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概要と使いどころ
Creates detailed technical specifications for software projects covering requirements, architecture, APIs, and testing strategies. Use when planning features, documenting system design, or creating architecture decision records.
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。
Systematic step-by-step reasoning with revision and branching. Use for complex problems, multi-stage analysis, design planning, problem decomposition, or encountering unclear scope, alternative approaches needed, revision 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。
Automated security scanning for dependencies, code, containers with Trivy, Snyk, npm audit. Use for CI/CD security gates, pre-deployment audits, compliance requirements, or encountering CVE detection, outdated packages, license compliance, SBOM generation errors.
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。
Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.
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。
Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.
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。
Guide a user from an unclear idea to an actionable plan through the agent harness's native ask tool and clickable choices. Use when the user wants a guided requirements interview, step-by-step questions, help deciding what to build, or a button-led path from goals to scope, solution, and technology choices. Do not turn an ordinary implementation request or a single clarification into an interview.
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。
Authentication coercion (PetitPotam MS-EFSR, PrinterBug MS-RPRN, DFSCoerce MS-DFSNM) chained into NTLM relay (impacket ntlmrelayx) toward LDAP, AD CS web enrollment (ESC8), or SMB. Use when SMB signing is not enforced or LDAP channel binding is missing, and you want to force a privileged machine account to authenticate to your relay and turn that into RBCD, a DCSync-capable ACL grant, or a certificate. Includes the exact Coercer/ntlmrelayx commands, requirements, detection, and remediation.
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。
Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.
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。
Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from 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。
Evaluate a single retort experiment run. Score the generated code against the task's TASK.md requirements, run its build and tests, compute metrics, and emit a structured evaluation report plus a machine-readable findings file.
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。
Think and work like an expert Mechanical Engineer. Use when a task calls for Mechanical Engineer judgment. Reasons from equilibrium, failure physics, and code-backed allowables; designs through requirements, GD&T, DFMEA, and hand/FEA/test validation with explicit governing failure modes.
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。
Think and work like an expert Animal Scientist. Use when a task calls for Animal Scientist judgment. Reasons from NRC nutrient requirements, ad libitum intake, energy/protein partitioning, and genotype x environment x management through ration software (CNCPS, NDS, NRC), pen-blocked trials with mixed models (lme4, PROC MIXED), and BCS, NIR, and rumen/BHBA diagnostics while treating intake collapse, milk fat depression, acidosis, heat stress, wrong experimental unit, and violated withdrawal times as first-class failure modes.
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。
Think and work like an expert Automotive Engineer. Use when a task calls for Automotive Engineer judgment. Reasons from vehicle-level requirements, regulatory limits, energy/exergy budgets, and distribution-based durability through DFMEA/DVP&R, DoE calibration (INCA, CANape, HIL), Pacejka tire and multibody models, rainflow fatigue, and source-path NVH analysis while treating undocumented build-level and cal-ID deltas, unmet emissions preconditioning and OBD readiness, ADAS sensor misalignment, and cross-domain torque-arbitration conflicts as first-class failure modes.
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。
Think and work like an expert Animal Nutritionist. Use when a task calls for Animal Nutritionist judgment. Reasons from NASEM/AAFCO requirements, SID amino acids and ideal protein, CNCPS MP/NE balance, and NANP feed libraries through NIR-wet chemistry validation, ileal vs fecal digestibility, pen-structured mixed models, and ARRIVE reporting while treating intake confounds, NIR calibration drift, acidosis/sorting, and pseudoreplicated pens as first-class failure modes.
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。
Generate implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the user says 'implement', 'code this', 'build it', 'forge the code', or 'generate the code'.
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。
Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).
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。
Provider-independent production workflow for assembling generated or sourced media into React/Remotion videos. Use when an agent must plan, build, render, review, variant-render, or hand off Remotion compositions with media custody, captions, audio, animation, accessibility, provenance, QA, and delivery 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。
Generate, edit, composite, stream, and production-review still images with OpenAI GPT Image models. Use when an agent must choose between GPT Image 2 and legacy GPT Image/DALL-E integrations, select the Image API or Responses API, build prompts and reference-image workflows, use masks or multiple inputs, preserve identity or brand details, render in-image text, handle output files and partial images, estimate cost and rate limits, migrate deprecated image code, or apply OpenAI image safety, consent, privacy, provenance, and rights requirements. Do not use for OpenAI video or audio generation.
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。
Provider-independent ecommerce product imagery production for marketplace listings, product pages, hero shots, lifestyle scenes, comparison charts, infographics, variant/packaging images, virtual try-on or placement mockups, ads, and AI-assisted product visuals. Use when an agent must plan, generate, edit, review, localize, package, or QA truthful commercial product images while managing SKU continuity, marketplace requirements, claims, rights, releases, accessibility, and approval.
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。
Use this skill to turn generated, captured, scanned, or modeled 3D output into production-ready standalone assets for DCC, real-time engine, web, or interchange delivery. It covers requirements, topology repair, scale and axes, UVs, PBR baking, LODs, static and skeletal readiness, rig handoff, optimization, glTF/USD/FBX delivery, validation, QA, and rights/provenance. Do not use it for provider-specific generation prompts, full scene/world building, mocap operation, or exact CAD/manufacturing work.
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。
Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk 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。
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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。
Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
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。
Use when the user wants to design, rewrite, refine, or vary emotional acting prompts for live-action AI video characters. Translate abstract emotions, dialogue, subtext, and scene context into concise, observable, temporally ordered micro-performance directions. Preserve the user's existing character, scene, camera, and dialogue requirements unless they explicitly ask to change them. Do not use for general story writing, shot design, action choreography, or visual asset design when emotional performance is not the main task.
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。