1.5万 件 ・ 関連度順
概要と使いどころ
Generate a complete subcontract from a firm's template, awarded bid, and project specs. Outputs a Word document. Triggers: 'subcontract', 'sub agreement', 'write contract', 'draft subcontract'. Requires awarded bid.
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Expert PE review of a submittal (shop drawings, product data, samples, equipment brochures) against specs, drawings, code questions and trade coordination. Produces findings with both sources, trade routing and a completeness gate. Triggers: 'review submittal', 'review shop drawings'.
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。
Split a bound project manual PDF into individual spec section PDFs and extract searchable text. Triggers: 'split specs', 'break up the project manual', 'separate spec sections', 'extract spec text'. Prerequisite for /submittal-log-generator.
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Phase 4 of 5 — implements tasks from TASKS-<N>-<slug>.md (with ARCH-<N>-<slug>.md as architecture-only context) using mode-appropriate verification (tdd, test-after, ui, checklist); pass 'auto' to run without stepping. Use only when the user asks to run Phase 4 or implement tasks from an ARCH doc — never trigger automatically from a coding request.
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Post-implementation QA planning — independent of /review (the developer chooses whether to run them sequentially or in parallel), only when the change has a running surface worth driving. Interviews the developer to turn the specs and the diff into an executable QA specification (specs/qa/QA-<N>-<slug>.md) that /execute-qa runs. The agent drives browser and shell; the human steps in only at named handoff points. Use only when the user asks to plan manual QA — never trigger automatically.
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Retire a closed issue's working artifacts out of specs/ into the GitHub wiki. Use only when the user asks to archive a finished issue, empty out specs/ for a done task, or move an issue's requirements/architecture/tasks/review/QA docs to the wiki.
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Polish cut gemstones to final finish using progressive abrasive sequences. Covers pre-polish sanding, polishing compounds, lap selection, and quality assessment for both cabochons and faceted stones. Use when a cabochon or faceted stone has completed cutting and is ready for polishing, selecting the correct compound and lap for a specific species, troubleshooting a polish that will not reach full lustre (orange peel, haze, scratches), or re-polishing a stone with surface wear.
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。
Assess and improve garden soil through testing, amendment, composting, and biodynamic preparations. Covers jar test, spade test, earthworm count, amendment by soil type (clay, sandy, depleted, compacted), composting methods (hot, cold, vermicomposting), no-till practices, cover cropping, and biodynamic preparations 500-508. Use when starting a new garden bed, when plants underperform despite adequate water and light, when transitioning to organic or biodynamic practice, when soil has become compacted or depleted, or when building a composting system.
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。
Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.
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Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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。
Plan garden activities using solar, lunar, and biodynamic calendars. Covers USDA hardiness zones, frost date calculation, equinox/solstice anchoring, synodic lunar cycle (waxing/waning), ascending/descending moon, Maria Thun biodynamic calendar (root/leaf/flower/fruit days), succession planting schedules, and seasonal task planning. Use when planning a new growing season and needing a planting schedule, integrating lunar or biodynamic timing into garden practice, calculating frost dates and planting windows for a specific zone, setting up succession planting for continuous harvest, or conducting end-of-season review.
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。
Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
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。
Plan a comprehensive spectroscopic analysis campaign by defining the analytical question, assessing sample characteristics, selecting appropriate techniques using a decision matrix, planning sample preparation for each technique, sequencing analyses from non-destructive to destructive, and defining success criteria with a cross-validation strategy.
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Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.
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Start and maintain a fire using friction, spark, and solar methods. Covers site selection, material grading (tinder/kindling/fuel), fire lay construction (teepee, log cabin, platform), ignition techniques (ferro rod, flint & steel, bow drill), flame nurturing, and Leave No Trace extinguishing. Use when needing warmth, light, or a signal in a wilderness setting, when boiling water for purification, when cooking foraged food, or in an emergency survival situation requiring heat or morale support.
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。
Create, track, switch, sync, and clean up Git branches. Covers naming conventions, safe branch switching with stash, upstream synchronization, and pruning merged branches. Use when starting work on a new feature or bug fix, switching between tasks on different branches, keeping a feature branch up to date with main, or cleaning up branches after merging pull requests.
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。
Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.
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。
Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.
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。
Implement GitOps continuous delivery using Argo CD or Flux with app-of-apps pattern, automated sync policies, drift detection, and multi-environment promotion. Manage Kubernetes deployments declaratively from Git with automated reconciliation. Use when implementing declarative infrastructure management, migrating from imperative kubectl commands to Git-driven deployments, setting up multi-environment promotion workflows, enforcing code review gates for production, or meeting audit and compliance requirements.
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Implement audit trail functionality for R projects in regulated environments. Covers logging, provenance tracking, electronic signatures, data integrity checks, and 21 CFR Part 11 compliance. Use when an R analysis requires electronic records compliance (21 CFR Part 11), when you need to track who did what and when in an analysis, when implementing data provenance tracking, or when creating tamper-evident analysis logs for regulatory submissions.
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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
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Extract data from web pages using the scrapling Python library — select the appropriate fetcher tier (HTTP, stealth Chromium, or full browser automation) based on target site defenses, configure headless browsing, and extract structured data with CSS selectors. Use when WebFetch is insufficient for JS-rendered pages, anti-bot-protected sites, or structured multi-element extraction requiring DOM traversal.
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。
Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
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Implement a JSON-RPC 2.0 A2A server with full task lifecycle management (submitted/working/completed/failed/canceled/input-required), SSE streaming, and push notifications. Use when implementing an agent that participates in multi-agent A2A workflows, building a backend for an Agent Card, adding A2A protocol support to an existing agent or service, or deploying an agent that must interoperate with other A2A-compliant agents.
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。