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CLI Anything plan for producer automation. Step by step guide ad executive brief
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Leivur R. Djurhuus
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CLI_ANYTHING_IMPLEMENTATION_PLAN.md
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CLI_ANYTHING_IMPLEMENTATION_PLAN.md
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# CLI-Anything Integration: HP CG Production Tracker
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## Executive Summary
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CLI-Anything is an open-source framework (MIT licensed) that automatically transforms our web application into a command-line interface. This CLI layer enables AI agents to operate the tracker through structured commands, unlocking full automation of the production pipeline — from job intake to delivery.
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The goal: shift producers from **operators** (doing everything manually) to **supervisors** (approving, overriding, handling exceptions).
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---
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## Key Findings
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### What CLI-Anything Does
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- Auto-generates a production-ready CLI from an existing codebase
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- 7-phase pipeline: analyze source → design commands → implement → test → document → publish
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- Outputs a pip-installable package with JSON output mode for AI consumption
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- Includes auto-generated tests and documentation
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- Validated across 11 major applications (1,508 passing tests)
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- Repository: https://github.com/HKUDS/CLI-Anything
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### Why It Fits Our Project
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- Our tracker already has a clean service layer with 26 REST API endpoints
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- Zod validators translate directly to CLI command schemas
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- Dependency engine and business rules are already codified
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- Skills, capacity, and workload data already exists in the system
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- Ollama is already running locally for embeddings (pgvector + nomic-embed-text)
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### Cost
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- CLI-Anything: Free (MIT license)
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- Claude API (default): ~$0.01–0.05 per interaction, highly reliable tool calling
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- Local LLM (Ollama): Free, already in our Docker Compose stack — serves as offline fallback
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- Primary cost is implementation time
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---
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## What Can Be Automated
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| Producer Task | Automation Level | How |
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|---|---|---|
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| Job intake / project creation | Fully automatable | AI parses incoming requests (email, brief docs) and creates projects, deliverables, and pipeline stages |
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| Artist assignment | Fully automatable | AI matches skills, capacity, and department data to assign the best available artist |
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| Stage progression | Mostly automatable | Dependency engine auto-advances stages as prerequisites are approved; AI triggers downstream work |
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| Deadline monitoring & escalation | Fully automatable | Scheduled agent flags overdue items, nudges artists, escalates only true blockers to producers |
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| Status reporting | Fully automatable | Agent queries tracker and generates summaries on demand or on schedule |
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| Revision cycles | Partially automatable | Agent logs revisions and reassigns artists; creative review still requires human eyes |
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### Producer Role: Before vs After
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| Today | With Automation |
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|---|---|
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| Create projects manually | Review auto-created projects, approve |
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| Assign artists by memory | Review AI-suggested assignments, override if needed |
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| Monitor every stage daily | Get alerted only on exceptions and blockers |
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| Chase artists for updates | Agent handles nudges and follow-ups |
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| Compile status reports | Reports generated automatically |
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| Advance stages manually | Pipeline advances itself |
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---
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## Architecture Decision: Chat Interface
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### Default: Claude API + Tool Use
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- Claude interprets natural language and calls tools mapped to our services
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- Best-in-class accuracy for tool calling, multi-step operations, and ambiguous requests
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- ~$0.01–0.05 per interaction — negligible cost at 10-30 producers
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- Estimated monthly cost: ~$20–100 depending on usage volume
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### Fallback: Local LLM via Ollama
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- Activates automatically if the Claude API is unreachable (outage, network issues)
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- Llama 3 70B or Qwen 2.5 72B running locally via existing Docker Compose stack
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- Handles most straightforward operations reliably
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- Ensures producers are never blocked — the chat assistant stays online regardless
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### Why Claude as Default
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- Tool calling accuracy is significantly higher than local models — fewer misrouted commands, fewer confirmation retries
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- Handles complex multi-step requests out of the box ("create 20 deliverables and assign them based on availability")
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- The cost is trivial relative to the producer time saved
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- Ollama remains valuable as a zero-downtime safety net, not a cost-saving measure
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---
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## Implementation Steps
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### Phase 1: Generate the CLI
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1. Install CLI-Anything as a Claude Code plugin:
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```bash
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/plugin marketplace add HKUDS/CLI-Anything
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/plugin install cli-anything
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```
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2. Run CLI generation against the tracker codebase:
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```bash
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/cli-anything ~/Documents/VScode/hp_prod_tracker
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```
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3. Review generated commands — ensure they map correctly to existing services:
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- Project CRUD (create, list, update, archive)
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- Deliverable CRUD + bulk creation
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- Stage advancement + status updates
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- Artist assignment (with skill/capacity awareness)
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- Revision logging
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- Workload queries
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- Excel import/export
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4. Refine coverage for any missing operations:
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```bash
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/cli-anything:refine ~/Documents/VScode/hp_prod_tracker "pipeline dependencies and bulk operations"
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```
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5. Run the auto-generated tests and validate against the real database
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6. Install the CLI locally:
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```bash
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cd hp_prod_tracker/agent-harness && pip install -e .
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```
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### Phase 2: Build the Chat UI Component
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1. Create a slide-out chat panel using shadcn/ui (`Sheet` + `ScrollArea` + `Input`)
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2. Add a chat icon/button to the app sidebar or top bar
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3. Store chat history per user (new Prisma model or simple local state for V1)
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4. Pass current context (active project, deliverable) into the chat so producers don't have to specify everything
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### Phase 3: Wire the AI Backend (Claude API — Default)
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1. Install Anthropic SDK: `npm install @anthropic-ai/sdk`
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2. Create `/api/chat` route in the Next.js app
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3. Configure Claude API client with API key (stored in environment variables)
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4. Define tools from existing Zod validators and service functions:
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- `create_project`, `list_projects`, `update_project`
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- `create_deliverable`, `list_deliverables`
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- `assign_artist`, `remove_assignment`
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- `advance_stage`, `get_blocked_stages`
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- `get_workload`, `get_available_artists`
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- `create_revision`, `list_overdue`
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- `export_excel`, `import_excel`
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5. Implement tool execution handlers that call the existing service layer
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6. Add confirmation flow: for any mutation, show the user what will happen before executing
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7. After execution, invalidate relevant TanStack Query caches so the UI updates in real-time
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8. Test against common producer requests:
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- "Create a new project for Pavilion 16, high priority, Q3"
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- "Assign Maria to Model Prep on Spectre x360"
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- "What's overdue this week?"
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- "Mark all Catalog Images for HP-2026-Q2 as delivered"
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### Phase 4: Ollama Fallback Layer
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1. Add a chat-capable Ollama model to `docker-compose.yml` (e.g., `llama3:70b` or `qwen2.5:72b`)
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2. Create a provider abstraction in the chat API route:
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```
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try Claude API → on connection failure → fall back to Ollama
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```
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3. Map the same tool definitions to Ollama's function calling format
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4. Add a health check endpoint that monitors Claude API availability
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5. Log all fallback events so we can track how often Ollama is needed
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6. Ensure producers see a subtle indicator when running in fallback mode (e.g., "Running locally — some complex requests may need to be simplified")
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### Phase 5: Automation Agents (Scheduled)
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1. Create scheduled agent scripts (cron or Next.js API routes triggered by cron):
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- **Deadline monitor**: Runs daily, flags overdue stages, sends notifications
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- **Auto-assignment**: When a stage unblocks, suggests or auto-assigns based on skills + capacity
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- **Stage auto-advance**: When all prerequisites are approved, automatically transition downstream stages
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- **Status digest**: Weekly summary per project emailed or posted to Slack
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2. Each agent uses the CLI or service layer directly
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3. Add producer override controls — ability to pause/resume automation per project
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### Phase 6: Full Pipeline Automation
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1. **Intake automation**: Monitor email inbox or Workfront API for new requests → auto-create projects
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2. **Smart assignment**: Factor in historical performance, current workload trends, and skill match scores
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3. **Predictive alerts**: Flag projects likely to miss deadlines before they're actually late
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4. **Self-healing pipeline**: If an artist hasn't started an assigned stage in X days, auto-reassign
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---
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## Risk Considerations
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| Risk | Mitigation |
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| Claude API outage | Automatic fallback to local Ollama model; producers are never blocked |
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| Claude API costs spike unexpectedly | Monitor usage via Anthropic dashboard; set billing alerts; ~$20-100/mo expected for 10-30 users |
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| Ollama fallback misinterprets a command | Confirmation step before all mutations; undo capability; subtle UI indicator when in fallback mode |
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| Producers don't trust the AI | Start with read-only queries (status checks, reports), add mutations gradually |
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| Wrong artist assigned automatically | Always surface assignments as suggestions first; let producers approve for first 2-4 weeks |
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| Over-automation removes producer oversight | Keep producers in the loop via notifications; require approval for high-impact actions (project creation, bulk operations) |
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---
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## Success Metrics
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- Reduction in time producers spend on manual tracker operations (target: 70%+)
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- Accuracy of AI-driven assignments vs producer overrides (target: 85%+ acceptance rate)
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- Producer adoption of chat interface (target: daily use within 4 weeks)
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- Reduction in overdue stages (target: 30%+ improvement from proactive monitoring)
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---
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## References
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- CLI-Anything: https://github.com/HKUDS/CLI-Anything
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- Claude API Tool Use: https://docs.anthropic.com/en/docs/build-with-claude/tool-use
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- Project codebase: ~/Documents/VScode/hp_prod_tracker
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- Existing implementation plan: ~/Documents/VScode/hp_prod_tracker/IMPLEMENTATION_PLAN.md
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- Upgrade roadmap: ~/Documents/VScode/hp_prod_tracker/UPGRADE_PLAN.md
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