mirror of
https://github.com/wshobson/agents.git
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4d504ed8fa
Rewrites 14 commands across 11 plugins to remove all cross-plugin subagent_type references (e.g., "unit-testing::test-automator"), which break when plugins are installed standalone. Each command now uses only local bundled agents or general-purpose with role context in the prompt. All rewritten commands follow conductor-style patterns: - CRITICAL BEHAVIORAL RULES with strong directives - State files for session tracking and resume support - Phase checkpoints requiring explicit user approval - File-based context passing between steps Also fixes 4 plugin.json files missing version/license fields and adds plugin.json for dotnet-contribution. Closes #433
785 lines
26 KiB
Markdown
785 lines
26 KiB
Markdown
---
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description: "Build features guided by data insights, A/B testing, and continuous measurement"
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argument-hint: "<feature description> [--experiment-type ab|multivariate|bandit] [--confidence 0.90|0.95|0.99]"
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---
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# Data-Driven Feature Development Orchestrator
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## CRITICAL BEHAVIORAL RULES
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You MUST follow these rules exactly. Violating any of them is a failure.
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1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps.
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2. **Write output files.** Each step MUST produce its output file in `.data-driven-feature/` before the next step begins. Read from prior step files — do NOT rely on context window memory.
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3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options.
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4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue.
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5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies.
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6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
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## Pre-flight Checks
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Before starting, perform these checks:
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### 1. Check for existing session
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Check if `.data-driven-feature/state.json` exists:
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- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
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```
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Found an in-progress data-driven feature session:
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Feature: [name from state]
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Current step: [step from state]
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1. Resume from where we left off
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2. Start fresh (archives existing session)
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```
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- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
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### 2. Initialize state
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Create `.data-driven-feature/` directory and `state.json`:
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```json
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{
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"feature": "$ARGUMENTS",
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"status": "in_progress",
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"experiment_type": "ab",
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"confidence_level": 0.95,
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"current_step": 1,
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"current_phase": 1,
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"completed_steps": [],
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"files_created": [],
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"started_at": "ISO_TIMESTAMP",
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"last_updated": "ISO_TIMESTAMP"
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}
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```
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Parse `$ARGUMENTS` for `--experiment-type` and `--confidence` flags. Use defaults if not specified.
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### 3. Parse feature description
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Extract the feature description from `$ARGUMENTS` (everything before the flags). This is referenced as `$FEATURE` in prompts below.
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---
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## Phase 1: Data Analysis & Hypothesis (Steps 1–3) — Interactive
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### Step 1: Exploratory Data Analysis
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Use the Task tool:
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```
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Task:
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subagent_type: "general-purpose"
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description: "Perform exploratory data analysis for $FEATURE"
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prompt: |
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You are a data scientist specializing in product analytics. Perform exploratory data analysis for feature: $FEATURE.
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## Instructions
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1. Analyze existing user behavior data, identify patterns and opportunities
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2. Segment users by behavior and engagement patterns
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3. Calculate baseline metrics for key indicators
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4. Use modern analytics tools (Amplitude, Mixpanel, Segment) to understand current user journeys, conversion funnels, and engagement patterns
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5. Identify data quality issues or gaps that need addressing
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Provide an EDA report with user segments, behavioral patterns, and baseline metrics.
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```
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Save the agent's output to `.data-driven-feature/01-eda-report.md`.
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Update `state.json`: set `current_step` to 2, add `"01-eda-report.md"` to `files_created`, add step 1 to `completed_steps`.
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### Step 2: Business Hypothesis Development
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Read `.data-driven-feature/01-eda-report.md` to load EDA context.
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Use the Task tool:
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```
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Task:
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subagent_type: "general-purpose"
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description: "Formulate business hypotheses for $FEATURE"
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prompt: |
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You are a business analyst specializing in data-driven product development. Formulate business hypotheses for feature: $FEATURE based on the data analysis below.
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## EDA Findings
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[Insert full contents of .data-driven-feature/01-eda-report.md]
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## Instructions
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1. Define clear success metrics and expected impact on key business KPIs
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2. Identify target user segments and minimum detectable effects
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3. Create measurable hypotheses using ICE or RICE prioritization frameworks
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4. Calculate expected ROI and business value
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Provide a hypothesis document with success metrics definition and expected ROI calculations.
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```
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Save the agent's output to `.data-driven-feature/02-hypotheses.md`.
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Update `state.json`: set `current_step` to 3, add step 2 to `completed_steps`.
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### Step 3: Statistical Experiment Design
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Read `.data-driven-feature/02-hypotheses.md` to load hypothesis context.
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Use the Task tool:
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```
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Task:
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subagent_type: "general-purpose"
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description: "Design statistical experiment for $FEATURE"
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prompt: |
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You are a data scientist specializing in experimentation and statistical analysis. Design the statistical experiment for feature: $FEATURE.
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## Business Hypotheses
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[Insert full contents of .data-driven-feature/02-hypotheses.md]
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## Experiment Type: [from state.json]
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## Confidence Level: [from state.json]
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## Instructions
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1. Calculate required sample size for statistical power
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2. Define control and treatment groups with randomization strategy
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3. Plan for multiple testing corrections if needed
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4. Consider Bayesian A/B testing approaches for faster decision making
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5. Design for both primary and guardrail metrics
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6. Specify experiment runtime and stopping rules
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Provide an experiment design document with power analysis and statistical test plan.
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```
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Save the agent's output to `.data-driven-feature/03-experiment-design.md`.
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Update `state.json`: set `current_step` to "checkpoint-1", add step 3 to `completed_steps`.
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---
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## PHASE CHECKPOINT 1 — User Approval Required
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You MUST stop here and present the analysis and experiment design for review.
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Display a summary of the hypotheses from `.data-driven-feature/02-hypotheses.md` and experiment design from `.data-driven-feature/03-experiment-design.md` (key metrics, target segments, sample size, experiment type) and ask:
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```
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Data analysis and experiment design complete. Please review:
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- .data-driven-feature/01-eda-report.md
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- .data-driven-feature/02-hypotheses.md
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- .data-driven-feature/03-experiment-design.md
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1. Approve — proceed to architecture and implementation
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2. Request changes — tell me what to adjust
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3. Pause — save progress and stop here
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```
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Do NOT proceed to Phase 2 until the user selects option 1. If they select option 2, revise and re-checkpoint. If option 3, update `state.json` status and stop.
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---
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## Phase 2: Architecture & Instrumentation (Steps 4–6)
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### Step 4: Feature Architecture Planning
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Read `.data-driven-feature/02-hypotheses.md` and `.data-driven-feature/03-experiment-design.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "backend-architect"
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description: "Design feature architecture for $FEATURE with A/B testing capability"
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prompt: |
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Design the feature architecture for: $FEATURE with A/B testing capability.
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## Business Hypotheses
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[Insert contents of .data-driven-feature/02-hypotheses.md]
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## Experiment Design
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[Insert contents of .data-driven-feature/03-experiment-design.md]
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## Instructions
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1. Include feature flag integration (LaunchDarkly, Split.io, or Optimizely)
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2. Design gradual rollout strategy with circuit breakers for safety
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3. Ensure clean separation between control and treatment logic
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4. Support real-time configuration updates
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5. Design for proper data collection at each decision point
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Provide architecture diagrams, feature flag schema, and rollout strategy.
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```
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Save the agent's output to `.data-driven-feature/04-architecture.md`.
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Update `state.json`: set `current_step` to 5, add step 4 to `completed_steps`.
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### Step 5: Analytics Instrumentation Design
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Read `.data-driven-feature/04-architecture.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "data-engineer"
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description: "Design analytics instrumentation for $FEATURE"
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prompt: |
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Design comprehensive analytics instrumentation for: $FEATURE.
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Experiment Design
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[Insert contents of .data-driven-feature/03-experiment-design.md]
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## Instructions
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1. Define event schemas for user interactions with proper taxonomy
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2. Specify properties for segmentation and analysis
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3. Design funnel tracking and conversion events
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4. Plan cohort analysis capabilities
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5. Implement using modern SDKs (Segment, Amplitude, Mixpanel) with proper event taxonomy
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Provide an event tracking plan, analytics schema, and instrumentation guide.
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```
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Save the agent's output to `.data-driven-feature/05-analytics-design.md`.
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Update `state.json`: set `current_step` to 6, add step 5 to `completed_steps`.
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### Step 6: Data Pipeline Architecture
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Read `.data-driven-feature/05-analytics-design.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "data-engineer"
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description: "Design data pipelines for $FEATURE"
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prompt: |
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Design data pipelines for feature: $FEATURE.
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## Analytics Design
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[Insert contents of .data-driven-feature/05-analytics-design.md]
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Instructions
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1. Include real-time streaming for live metrics (Kafka, Kinesis)
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2. Design batch processing for detailed analysis
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3. Plan data warehouse integration (Snowflake, BigQuery)
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4. Include feature store for ML if applicable
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5. Ensure proper data governance and GDPR compliance
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6. Define data retention and archival policies
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Provide pipeline architecture, ETL/ELT specifications, and data flow diagrams.
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```
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Save the agent's output to `.data-driven-feature/06-data-pipelines.md`.
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Update `state.json`: set `current_step` to "checkpoint-2", add step 6 to `completed_steps`.
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---
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## PHASE CHECKPOINT 2 — User Approval Required
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Display a summary of the architecture, analytics design, and data pipelines and ask:
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```
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Architecture and instrumentation design complete. Please review:
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- .data-driven-feature/04-architecture.md
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- .data-driven-feature/05-analytics-design.md
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- .data-driven-feature/06-data-pipelines.md
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1. Approve — proceed to implementation
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2. Request changes — tell me what to adjust
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3. Pause — save progress and stop here
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```
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Do NOT proceed to Phase 3 until the user approves.
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---
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## Phase 3: Implementation (Steps 7–9)
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### Step 7: Backend Implementation
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Read `.data-driven-feature/04-architecture.md` and `.data-driven-feature/05-analytics-design.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "backend-architect"
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description: "Implement backend for $FEATURE with full instrumentation"
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prompt: |
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Implement the backend for feature: $FEATURE with full instrumentation.
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Analytics Design
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[Insert contents of .data-driven-feature/05-analytics-design.md]
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## Instructions
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1. Include feature flag checks at decision points
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2. Implement comprehensive event tracking for all user actions
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3. Add performance metrics collection
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4. Implement error tracking and monitoring
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5. Add proper logging for experiment analysis
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6. Follow the project's existing code patterns and conventions
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Write all code files. Report what files were created/modified.
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```
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Save a summary to `.data-driven-feature/07-backend.md`.
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Update `state.json`: set `current_step` to 8, add step 7 to `completed_steps`.
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### Step 8: Frontend Implementation
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Read `.data-driven-feature/04-architecture.md`, `.data-driven-feature/05-analytics-design.md`, and `.data-driven-feature/07-backend.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "general-purpose"
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description: "Implement frontend for $FEATURE with analytics tracking"
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prompt: |
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You are a frontend developer. Build the frontend for feature: $FEATURE with analytics tracking.
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Analytics Design
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[Insert contents of .data-driven-feature/05-analytics-design.md]
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## Backend Implementation
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[Insert contents of .data-driven-feature/07-backend.md]
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## Instructions
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1. Implement event tracking for all user interactions
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2. Build A/B test variants with proper variant assignment
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3. Add session recording integration if applicable
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4. Track performance metrics (Core Web Vitals)
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5. Add proper error boundaries
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6. Ensure consistent experience between control and treatment groups
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7. Follow the project's existing frontend patterns and conventions
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Write all code files. Report what files were created/modified.
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```
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Save a summary to `.data-driven-feature/08-frontend.md`.
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**Note:** If the feature has no frontend component (pure backend/API/pipeline), skip this step — write a brief note in `08-frontend.md` explaining why it was skipped, and continue.
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Update `state.json`: set `current_step` to 9, add step 8 to `completed_steps`.
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### Step 9: ML Model Integration (if applicable)
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Read `.data-driven-feature/04-architecture.md` and `.data-driven-feature/06-data-pipelines.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "general-purpose"
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description: "Integrate ML models for $FEATURE"
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prompt: |
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You are an ML engineer. Integrate ML models for feature: $FEATURE if needed.
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Data Pipelines
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[Insert contents of .data-driven-feature/06-data-pipelines.md]
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## Instructions
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1. Implement online inference with low latency
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2. Set up A/B testing between model versions
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3. Add model performance tracking and drift detection
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4. Implement automatic fallback mechanisms
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5. Set up model monitoring dashboards
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If no ML component is needed for this feature, explain why and skip.
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Write all code files. Report what files were created/modified.
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```
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Save a summary to `.data-driven-feature/09-ml-integration.md`.
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Update `state.json`: set `current_step` to "checkpoint-3", add step 9 to `completed_steps`.
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---
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## PHASE CHECKPOINT 3 — User Approval Required
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Display a summary of the implementation and ask:
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```
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Implementation complete. Please review:
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- .data-driven-feature/07-backend.md
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- .data-driven-feature/08-frontend.md
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- .data-driven-feature/09-ml-integration.md
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1. Approve — proceed to validation and launch
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2. Request changes — tell me what to fix
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3. Pause — save progress and stop here
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```
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Do NOT proceed to Phase 4 until the user approves.
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---
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## Phase 4: Validation & Launch (Steps 10–13)
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### Step 10: Analytics Validation
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Read `.data-driven-feature/05-analytics-design.md`, `.data-driven-feature/07-backend.md`, and `.data-driven-feature/08-frontend.md`.
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Use the Task tool:
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```
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Task:
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subagent_type: "data-engineer"
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description: "Validate analytics implementation for $FEATURE"
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prompt: |
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Validate the analytics implementation for: $FEATURE.
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## Analytics Design
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[Insert contents of .data-driven-feature/05-analytics-design.md]
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## Backend Implementation
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[Insert contents of .data-driven-feature/07-backend.md]
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## Frontend Implementation
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[Insert contents of .data-driven-feature/08-frontend.md]
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## Instructions
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1. Test all event tracking in staging environment
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2. Verify data quality and completeness
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3. Validate funnel definitions and conversion tracking
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4. Ensure proper user identification and session tracking
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5. Run end-to-end tests for data pipeline
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6. Check for tracking gaps or inconsistencies
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Provide a validation report with data quality metrics and tracking coverage analysis.
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```
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Save the agent's output to `.data-driven-feature/10-analytics-validation.md`.
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Update `state.json`: set `current_step` to 11, add step 10 to `completed_steps`.
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### Step 11: Experiment Setup & Deployment
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Read `.data-driven-feature/03-experiment-design.md` and `.data-driven-feature/04-architecture.md`.
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Launch two agents in parallel using multiple Task tool calls in a single response:
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**11a. Experiment Infrastructure:**
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```
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Task:
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subagent_type: "general-purpose"
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description: "Configure experiment infrastructure for $FEATURE"
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prompt: |
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You are a deployment engineer specializing in experimentation platforms. Configure experiment infrastructure for: $FEATURE.
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## Experiment Design
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[Insert contents of .data-driven-feature/03-experiment-design.md]
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Instructions
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1. Set up feature flags with proper targeting rules
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2. Configure traffic allocation (start with 5-10%)
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3. Implement kill switches for safety
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4. Set up monitoring alerts for key metrics
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5. Test randomization and assignment logic
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6. Create rollback procedures
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Provide experiment configuration, monitoring dashboards, and rollout plan.
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```
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**11b. Monitoring Setup:**
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```
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Task:
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subagent_type: "general-purpose"
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description: "Set up monitoring for $FEATURE experiment"
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prompt: |
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You are an observability engineer. Set up comprehensive monitoring for: $FEATURE.
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## Architecture
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[Insert contents of .data-driven-feature/04-architecture.md]
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## Experiment Design
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[Insert contents of .data-driven-feature/03-experiment-design.md]
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## Analytics Design
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[Insert contents of .data-driven-feature/05-analytics-design.md]
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## Instructions
|
||
1. Create real-time dashboards for experiment metrics
|
||
2. Configure alerts for statistical significance milestones
|
||
3. Monitor guardrail metrics for negative impacts
|
||
4. Track system performance and error rates
|
||
5. Define SLOs for the experiment period
|
||
6. Use tools like Datadog, New Relic, or custom dashboards
|
||
|
||
Provide monitoring dashboard configs, alert definitions, and SLO specifications.
|
||
```
|
||
|
||
After both complete, consolidate results into `.data-driven-feature/11-experiment-setup.md`:
|
||
|
||
```markdown
|
||
# Experiment Setup: $FEATURE
|
||
|
||
## Experiment Infrastructure
|
||
|
||
[Summary from 11a — feature flags, traffic allocation, rollback plan]
|
||
|
||
## Monitoring Configuration
|
||
|
||
[Summary from 11b — dashboards, alerts, SLOs]
|
||
```
|
||
|
||
Update `state.json`: set `current_step` to 12, add step 11 to `completed_steps`.
|
||
|
||
### Step 12: Gradual Rollout
|
||
|
||
Read `.data-driven-feature/11-experiment-setup.md`.
|
||
|
||
Use the Task tool:
|
||
|
||
```
|
||
Task:
|
||
subagent_type: "general-purpose"
|
||
description: "Create gradual rollout plan for $FEATURE"
|
||
prompt: |
|
||
You are a deployment engineer. Create a detailed gradual rollout plan for feature: $FEATURE.
|
||
|
||
## Experiment Setup
|
||
[Insert contents of .data-driven-feature/11-experiment-setup.md]
|
||
|
||
## Instructions
|
||
1. Define rollout stages: internal dogfooding → beta (1-5%) → gradual increase to target traffic
|
||
2. Specify health checks and go/no-go criteria for each stage
|
||
3. Define monitoring checkpoints and metrics thresholds
|
||
4. Create automated rollback triggers for anomalies
|
||
5. Document manual rollback procedures
|
||
|
||
Provide a stage-by-stage rollout plan with decision criteria.
|
||
```
|
||
|
||
Save the agent's output to `.data-driven-feature/12-rollout-plan.md`.
|
||
|
||
Update `state.json`: set `current_step` to 13, add step 12 to `completed_steps`.
|
||
|
||
### Step 13: Security Review
|
||
|
||
Read `.data-driven-feature/04-architecture.md`, `.data-driven-feature/07-backend.md`, and `.data-driven-feature/08-frontend.md`.
|
||
|
||
Use the Task tool:
|
||
|
||
```
|
||
Task:
|
||
subagent_type: "general-purpose"
|
||
description: "Security review of $FEATURE"
|
||
prompt: |
|
||
You are a security auditor. Perform a security review of this data-driven feature implementation.
|
||
|
||
## Architecture
|
||
[Insert contents of .data-driven-feature/04-architecture.md]
|
||
|
||
## Backend Implementation
|
||
[Insert contents of .data-driven-feature/07-backend.md]
|
||
|
||
## Frontend Implementation
|
||
[Insert contents of .data-driven-feature/08-frontend.md]
|
||
|
||
## Instructions
|
||
Review for: OWASP Top 10, data privacy and GDPR compliance, PII handling in analytics events,
|
||
authentication/authorization flaws, input validation gaps, experiment manipulation risks,
|
||
and any security anti-patterns.
|
||
|
||
Provide findings with severity, location, and specific fix recommendations.
|
||
```
|
||
|
||
Save the agent's output to `.data-driven-feature/13-security-review.md`.
|
||
|
||
If there are Critical or High severity findings, address them now before proceeding. Apply fixes and re-validate.
|
||
|
||
Update `state.json`: set `current_step` to "checkpoint-4", add step 13 to `completed_steps`.
|
||
|
||
---
|
||
|
||
## PHASE CHECKPOINT 4 — User Approval Required
|
||
|
||
Display a summary of validation and launch readiness and ask:
|
||
|
||
```
|
||
Validation and launch preparation complete. Please review:
|
||
- .data-driven-feature/10-analytics-validation.md
|
||
- .data-driven-feature/11-experiment-setup.md
|
||
- .data-driven-feature/12-rollout-plan.md
|
||
- .data-driven-feature/13-security-review.md
|
||
|
||
Security findings: [X critical, Y high, Z medium]
|
||
|
||
1. Approve — proceed to analysis planning
|
||
2. Request changes — tell me what to fix
|
||
3. Pause — save progress and stop here
|
||
```
|
||
|
||
Do NOT proceed to Phase 5 until the user approves.
|
||
|
||
---
|
||
|
||
## Phase 5: Analysis & Decision (Steps 14–16)
|
||
|
||
### Step 14: Statistical Analysis
|
||
|
||
Read `.data-driven-feature/03-experiment-design.md` and `.data-driven-feature/02-hypotheses.md`.
|
||
|
||
Use the Task tool:
|
||
|
||
```
|
||
Task:
|
||
subagent_type: "general-purpose"
|
||
description: "Create statistical analysis plan for $FEATURE experiment"
|
||
prompt: |
|
||
You are a data scientist specializing in experimentation. Create the statistical analysis plan for the A/B test results of: $FEATURE.
|
||
|
||
## Experiment Design
|
||
[Insert contents of .data-driven-feature/03-experiment-design.md]
|
||
|
||
## Hypotheses
|
||
[Insert contents of .data-driven-feature/02-hypotheses.md]
|
||
|
||
## Instructions
|
||
1. Define statistical significance calculations with confidence intervals
|
||
2. Plan segment-level effect analysis
|
||
3. Specify secondary metrics impact analysis
|
||
4. Use both frequentist and Bayesian approaches
|
||
5. Account for multiple testing corrections
|
||
6. Define stopping rules and decision criteria
|
||
|
||
Provide an analysis plan with templates for results reporting.
|
||
```
|
||
|
||
Save the agent's output to `.data-driven-feature/14-analysis-plan.md`.
|
||
|
||
Update `state.json`: set `current_step` to 15, add step 14 to `completed_steps`.
|
||
|
||
### Step 15: Business Impact Assessment Framework
|
||
|
||
Read `.data-driven-feature/02-hypotheses.md` and `.data-driven-feature/14-analysis-plan.md`.
|
||
|
||
Use the Task tool:
|
||
|
||
```
|
||
Task:
|
||
subagent_type: "general-purpose"
|
||
description: "Create business impact assessment framework for $FEATURE"
|
||
prompt: |
|
||
You are a business analyst. Create a business impact assessment framework for feature: $FEATURE.
|
||
|
||
## Hypotheses
|
||
[Insert contents of .data-driven-feature/02-hypotheses.md]
|
||
|
||
## Analysis Plan
|
||
[Insert contents of .data-driven-feature/14-analysis-plan.md]
|
||
|
||
## Instructions
|
||
1. Define actual vs expected ROI calculation methodology
|
||
2. Create a framework for analyzing impact on key business metrics
|
||
3. Plan cost-benefit analysis including operational overhead
|
||
4. Define criteria for full rollout, iteration, or rollback decisions
|
||
5. Create templates for stakeholder reporting
|
||
|
||
Provide a business impact framework and decision matrix.
|
||
```
|
||
|
||
Save the agent's output to `.data-driven-feature/15-impact-framework.md`.
|
||
|
||
Update `state.json`: set `current_step` to 16, add step 15 to `completed_steps`.
|
||
|
||
### Step 16: Optimization Roadmap
|
||
|
||
Read `.data-driven-feature/14-analysis-plan.md` and `.data-driven-feature/15-impact-framework.md`.
|
||
|
||
Use the Task tool:
|
||
|
||
```
|
||
Task:
|
||
subagent_type: "general-purpose"
|
||
description: "Create post-launch optimization roadmap for $FEATURE"
|
||
prompt: |
|
||
You are a data scientist specializing in product optimization. Create a post-launch optimization roadmap for: $FEATURE.
|
||
|
||
## Analysis Plan
|
||
[Insert contents of .data-driven-feature/14-analysis-plan.md]
|
||
|
||
## Impact Framework
|
||
[Insert contents of .data-driven-feature/15-impact-framework.md]
|
||
|
||
## Instructions
|
||
1. Define user behavior analysis methodology for treatment group
|
||
2. Plan friction point identification in user journeys
|
||
3. Suggest improvement hypotheses based on expected data patterns
|
||
4. Plan follow-up experiments and iteration cycles
|
||
5. Design cohort analysis for long-term impact assessment
|
||
6. Create a continuous learning feedback loop
|
||
|
||
Provide an optimization roadmap with follow-up experiment plans.
|
||
```
|
||
|
||
Save the agent's output to `.data-driven-feature/16-optimization-roadmap.md`.
|
||
|
||
Update `state.json`: set `current_step` to "complete", add step 16 to `completed_steps`.
|
||
|
||
---
|
||
|
||
## Completion
|
||
|
||
Update `state.json`:
|
||
|
||
- Set `status` to `"complete"`
|
||
- Set `last_updated` to current timestamp
|
||
|
||
Present the final summary:
|
||
|
||
```
|
||
Data-driven feature development complete: $FEATURE
|
||
|
||
## Files Created
|
||
[List all .data-driven-feature/ output files]
|
||
|
||
## Development Summary
|
||
- EDA Report: .data-driven-feature/01-eda-report.md
|
||
- Hypotheses: .data-driven-feature/02-hypotheses.md
|
||
- Experiment Design: .data-driven-feature/03-experiment-design.md
|
||
- Architecture: .data-driven-feature/04-architecture.md
|
||
- Analytics Design: .data-driven-feature/05-analytics-design.md
|
||
- Data Pipelines: .data-driven-feature/06-data-pipelines.md
|
||
- Backend: .data-driven-feature/07-backend.md
|
||
- Frontend: .data-driven-feature/08-frontend.md
|
||
- ML Integration: .data-driven-feature/09-ml-integration.md
|
||
- Analytics Validation: .data-driven-feature/10-analytics-validation.md
|
||
- Experiment Setup: .data-driven-feature/11-experiment-setup.md
|
||
- Rollout Plan: .data-driven-feature/12-rollout-plan.md
|
||
- Security Review: .data-driven-feature/13-security-review.md
|
||
- Analysis Plan: .data-driven-feature/14-analysis-plan.md
|
||
- Impact Framework: .data-driven-feature/15-impact-framework.md
|
||
- Optimization Roadmap: .data-driven-feature/16-optimization-roadmap.md
|
||
|
||
## Next Steps
|
||
1. Review all generated artifacts and documentation
|
||
2. Execute the rollout plan in .data-driven-feature/12-rollout-plan.md
|
||
3. Monitor using the dashboards from .data-driven-feature/11-experiment-setup.md
|
||
4. Run analysis after experiment completes using .data-driven-feature/14-analysis-plan.md
|
||
5. Make go/no-go decision using .data-driven-feature/15-impact-framework.md
|
||
```
|