'modes' 검색 결과

Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.

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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.

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Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

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Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.

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Write or revise MiniMax H3 prompts for text, keyframe, and full-reference modes, preserving mode-specific fields, labels, and timing.

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Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.

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Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo.

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AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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OpenAI Codex CLI wrapper — three modes. Code review: independent diff review via codex review with pass/fail gate. Challenge: adversarial mode that tries to break your code. Consult: ask codex anything with session continuity for follow-ups. The "200 IQ autistic developer" second opinion. Use when asked to "codex review", "codex challenge", "ask codex", "second opinion", or "consult codex". (gstack) Voice triggers (speech-to-text aliases): "code x", "code ex", "get another opinion".

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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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Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO review". (gstack) Voice triggers (speech-to-text aliases): "see-so", "see so", "security review", "security check", "vulnerability scan", "run security".

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Interactive developer experience plan review. Explores developer personas, benchmarks against competitors, designs magical moments, and traces friction points before scoring. Three modes: DX EXPANSION (competitive advantage), DX POLISH (bulletproof every touchpoint), DX TRIAGE (critical gaps only). Use when asked to "DX review", "developer experience audit", "devex review", or "API design review". Proactively suggest when the user has a plan for developer-facing products (APIs, CLIs, SDKs, libraries, platforms, docs). (gstack) Voice triggers (speech-to-text aliases): "dx review", "developer experience review", "devex review", "devex audit", "API design review", "onboarding review".

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CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Four modes: SCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick expansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Use when asked to "think bigger", "expand scope", "strategy review", "rethink this", or "is this ambitious enough". Proactively suggest when the user is questioning scope or ambition of a plan, or when the plan feels like it could be thinking bigger. (gstack)

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Static-to-EDS conversion that preserves the original design while making content authorable in Document Authoring. Two modes — page-level (overlay template with slot markers) and block-level (each section becomes an independent EDS block). Use when converting an AI-generated static HTML page (Stardust, Mobirise, Relume, Lovable, v0, Figma-derived hand-coded, etc.) into an Edge Delivery Services page. Triggers on "convert this page to EDS", "static-to-EDS overlay", "convert to EDS blocks", "next experimentation", "next run", "start run", or when a user provides a source URL and asks to make it editable in DA while keeping the original design intact. Do NOT use for canonical EDS block-rewrite migrations — that's the page-import skill.

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Route code cleanup, maintainability, React diagnostics, and over-engineering review to focused review modes. Use when auditing code quality or asking for a particular review lens.

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Route communication modes, learning workflows, retrospectives, output discipline, and perspective shifts to focused productivity skills.

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Quick-reference card for all ponytail modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /ponytail-help, "ponytail help", "what ponytail commands", "how do I use ponytail".

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Write the executable feature specification — the WHAT and WHY artifact that agents and reviewers treat as source of truth. Owns both /specify and /clarify modes. Load when the user asks to write a feature spec, write a specification, write an executable spec, define functional requirements, capture acceptance criteria as Given/When/Then, or when the spec-driven-development orchestrator routes here. Also triggers on "feature spec", "executable spec", "/specify", "/clarify", "write the spec for this feature", "specification for", "spec-driven", "machine-readable spec". Output: docs/specs/YYYY-MM-DD-<slug>-feature-spec.md. Hard gate: cannot Approve while [NEEDS CLARIFICATION] markers remain.

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I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.

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Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

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Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

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