「unit」の検索結果

Build daemon/background processes that start on boot, run continuously, and manage their own lifecycle. Covers macOS launchd (plist files, agents vs daemons), Linux systemd (unit files), Windows services, process supervision, logging, health checks, graceful shutdown, auto-restart, and AI-powered daemons that manage LLM API connections and rate limits. Activate on: "daemon", "background process", "launchd", "systemd", "service file", "plist", "launch agent", "launch daemon", "auto-start", "always running", "process supervisor", "pm2", "background service", "boot service", "AI daemon", "long-running process". NOT for: container orchestration (use devops-automator), cron jobs that run and exit (use task-scheduler), web server deployment (use backend-architect).

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Strategic analyst that maps competitive landscapes, identifies white space opportunities, and provides positioning recommendations. Use when users need competitive analysis, market positioning strategy, differentiation tactics, or "how do I stand out?" guidance across any domain (portfolios, products, services). NOT for market size estimation or financial forecasting.

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.

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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.

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Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.

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Recommends real-time CGM with confirmatory bedside POC-BG monitoring for insulin dosing adjustments rather than POC-BG testing alone in adults with insulin-treated diabetes at high risk of hypoglycemia. Consider this approach for patients aged ≥65 years, BMI ≤27 kg/m², total daily insulin dose ≥0.6 units/kg, CKD stage ≥3, or recent hypoglycemia.

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This skill suggests follow-up tests for specific comorbidities associated with Cushing's syndrome in patients with adrenal adenoma when CT density is less than 10 Hounsfield units. Use when managing a post-adrenalectomy Cushing's syndrome patient with confirmed adrenal adenoma pathology and low-density imaging (<10 HU).

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This skill recommends lifelong biochemical testing for recurrence in all patients with Cushing's syndrome after adrenalectomy, except those with resected adrenal adenoma showing CT density <10 Hounsfield units. Use when determining long‑term follow‑up strategy for a post‑adrenalectomy CS patient; triggers include “post‑adrenalectomy Cushing’s syndrome”, “CT density <10 HU”, and “adrenal adenoma resection”.

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This skill guides evaluation for malignancy using imaging in patients with a history of adrenal adenoma (post‑adrenalectomy Cushing’s syndrome) when the adenoma demonstrates a CT density of ≥10 Hounsfield units or when pathology is consistent with possible carcinoma. Trigger phrases include “CT density ≥10 HU”, “possible carcinoma on pathology”, and “post‑adrenalectomy CS patient with adrenal adenoma history”.

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Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.

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Vitest fast unit testing framework powered by Vite with Jest-compatible API. Use when writing tests, mocking, configuring coverage, or working with test filtering and fixtures.

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Design, implement, and evaluate risk-based software tests across unit, integration, contract, end-to-end, and regression layers. Use when adding tests, reproducing bugs, improving coverage, diagnosing flaky tests, or defining a test strategy; do not use to change production behavior unless the user also requests implementation.

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Generate the ≤10-minute Loom walkthrough script for a completed Weekly Community Signal report — a plain spoken briefing that dives straight into what the team needs to know, walking the report top to bottom (CopilotKit page first, then AG-UI in the same order). Straightforward and factual, NOT a radio show or a performance; no hype opener, no blame framing. Runs after every report (invoked by weekly-report) and on "loom script", "walkthrough script", "record the loom", "narrate the report".

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P0 classification rules for community reports. Six categories that auto-headline regardless of reporter count. Reference doc — invoked by weekly-report when classifying.

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Deep enrichment for community-sourced enterprise PROSPECTS (the 🎯 Prospective enterprise customers subsection). For each prospect, resolve their LinkedIn from their GitHub self-linked accounts FIRST (search only if none is self-linked), verify the employer matches, capture their full name, then list the company website + company size (ARR / latest funding round / employee count). Runs as a general-purpose subagent (many web searches) so the orchestrator's context stays small. Invoked by weekly-report (enterprise section) and the enterprise skill.

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Generic ad-hoc topic search across CopilotKit + AG-UI Discord/GitHub. "Find all reports about <topic> in the last <N> days." Returns a structured chronological list with reporter handles + 1-line summaries + optional enterprise enrichment. Triggers on "find all reports about", "search across both communities", "look for X in last N days".

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Builds the "Report Sources" child page for a finished Weekly Community Signal report — an evidence-backed defense of WHY every item landed in the column/section/rank it did. The agent argues each placement like a lawyer (claim → evidence → rule → rebuttal) but is under oath: it CANNOT invent, spin, or overstate — every claim ties to a verifiable fact (issue #, label, PR type, quoted comment, npm output, the front-door test). Not CYA — it's so Nathan can defend the report with facts and see the exact reasoning behind each call. Runs after the report (invoked by weekly-report, last steps) and on "report sources", "defend the report", "why is this here".

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Enterprise-specific view across CopilotKit + AG-UI. "Enterprise" here = CopilotKit's COMMERCIAL surfaces (Premium / CopilotKit Enterprise / Intelligence Platform / paid tiers), NOT CopilotKit running inside a big company. Tracks the commercial product surfaces (from product-surface-scan — Enterprise Intelligence, Cloud, self-host license, threads/persistence tier, Inspector, premium UI/Angular SDK, analytics, security bundle, Slack/Teams), applies the free-vs-paid classifier to decide if an issue belongs here, surfaces cross-page product contradictions, and tracks enterprise questions/complaints, community-sourced prospects (with a sales owner), and enterprise reporters with prior-week trend. Elevated to the top of the weekly report. Runs standalone ("who's at enterprise this week") or invoked by weekly-report. Triggers on "enterprise report", "enterprise signals", "enterprise status", "who at enterprise this week".

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Expert-level ecology covering population ecology, community ecology, ecosystem ecology, food webs, energy flow, nutrient cycling, biodiversity, and conservation biology.

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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

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Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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