a fronteira da interação com IA - humanos falando naturalmente com sistemas de IA. O desafio não é apenas reconhecimento e síntese de fala, mas alcançar fluxo de conversa natural com latência inferior a 800ms enquanto se lidam com interrupções, ruído de fundo e nuances emocionais. Esta habilidade cobre duas arquiteturas: fala-para-fala (OpenAI Realtime API, menor latência, mais natural) e pipeline (STT→LLM→TTS, mais controle, mais fácil de depurar). Insight-chave: latência é a restrição. Hu

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Ajuste fino com eficiência de parâmetros para LLMs usando LoRA, QLoRA e 25+ métodos. Use ao fazer fine-tuning de modelos grandes (7B-70B) com memória GPU limitada, quando precisa treinar <1% dos parâmetros com perda mínima de precisão, ou para serviços multi-adapter. Biblioteca oficial do HuggingFace integrada ao ecossistema transformers.

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。