1.3천건 · 관련도순
Apply repeatable dialogue MA, Studio-based or automatic portrait reframing, overlay-safe rendering, and loudness/sync QA to an existing interview or talking-head rough cut. Use when the user asks to make speech easier to hear, perform MA, enlarge or rebalance a person in frame, crop or punch in an interview, automatically propose framing, or prepare a finishing preview before final approval.
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Finish an existing Japanese business talking-head, lecture, interview, or testimonial rough cut as a credibility-first vertical social video. Use when the user wants an SNS short that preserves the speaker's meaning while tightening repeated ideas, adding a first-second thesis, readable full captions, restrained punch-ins, speaker identification, a registered CTA card, dialogue-first audio, and retention QA.
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Manage Claude Code Viewer (CCV): preview in-progress CCV changes on ccv-preview, register HTML artifacts, download files, or schedule jobs via CCV local API. Use when user asks for CCV preview, ccv-preview, browser visual QA, UI/UX validation, CCV artifacts, CCV file download, or CCV scheduler jobs. Treat preview/プレビュー as the CCV app preview service first; use artifacts only when the user explicitly asks for artifact/dashboard/report HTML. Do NOT use for generic web app previews outside CCV, production deployment, Cloudflare config, or Anthropic remote triggers.
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23 engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw, and 6 more tools. Architecture, frontend, backend, QA, DevOps, security, AI/ML, data engineering, Playwright, Stripe, AWS, MS365. 30+ Python tools (stdlib-only).
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Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Promote QA → production by opening a PR from peterdrier/Humans:main to nobodies-collective/Humans:main with a properly-qualified commit summary. Use when the user says "PR to production", "promote to prod", "PR from origin to upstream", "prod PR", or any variation of pushing batched fork changes upstream. Always use this skill for the two-remote promotion flow even if the user doesn't say "prod" explicitly.
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Coordinate multiple agents for a complex multi-domain project using PM planning, parallel agent spawning, and QA review
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Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.
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Full QA review pipeline — security audit (OWASP Top 10), performance analysis, accessibility check (WCAG 2.1 AA), and code quality review
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Full pipeline: evaluation -> spec -> implementation -> review -> QA
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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.
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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus strategy.
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Orchestrate multiple Antigravity skills through guided workflows for SaaS MVP delivery, security audits, AI agent builds, and browser QA.
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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports
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Discover, build, verify, and optionally export (HTML, optional PDF) the evidence manifest of one SDD Composy task in evidence_required, routing it toward review_required. Use when acceptance evidence must be assembled or checked — 'juntar as evidências da task', 'gerar o relatório de evidências', 'verify the evidence manifest'. Do NOT use to run tests (sdd-qa), review code (sdd-review), or inspect the trace (sdd-trace).
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Run a bounded, provider-neutral SDD Composy correction loop on one approved task (EXECUTE, QA, EVIDENCE, REVIEW, VERIFY; at most 3 iterations) with explicit approval, safe-stop guards, and fresh verification. Use to continue an approved TASK autonomously — 'rodar o loop na TASK-003', 'continuar a task até passar', 'start a loop on task 4'. Do NOT use for the host's recurring loop runner, for fleets (sdd-fleet), or for a task without human approval.
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Adversarial verification of one SDD Composy task in verify_required: reproduce every claim with fresh commands, build the truth table, refute stale evidence, and produce the verdict that gates complete. Use when review is done and the task claims to be finished — 'verificar a TASK-003', 'a task está pronta mesmo?', 'is this task really complete'. Do NOT use for QA, code review, or a general 'verify my work' outside an SDD task.
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Design traceable Web and browser checks with explicit browser coverage, observed interactions, isolated sessions, and evidence limitations.
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Assess release readiness from applicable criteria, all current defects, risks, limitations, and recorded human decisions without approving release.
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Design isolated repeatable automation, distinguish interactive browser exploration, and handle flaky results through evidence and reproduction.
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Assess requirement clarity, testability, completeness, consistency, and traceability without inventing missing acceptance criteria.
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Design traceable API checks for contracts, authentication, authorization, errors, and idempotency with observable outcomes.
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