315 件 ・ 関連度順
概要と使いどころ
End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.
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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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Query aggregate Langfuse cost, volume, latency, and model telemetry efficiently with Metrics API v2. Use for dashboards, rollout checks, usage summaries, and cost or latency comparisons; use Observations API v2 instead when individual rows are actually required.
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Use after the backend follows the model — derive E2E flow scripts from Lean golden traces, replay them against the Lean-backed dev screens and the real REST backend with Playwright, and compare observations. Also use to add a flow when a new command or screen appeared.
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Debrief a finished Otameshi Tenshoku trial from the employer's side: set the observed facts against the completion criteria agreed before the start, keep expectations added afterwards, second-hand or off-the-job observations and the company's own late or missing support apart, settle the hours actually worked separately from any evaluation, and draft feedback to the candidate that states facts, support gaps and what happens next without promising a hire or continuation the company has not decided. Use when a hiring manager, recruiter or small-business owner wants to review a trial, decide what to tell the candidate, or work out the settlement after a shortfall; do not use to judge the candidate's aptitude or predict a hire, to cut pay or demand unpaid rework because of dissatisfaction, to compare candidates, or to send the feedback, pay, or update records on the user's behalf.
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。
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Plan explicitly authorized read-only production observations and trace evidence-backed causes into preventive regression coverage without external effects.
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Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.
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YC Office Hours — two modes. Startup mode: six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit. Builder mode: design thinking brainstorming for side projects, hackathons, learning, and open source. Saves a design doc. Use when asked to "brainstorm this", "I have an idea", "help me think through this", "office hours", or "is this worth building". Proactively invoke this skill (do NOT answer directly) when the user describes a new product idea, asks whether something is worth building, wants to think through design decisions for something that doesn't exist yet, or is exploring a concept before any code is written. Use before /plan-ceo-review or /plan-eng-review. (gstack)
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Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.
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Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.
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Select alternative execution paths when a plan step fails — branch on outcomes instead of blind retry. Load when a structured plan step fails, an unexpected observation arrives, or the user asks what to try next after an error. Also triggers on "try another approach", "route around this failure", "replan on failure", "if X fails try Y", or outcome-based branching during multi-step work. Pairs with structured-planning and debug-and-fix. Does not replace root-cause debugging — adds plan-level path selection.
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。
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
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Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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Human verification for AI agents. Submit claims, draft responses, or observation requests to human domain experts via the 2O API. Returns structured verdicts with confidence scores and evidence.
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MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor beh
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AEM Cloud Service expert skill for OSGi Event Admin handlers (non-resource events). Covers migration of javax.jcr.observation.EventListener (residual non-resource cases) and OSGi EventHandler with inline business logic to the lightweight EventHandler + JobConsumer split. Includes routing rules (resource events go to resource-change-listener, external notification goes to AEM Eventing), TopologyEventListener for leader-only execution, replication and workflow event patterns, service-user setup, review checklist, troubleshooting, and common pitfalls.
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AEM Cloud Service expert skill for Sling ResourceChangeListener. Covers the lightweight listener + JobConsumer pattern, migration from javax.jcr.observation.EventListener and resource-topic OSGi EventHandler, ResourceChangeListener vs ExternalResourceChangeListener decision, OSGi filter configuration, review checklist, troubleshooting (silent failures, hot-path blocking, missing service-user mapping), and common pitfalls.
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Client-facing red-team deliverable format — codifies the Subject / Observations / Description / Impact / Recommendation / PoC structure used for external red-team engagements (not bug-bounty platform reports). Different audience, different tone, different cadence. Built from an authorized engagement deliverable where 14 findings were packaged into a 52KB MD + 2.2MB DOCX with 16 embedded screenshots. Use when the engagement is 'external red team for an enterprise client' (not H1/Bugcrowd/Intigriti), when generating the final report, when the client has specified a custom report format, or when packaging findings into DOCX/PDF.
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Methodology for detecting client SOC patches, attacker activity, and security-state changes that occur DURING a red-team engagement — and converting those observations into deliverable findings. Built from authorized red-team work where the client patched a confirmed SQLi within 30 minutes of detection AND an external attacker locked multiple new accounts during a single test session. Use when (a) running ANY active engagement against a monitored target, (b) a previously-confirmed finding stops reproducing, (c) baseline timing shifts unexpectedly, or (d) you notice response patterns changing during testing.
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Structured interview and observation techniques for extracting tacit knowledge from domain experts
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