「pair」の検索結果

Complete MCP engineering work on the live path: build or repair servers and Apps, debug stdio or Streamable HTTP, migrate protocol/SDK/transport versions, reconcile source with package and activated runtime, inspect capability/effect authorization, and verify named-host claims without promoting weaker evidence. Use for real Model Context Protocol implementation, review, migration, runtime, or host-integration tasks. Do not use for ordinary non-MCP APIs, generic frontend work, or incidental product copy.

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Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks mention/citation rates over time, detects trends, and identifies opportunities. Uses Gemini API free tier (with grounding) as primary method, web search as fallback. Use when a user wants to: check if AI models mention their brand, track AI citation changes over time, measure AEO content effectiveness, monitor competitor AI visibility, or audit their brand's presence in AI-generated answers. Pairs with aeo-prompt-research-free (identifies prompts) and aeo-content-free (creates/refreshes content). This skill closes the loop by measuring results.

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Create or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).

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Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.

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。

Load when creating, auditing, refactoring, or repairing an Agent Skill for reuse, governance, and runtime efficiency.

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DRY (Don't Repeat Yourself) review rules for code reviews: find the same knowledge — a rule, a constant, a parser, a validation, a protocol detail — implemented in several places that must change together, and tell it apart from code that merely looks alike. Use when reviewing a diff, a pull request or recent changes for duplication, on "DRY review", "is this duplicated", "copy-paste check", or when a review checklist asks for DRY. Pair it with the solid skill for design and with owasp-asvs for security.

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SOLID design review rules (SRP, OCP, LSP, ISP, DIP) for code reviews: what counts as a real design finding, what is only taste, how to cite it and how severe it is. Use when reviewing a diff, a pull request or recent changes for design problems, when asked for a "SOLID review", "design review", "is this class doing too much", or when a review checklist asks for SOLID. Pair it with the dry skill for duplication and with owasp-asvs for security.

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Diagnose and repair a failed GEODE smoke workflow within the requested acceptance criteria. Preserve failed evidence, verify the shared root cause, and apply merge, rebuild, or live rerun stages only when authorized.

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Align, run, audit, and publish GEODE benchmark comparisons against Agent-World arXiv 2604.18292v1 across MCP-Mark, BFCL V4, and tau2-bench. Use for Agent-World or AgentWorld comparison requests, cross-suite agent benchmark design, Tau2 comparator/profile diagnosis, paired thin-runtime-versus-GEODE experiments, mean-accuracy@8 replication, or benchmark comparability and artifact reviews.

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Publish and verify a released GEODE version across GitHub Release and PyPI/uv. Use for uv/uvx distribution, stable promotions, release repair runs, and public installation-channel audits.

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Trigger when: (1) the user mentions "manim", "scene", "video", "EN/KO render", "GEODE_HERO_LANG", "1080p60", or (2) editing files under `scripts/visualizations/` ending in `.py`, or (3) the user asks to render, re-render, or extend any of the four validated scenes (`geode_hero.py`, `autoresearch_filewalk.py`, `autoresearch_compare.py`, `critical_floor.py`). GEODE Manim Scene authoring standard. Locks the patterns the four validated scenes already share — Anthropic-style palette, Helvetica Neue + Pretendard font pairing with weight=NORMAL, EN/KO multilingual lang via `GEODE_HERO_LANG` env, `_dashed_arrow_with_head` stage-coupled tinting, `verify_hero_layout.py` ratchet — so the next scene does not re-discover the kerning / padding / transition regressions catalogued in [[viz-frame-audit]].

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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Enforces Beads policy for a repository maintained initially by one owner, granting issue-management and commit authority while requiring explicit permission for Git pushes (other than to the user's own fork) and Dolt sync or push operations. Use when the user asks to enroll, repair, or operate a solo-maintainer Beads workspace, and when working with Beads in a repository that may carry a beads-solo enrollment, including when bd refuses a schema migration or mentions a remote.

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Establish concise, cross-agent project instructions from repository evidence. Use when initializing or repairing AGENTS.md, CLAUDE.md, related discovery links, or project-specific agent guidance.

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Design UI components/design systems, review visual design, check accessibility. Triggers: design system, color palette, font pairing, WCAG check. Data-driven: palettes, fonts, UX guidelines.

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Use when applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.

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Choosing and operating an HTTP API versioning strategy that doesn't break clients — Stripe's date-based pinned versions, the Deprecation/Sunset header pair (RFC 9745 + RFC 8594), URI vs header vs media-type approaches, and the version-transformer pattern. Grounded in Stripe's published architecture and IETF RFCs.

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Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).

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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

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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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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).

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Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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。