「grant」の検索結果

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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Qualify the exact final diff for a LoopX-managed goal. Use when goal policy enables change_quality_qualification, before a non-trivial delivery or merge, and when producing or repairing an exact-scope quality receipt. The workflow is language-neutral, permits at most one policy-authorized safe-fix pass, and never grants merge or repository authority.

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Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

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Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

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。

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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。

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

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。

Sets up CloudWatch observability for the first time - Omni (CloudWatch Application Observability) and classic CloudWatch. Omni: creating a Space or Domain; access grants (who has access, at what level) and access profiles bounding async alerts, integrations, or agents; instrumenting an app or AI agent with the plain ADOT SDK so traces reach Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda), incl. no-image-rebuild and .NET CoreCLR vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS; connecting Slack to a Space; whether GitHub or a custom MCP tool server (HTTP/stdio; API key, bearer, OAuth2) can be connected. CloudWatch: onboarding a service to Application Signals - ADOT auto-instrumentation, the amazon-cloudwatch-observability add-on, monitored service, reporting telemetry, ServiceEvents, CI/CD git/deployment metadata, Terraform/manifest edits. For using what is set up - queries, dashboards, alarms, Omni alerts, X-Ray, synthetics, Dynamic Instrumentation - use aws-observability.

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Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.

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。

Format CVs for academic positions with publications, grants, and teaching

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Use BEFORE editing any file in `supabase/migrations/` or `supabase/schemas/`, OR when the user runs a `/database` subcommand (`compact local migration`, `rls scenarios`, `align`). Encodes the three contracts that protect the Grida database layer: applied migrations are immutable, RLS implementation mirrors tests (never the reverse), `schemas/*.sql` is the human-readable end-state. Companion to `supabase/AGENTS.md` (RLS, grants, security boundaries).

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Prepare journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Use when selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.

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。

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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。

Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the author's own voice. It never changes a number, result, or citation, and it is not for evading AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.

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Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on "write my limitations", "how should I address the limitation of", "reviewer said my sample is too small", or "help me word this limitation".

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Triage academic email and calendar (Gmail / Google Calendar via the session's MCP) into a prioritized digest plus a referee-obligations tracker — classifying referee requests, R&R and editor correspondence, co-author threads, seminar and conference invites, and grant/admin deadlines, and proposing a human-gated action for each (draft reply, calendar hold, scaffold a project, snooze). Use when user says "triage my inbox", "check my academic email", "what needs my attention this week", "go through my mail", or when run as a scheduled routine. NEVER auto-sends or auto-accepts anything.

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Build a credit history from zero in a new country — understand that credit doesn't transfer across borders, get the first products that report, avoid the newcomer traps, and reach a usable score in months not years. Use when someone says 'I have no credit history in [country]', 'build credit as a newcomer/immigrant', 'why was I rejected with a great score back home', or 'how do I get a credit card/loan as a new arrival'. Produces a credit-building plan, the starter products that report, a timeline, and the traps to avoid. Educational, not financial advice; routes to official credit sources.

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Compute what a degree will actually cost — sticker minus real aid, inflated per year, split into cash and loans, with the loan's decade-long monthly tail made visible before enrollment instead of after. Use when asked what will college really cost, compare these two offers' real prices, how much loan payment after graduation, or is this school affordable. Produces the all-in number from the script, the offer-letter decode (grants vs loans untangled), the monthly-tail reality check, and the two-school comparison.

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Build or tailor an academic CV for faculty, postdoc, research or fellowship applications: publications, grants, teaching, supervision and service in the order the field expects, with consistent citations. Use when asked to write my academic CV, update my CV for a lectureship, tenure-track or postdoc application, format my publications list, or adapt my academic CV for an industry research role. Produces the full academic CV, a field-appropriate section order, a clean publications list in one citation style, and a two-page summary version when asked.

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Offboard an AI agent the way you'd offboard an employee — inventory what it knew and touched, export then purge its memory, revoke every credential and access grant, and write the handover for its successor (human or agent). Use when decommissioning an agent or bot, switching agent vendors, ending an AI pilot, or when someone asks 'what did this thing have access to?'. Produces a severance checklist, an access-revocation table, a memory disposition record, and a successor handover.

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Use when handling research ethics, consent, community accountability, and data/transparency for an American Anthropologist (AA) manuscript — informed consent, anonymization, protection of vulnerable interlocutors, heritage and repatriation obligations, and what (and what NOT) to share. At AA this is an ethics-and-accountability skill grounded in the AAA ethics principles and an ethics of care. Plans the ethics and transparency; it does not grant waivers.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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This skill should be used when the user asks to "simplify", "clean up the diff", "run simplify", "simplify the changes", "review changed code for cleanup", explicitly invokes "/simplify", or asks to "commit without simplify", "skip simplify for this commit", or "commit this but skip simplify". Reviews changed code across reuse, simplification, efficiency, and altitude, or grants an explicit one-commit bypass. Not for correctness bugs.

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Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).

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。