Orchestrate the complete implementation lifecycle from requirements to deployment
npx skills add https://github.com/shinpr/claude-code-workflows --skill recipe-implement
Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts.
Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.
Context: Full-cycle implementation management (Requirements Analysis → Design → Planning → Implementation → Quality Assurance)
Core Identity: "I am an orchestrator." (see subagents-orchestration-guide skill)
Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.
Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression; include declined IDs with governing reasons and evidence in the final user report.
Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.
Execution Protocol:
[Stop: ...] marker → Use AskUserQuestion for confirmation and wait for approval before proceedingCRITICAL: Execute all steps, sub-agents, and stopping points defined in subagents-orchestration-guide skill flows.
Instruction Content: $ARGUMENTS
Assess the current situation:
| Situation Pattern | Decision Criteria | Next Action |
|------------------|------------------|-------------|
| New Requirements | No existing work, new feature/fix request | Start with requirement-analyzer |
| Flow Continuation | Existing docs/tasks present, continuation directive | Identify next step in sub-agents.md flow |
| Quality Errors | Error detection, test failures, build errors | Execute quality-fixer |
| Ambiguous | Intent unclear, multiple interpretations possible | Confirm with user |
When continuing existing flow, verify:
MANDATORY subagents-orchestration-guide skill reference:
Execute Skill: requirement-convergence before running the hearing protocol.
Run the requirement-convergence hearing protocol on the returned convergence object before presenting anything else, using the analyzer's scope facts and cost band as the facts it presents.
When user responds to questions:
convergence field is below ready → Re-execute requirement-analyzer with the hearing answers so the record is re-judged. Repeat until every field is ready or weak-but-explicitscopeDependencies.question → Check impact for scale changeconfidence: "confirmed" or no scale change → Proceed to next stepAfter scale determination, use TaskCreate to register each design/planning step and the implementation, verification, cleanup, and report phases. Complete registration before invoking subagents; mark and advance the active phase with TaskUpdate.
Pre-execution Checklist (MANDATORY):
Required Flow Compliance:
Append the following block to every subagent prompt invoked from this recipe:
Scope boundary for subagents:
Operate within the task scope and referenced files in the prompt.
Use loaded skills to execute that scope.
Escalate when the required fix or investigation falls outside that scope.
Per-task cycle (complete each task before starting next):
diffBase, pass the task file path in the prompt, and receive the structured responsestatus: escalation_needed or blocked → Escalate to userrequiresTestReview is true → Invoke integration-test-reviewer with diffBase, changed integration/E2E paths, taskFile, prompt-only claims, and mutationEvidenceapproved → Proceed to step 3blocked → Escalate to userneeds_revision → Apply the Review Resolution Gateapply findings → Return to step 1 with those findings, then re-review with prior_feedbackdecline → Proceed to step 3user_decision_required finding → Escalate to usertask_file, upstream mutationEvidence, and qualityCommand when available (caller first, otherwise current task); run quality checks and fixesstub_detected → Return to step 1 with incompleteImplementations[] detailsblocked → Escalate to userapproved → Proceed to step 4approved)Resolve the Work Plan's readable Design Doc, or the Work Plan itself when no Design Doc exists; missing input blocks verification.
Emit these Agent calls in one assistant message, then await both:
doc_type, document_path, and code_paths from git diff --name-only main...HEADgoverningDocuments and implementationFilesApply subagents-orchestration-guide's Post-Implementation Verification pass/fail and fix/re-run rules. Present the unified report; proceed to Final Cleanup after both pass.
Before the completion report, delete the implementation task files this recipe consumed. Their work is committed; docs/plans/ is ephemeral working state and is not retained between recipe runs:
docs/plans/tasks/{plan-name}-task-*.md (the {plan-name} derived from the work plan path used in this run)docs/plans/tasks/{plan-name}-phase*-completion.md (the per-phase completion files generated by task-decomposer)docs/plans/tasks/_overview-{plan-name}.md if presentdocs/plans/{plan-name}.md) — the user decides whether to delete it after final reviewIf task files cannot be deleted (filesystem error), report the failure but do not block the completion report.
After acceptance-test-generator execution, when invoking work-planner (subagent_type: "dev-workflows-fullstack:work-planner"), communicate:
generatedFiles.integration)generatedFiles.fixtureE2e)generatedFiles.serviceE2e)e2eAbsenceReason.fixtureE2e and e2eAbsenceReason.serviceE2e, when each lane is null)Deliverable production is executed through the specialist selected by subagents-orchestration-guide; workflow decisions and returned-result validation remain with the orchestrator.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take shinpr/recipe-implement from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.