「ida」の検索結果

Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED and marks a claim verified only when this run fetched the source and found the passage. Checks that a source says what the post says it says; it cannot prove the source is true or verify first-hand experience. Use when the user says "fact check", "verify statistics", "check sources", "factcheck". Report only; not for rewriting posts (blog-rewrite).

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Build a blog-level strategy for a niche or business: audience segments, 3-5 content pillars with hub-and-spoke clusters, competitive gaps, measurement plan, and a 90-day roadmap. Use when the user says "blog strategy", "content strategy", "blog positioning", "content pillars", "what should I blog about". Not for a dated publishing schedule (blog-calendar), SERP-validated cluster maps (blog-cluster), or a brief or outline for one post (blog-brief, blog-outline). Makes no promise of rankings or AI citations.

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Compare two Google Search Console page exports (current vs previous period) to flag pages whose clicks or impressions fell by a chosen threshold, with a triage hint (refresh, query shift, consolidate, prune). The 20% default is a screening heuristic, not proof of decay. Use for "/blog decay", "content decay", "QoQ decline", "refresh declining posts". Needs two GSC exports; not for diagnosing a single traffic drop live (blog-google) or rewriting posts (blog-rewrite).

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Build a dated monthly or quarterly editorial calendar: which posts to publish or review when, content mix, templates, distribution timing, and a material-change review queue. Use when the user says "editorial calendar", "content calendar", "publishing schedule", "what should I write this month". Not for positioning or pillars (blog-strategy), SERP-validated cluster maps (blog-cluster), or detecting declining pages from Search Console exports (blog-decay). Publication frequency is not a ranking signal.

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Create cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill, audit this skill, is this skill safe, vet a skill before installing, what does this skill access. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, spec validation, and security auditing of third-party skills before install.

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Verify a WordPress plugin's Abilities API registrations: enumerate abilities, check that callback behavior matches each annotation's claim (the adversarial readonly-but-writes detection), validate permissions and schemas, and validate audit documents produced by wp-abilities-audit.

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Use when reviewing WordPress plugins for GPL compliance, checking license headers or compatibility, evaluating upsell/freemium/trialware patterns, validating plugin naming or trademark rules, checking plugin slugs, running Plugin Check (PCP) to validate plugin changes, interpreting its findings, understanding why a plugin was rejected from WordPress.org, or answering questions about the 18 WordPress.org Plugin Directory guidelines.

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Validate at every layer data passes through to make bugs impossible

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When the user wants to optimize their App Store listing for seasonal events, holidays, or trending moments — including keyword opportunities, metadata updates, screenshot theming, and timing strategy. Use when the user mentions "seasonal", "holiday", "Christmas", "New Year", "Valentine's Day", "summer", "back to school", "seasonal keywords", "trending now", "limited time", or wants to capitalize on a calendar event. For general keyword research, see keyword-research. For full metadata rewrites, see metadata-optimization.

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Guide users through creating, updating, debugging, and validating Agent Skills for AI agents. Use when the user wants to create, write, author, design, troubleshoot, validate, or improve a Skill, or needs help with SKILL.md, frontmatter, or skill structure.

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Plan and collect OpenClaw Windows validation/proof: tests, rubber-duck review, UI evidence, MCP output, and gateway runtime proof.

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Use Crabbox from macOS, Linux, or native Windows controllers to run OpenClaw Windows node builds, tests, and targeted proof on remote native Windows or WSL2 hosts, including Azure or brokered AWS leases and static SSH hosts. Use when remote Windows validation is needed or the user asks for Crabbox validation. Always report the actual provider, lease id, run URL, command, and result.

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Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.

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Evaluates a software or startup idea against problem, market, competition, monetization, defensibility, and execution, and returns a verdict rather than encouragement. Use this when an idea needs pressure-testing before anyone builds, when deciding whether something is worth pursuing, when assessing competition or willingness to pay, or when a validated idea needs a first-customers and MVP plan. Default to scrutiny; the useful answer is usually the unwelcome one.

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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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Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Generates complete single-compound network-toxicology research designs from one exposure, one disease or toxic phenotype, and a validation direction. Use when a study centers on one compound–one disease link and needs target collection, overlap construction, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-check, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.

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Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as "help me write a paper on X", "design an MR study for Y", or "I want to test whether A causally affects B using GWAS". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.

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Designs internal, external, temporal, and functional validation strategies at the protocol stage for medical research studies.

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。

Designs studies for predicting treatment response or resistance in biomedical and clinical research. Always use this skill when the user needs a treatment-response or resistance prediction study blueprint rather than a prognostic biomarker protocol, diagnostic test design, causal treatment-effect estimation, or a completed manuscript. Focus on responder definition, treatment context, baseline comparability, feature integration strategy, model development logic, validation architecture, and interpretation boundaries. Do not invent response rates, cohort size, assay readiness, regimen uniformity, literature support, or validation access.

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Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.

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Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.

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Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.

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。