293건 · 관련도순
Import an FBX character into a CARLA source build as a pedestrian available through the Python API. Check its GEN3 skeleton, create assets and a walker blueprint, register it, and verify spawning, ground placement, and movement.
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Collect unresolved work that needs human judgment from configured pull requests, issues, feedback, errors, and delivery signals. Group related problems into a decision queue with source links, supporting evidence, and visible gaps in coverage.
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Collect product feedback, usage data, runtime errors, and issue reports from configured sources. Classify and group findings with supporting evidence, then apply separate project rules for implementing fixes, requesting clarification, or closing reports.
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Configure a software delivery workflow that turns selected feedback and maintenance signals into verified, reviewed changes. Define information sources, schedules, working directories, and separate permissions for actions such as implementation, merging, deployment, and notifications.
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An experimental workflow for delivering software changes through configured checks, pull requests, review updates, and merge verification. It follows project permissions and release conditions, and reports missing evidence or checks before treating delivery as complete.
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Review a configured queue of pull requests, checking code changes, linked issues, tests, and review discussions. Report actionable findings and supporting evidence, then assess approval or merge separately against the permissions and conditions configured for each action.
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Create, adapt, or repair a Tutti workspace app as a publishable package or a local debugging setup. It prepares manifests, launch scripts, storage settings, and validation, including fixes for projects that fail to load.
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Expert knowledge for Azure Health Data Services development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using FHIR/DICOM APIs, bulk export/import, de-identification, change feeds, or Synapse/Data Factory, and other Azure Health Data Services related development tasks. Not for Azure Health Bot (use azure-health-bot), Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Machine Learning (use azure-machine-learning).
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Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring IR/SSIS/SHIR, mapping data flows, SAP/SaaS connectors, CI/CD deployments, or secure VNets/Key Vault, and other Azure Data Factory related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics), Azure Databricks (use azure-databricks).
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Scheduled health sweep that closes the factory loop - reads CI failures, recent commits, dependency and security advisories, and live queue labels, then files issues and writes an immutable run record. Use for the nightly or weekly monitor routine.
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Turn an ambiguous factory issue into human-approved product, behavior, design, and implementation slices. Use interactively before implementation.
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Show the factory control room from live GitHub labels, open PRs, recent run records, gate health, and charter limits.
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Classify GitHub issues into the factory's live label queue and write an auditable snapshot. Use for backlog triage or a scheduled triage run.
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Inspect factory health, stale work, CI, security advisories, and recent run evidence without implementing fixes.
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Classify incoming issues into the live GitHub label queue, reproduce where cheap, and write an auditable QUEUE.md snapshot. Use when triaging a backlog, running the scheduled triage routine, or deciding what the factory should pick up next.
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Independently verify a factory pull request, including fail-closed gates, negative test proof, scope, and load-bearing review.
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Review factory evidence and propose tighter or looser constraints without editing the charter. Use for a periodic constraint review.
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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-compliant data handling. Use when: "test data," "fixtures," "factories," "seed data," "synthetic data," "test database," "data anonymization." Not for: migration/integrity testing of the DB itself — use database-testing; environment provisioning and database branching strategy — use test-environments. Related: test-environments, database-testing, api-testing, unit-testing.
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Suede Labs Android app factory: plan, build, test, and release a production-grade native Android app from a product idea through Google Play. Use for requests to create, ship, submit, monetize, or modernize an Android app. Covers Kotlin and Jetpack Compose architecture, API-level policy verification, accessibility, privacy and Data Safety, Play Billing, Play Integrity, account deletion, testing, performance, store assets, signing, staged rollout, and a release evidence gate. NOT FOR: iOS work (private Suede Labs companion, not in this pack: ios-app-factory), a review-only pass on an existing app (use suede-code-review), or live Play listing and keyword audits on a shipped app (use suede-aso).
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software-factory-mode-2026akiのブレーキをかなり外したモード
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Software Factory 2026秋の開発フローをこのsessionに適用する。 着手前に不明点をユーザーに質問、変更が一段落したらcodexでbug確認、draft PR作成、対話コンテキストexport、インラインレビューコメントの草稿、ユーザーの指示でsanity-review、ready for review後のsquash and mergeのcommit message草稿の提示、という進め方を定める。 開発の開始時にユーザーが手動で起動する。
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Orientação especializada para fine-tuning de LLMs com LLaMA-Factory - WebUI sem código, 100+ modelos, QLoRA 2/3/4/5/6/8-bit, suporte multimodal
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Classify GitHub issues into the factory's live label queue and write an auditable snapshot. Use for backlog triage or a scheduled triage run.
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