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docs+cli: deprecate prompt targets and remove generate_prompt_targets command (#944)
Prompt targets are no longer actively maintained. Mark them as deprecated in the docs and remove the `planoai generate_prompt_targets` CLI command that existed only to scaffold them. Docs - Add `.. deprecated::` banner to the Prompt Target concept page and to the function-calling guide / quickstart sections that walk users through configuring prompt targets. - Relabel the Prompt Target card on the overview page as deprecated. - Drop the Prompt Targets bullet from the README's Getting Started list. CLI - Remove the `generate_prompt_targets` Click command, its registration, and the `Utilities` rich-click command group. - Delete `cli/planoai/targets.py` (the command's only consumer). - Drop the `planoai prompt_targets` section from the CLI reference page. Skills - Delete the `cli-generate` rule, drop it from `plano-cli-operations` (description, when-to-use, rules list, execution checklist), and update the skills README. Hand-edit AGENTS.md to remove section 6.2 and renumber 6.3/6.4 so the commit stays scoped (regenerating pulled in unrelated drift between rules/ and AGENTS.md). The runtime gateway, schema, and existing demo configs still accept `prompt_targets` blocks; this is deprecation, not removal of behavior.
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101
skills/AGENTS.md
101
skills/AGENTS.md
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@ -31,9 +31,8 @@
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- [5.3 Use `planoai trace` to Inspect Routing Decisions](#use-planoai-trace-to-inspect-routing-decisions)
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- [Section 6: CLI Operations](#section-6)
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- [6.1 Follow the `planoai up` Validation Workflow Before Debugging Runtime Issues](#follow-the-planoai-up-validation-workflow-before-debugging-runtime-issues)
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- [6.2 Generate Prompt Targets from Python Functions with `planoai generate_prompt_targets`](#generate-prompt-targets-from-python-functions-with-planoai-generateprompttargets)
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- [6.3 Use `planoai cli_agent` to Connect Claude Code Through Plano](#use-planoai-cliagent-to-connect-claude-code-through-plano)
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- [6.4 Use `planoai init` Templates to Bootstrap New Projects Correctly](#use-planoai-init-templates-to-bootstrap-new-projects-correctly)
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- [6.2 Use `planoai cli_agent` to Connect Claude Code Through Plano](#use-planoai-cliagent-to-connect-claude-code-through-plano)
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- [6.3 Use `planoai init` Templates to Bootstrap New Projects Correctly](#use-planoai-init-templates-to-bootstrap-new-projects-correctly)
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- [Section 7: Deployment & Security](#section-7)
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- [7.1 Understand Plano's Docker Network Topology for Agent URL Configuration](#understand-planos-docker-network-topology-for-agent-url-configuration)
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- [7.2 Use PostgreSQL State Storage for Multi-Turn Conversations in Production](#use-postgresql-state-storage-for-multi-turn-conversations-in-production)
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@ -1377,99 +1376,7 @@ Reference: https://github.com/katanemo/archgw
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---
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### 6.2 Generate Prompt Targets from Python Functions with `planoai generate_prompt_targets`
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**Impact:** `MEDIUM` — Manually writing prompt_targets YAML for existing Python APIs is error-prone — the generator introspects function signatures and produces correct YAML automatically
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**Tags:** `cli`, `generate`, `prompt-targets`, `python`, `code-generation`
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## Generate Prompt Targets from Python Functions with `planoai generate_prompt_targets`
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`planoai generate_prompt_targets` introspects Python function signatures and docstrings to generate `prompt_targets` YAML for your Plano config. This is the fastest way to expose existing Python APIs as LLM-callable functions without manually writing the YAML schema.
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**Python function requirements for generation:**
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- Use simple type annotations: `int`, `float`, `bool`, `str`, `list`, `tuple`, `set`, `dict`
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- Include a docstring describing what the function does (becomes the `description`)
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- Complex Pydantic models must be flattened into primitive typed parameters first
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**Example Python file:**
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```python
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# api.py
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def get_stock_quote(symbol: str, exchange: str = "NYSE") -> dict:
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"""Get the current stock price and trading data for a given stock symbol.
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Returns price, volume, market cap, and 24h change percentage.
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"""
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# Implementation calls stock API
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pass
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def get_weather_forecast(city: str, days: int = 3, units: str = "celsius") -> dict:
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"""Get the weather forecast for a city.
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Returns temperature, precipitation, and conditions for the specified number of days.
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"""
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pass
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def search_flights(origin: str, destination: str, date: str, passengers: int = 1) -> list:
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"""Search for available flights between two airports on a given date.
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Date format: YYYY-MM-DD. Returns list of flight options with prices.
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"""
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pass
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```
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**Running the generator:**
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```bash
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planoai generate_prompt_targets --file api.py
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```
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**Generated output (add to your config.yaml):**
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```yaml
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prompt_targets:
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- name: get_stock_quote
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description: Get the current stock price and trading data for a given stock symbol.
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parameters:
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- name: symbol
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type: str
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required: true
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- name: exchange
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type: str
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required: false
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default: NYSE
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# Add endpoint manually:
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endpoint:
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name: stock_api
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path: /quote?symbol={symbol}&exchange={exchange}
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- name: get_weather_forecast
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description: Get the weather forecast for a city.
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parameters:
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- name: city
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type: str
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required: true
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- name: days
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type: int
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required: false
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default: 3
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- name: units
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type: str
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required: false
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default: celsius
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endpoint:
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name: weather_api
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path: /forecast?city={city}&days={days}&units={units}
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```
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After generation, manually add the `endpoint` blocks pointing to your actual API. The generator produces the schema; you wire in the connectivity.
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Reference: https://github.com/katanemo/archgw
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---
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### 6.3 Use `planoai cli_agent` to Connect Claude Code Through Plano
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### 6.2 Use `planoai cli_agent` to Connect Claude Code Through Plano
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**Impact:** `MEDIUM-HIGH` — Running Claude Code directly against provider APIs bypasses Plano's routing, observability, and guardrails — cli_agent routes all Claude Code traffic through your configured Plano instance
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**Tags:** `cli`, `cli-agent`, `claude`, `coding-agent`, `integration`
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@ -1562,7 +1469,7 @@ Reference: [https://github.com/katanemo/archgw](https://github.com/katanemo/arch
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---
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### 6.4 Use `planoai init` Templates to Bootstrap New Projects Correctly
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### 6.3 Use `planoai init` Templates to Bootstrap New Projects Correctly
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**Impact:** `MEDIUM` — Starting from a blank config.yaml leads to missing required fields and common structural mistakes — templates provide validated, idiomatic starting points
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**Tags:** `cli`, `init`, `templates`, `getting-started`, `project-setup`
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@ -63,7 +63,7 @@ After installation, these skills are available to your coding agent and can be i
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- `plano-agent-orchestration` - Agent registration and routing descriptions
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- `plano-filter-guardrails` - MCP filters, guardrail messaging, filter ordering
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- `plano-observability-debugging` - Tracing setup, span attributes, trace analysis
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- `plano-cli-operations` - `planoai up`, `cli_agent`, init, prompt target generation
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- `plano-cli-operations` - `planoai up`, `cli_agent`, init
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- `plano-deployment-security` - Docker networking, health checks, state storage
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- `plano-advanced-patterns` - Multi-listener architecture and prompt target schema design
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@ -110,7 +110,7 @@ skills/
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| 3 | `agent-` | Agent Orchestration | Descriptions, agent registration |
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| 4 | `filter-` | Filter Chains & Guardrails | Ordering, MCP integration, guardrails |
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| 5 | `observe-` | Observability & Debugging | Tracing, trace inspection, span attributes |
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| 6 | `cli-` | CLI Operations | Startup, CLI agent, init, code generation |
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| 6 | `cli-` | CLI Operations | Startup, CLI agent, init |
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| 7 | `deploy-` | Deployment & Security | Docker networking, state storage, health checks |
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| 8 | `advanced-` | Advanced Patterns | Prompt targets, rate limits, multi-listener |
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@ -1,6 +1,6 @@
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---
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name: plano-cli-operations
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description: Apply Plano CLI best practices. Use for startup troubleshooting, cli_agent workflows, prompt target generation, and template-based project bootstrapping.
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description: Apply Plano CLI best practices. Use for startup troubleshooting, cli_agent workflows, and template-based project bootstrapping.
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license: Apache-2.0
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metadata:
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author: katanemo
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@ -15,20 +15,17 @@ Use this skill when the task is primarily operational and CLI-driven.
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- "Fix `planoai up` failures"
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- "Use `planoai cli_agent` with coding agents"
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- "Generate prompt targets from Python functions"
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- "Bootstrap a project with `planoai init` templates"
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## Apply These Rules
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- `cli-startup`
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- `cli-agent`
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- `cli-generate`
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- `cli-init`
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## Execution Checklist
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1. Follow startup validation order before deep debugging.
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2. Use `cli_agent` to route coding-agent traffic through Plano.
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3. Generate prompt target schema, then wire endpoint details explicitly.
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4. Start from templates for reliable first-time setup.
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5. Provide a compact runbook with exact CLI commands.
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3. Start from templates for reliable first-time setup.
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4. Provide a compact runbook with exact CLI commands.
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@ -1,91 +0,0 @@
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---
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title: Generate Prompt Targets from Python Functions with `planoai generate_prompt_targets`
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impact: MEDIUM
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impactDescription: Manually writing prompt_targets YAML for existing Python APIs is error-prone — the generator introspects function signatures and produces correct YAML automatically
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tags: cli, generate, prompt-targets, python, code-generation
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---
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## Generate Prompt Targets from Python Functions with `planoai generate_prompt_targets`
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`planoai generate_prompt_targets` introspects Python function signatures and docstrings to generate `prompt_targets` YAML for your Plano config. This is the fastest way to expose existing Python APIs as LLM-callable functions without manually writing the YAML schema.
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**Python function requirements for generation:**
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- Use simple type annotations: `int`, `float`, `bool`, `str`, `list`, `tuple`, `set`, `dict`
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- Include a docstring describing what the function does (becomes the `description`)
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- Complex Pydantic models must be flattened into primitive typed parameters first
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**Example Python file:**
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```python
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# api.py
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def get_stock_quote(symbol: str, exchange: str = "NYSE") -> dict:
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"""Get the current stock price and trading data for a given stock symbol.
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Returns price, volume, market cap, and 24h change percentage.
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"""
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# Implementation calls stock API
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pass
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def get_weather_forecast(city: str, days: int = 3, units: str = "celsius") -> dict:
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"""Get the weather forecast for a city.
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Returns temperature, precipitation, and conditions for the specified number of days.
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"""
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pass
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def search_flights(origin: str, destination: str, date: str, passengers: int = 1) -> list:
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"""Search for available flights between two airports on a given date.
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Date format: YYYY-MM-DD. Returns list of flight options with prices.
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"""
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pass
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```
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**Running the generator:**
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```bash
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planoai generate_prompt_targets --file api.py
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```
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**Generated output (add to your config.yaml):**
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```yaml
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prompt_targets:
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- name: get_stock_quote
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description: Get the current stock price and trading data for a given stock symbol.
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parameters:
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- name: symbol
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type: str
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required: true
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- name: exchange
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type: str
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required: false
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default: NYSE
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# Add endpoint manually:
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endpoint:
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name: stock_api
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path: /quote?symbol={symbol}&exchange={exchange}
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- name: get_weather_forecast
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description: Get the weather forecast for a city.
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parameters:
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- name: city
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type: str
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required: true
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- name: days
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type: int
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required: false
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default: 3
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- name: units
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type: str
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required: false
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default: celsius
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endpoint:
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name: weather_api
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path: /forecast?city={city}&days={days}&units={units}
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```
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After generation, manually add the `endpoint` blocks pointing to your actual API. The generator produces the schema; you wire in the connectivity.
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Reference: https://github.com/katanemo/archgw
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