Agent skill

create-intent-map

Train LoRA adapters for converting natural language queries into structured QuerySpec JSON.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill create-intent-map --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/ai-ml/create-intent-map/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

--- name: create-intent-map description: > Train LoRA intent mappers for structured query generation using GRPO with execution feedback. Uses DeepSeek-R1-Distill-Qwen-7B as base model. Supports SFT warmup, GRPO training with ArangoDB rewards, and iterative improvement with automatic retry on eval failure. allowed-tools: Bash, Read triggers: - train intent mapper - intent map training - create intent model - lora training - query spec training - train query mapper - grpo training - execution feedback training metadata: short-description: GRPO training for intent mapping with execution feedback provides: - create-intent-map composes: [, task-monitor] --- # Create Intent Map Train LoRA adapters for converting natural language queries into structured QuerySpec JSON. Uses GRPO (Group Relative Policy Optimization) with execution feedback from ArangoDB. ## Training Approaches | Approach | Description | Use When | |----------|-------------|----------| | **GRPO (Recommended)** | RL with execution feedback | Production training | | **SFT Only** | Supervised fine-tuning | Quick baseline | | **Docker SFT** | Dockerized training | RunPod deployment | ## Quick Start (GRPO with Execution Feedback

What's inside
Steps it walks through
  1. Training Approaches
  2. Quick Start (GRPO with Execution Feedback)
  3. Quick Start (Docker SFT - for RunPod)
  4. GRPO Training Pipeline
  5. Reward Functions
  6. Evaluation Thresholds
  7. Architecture
  8. Training Data Format
  9. Commands
  10. Data Preparation
  11. GRPO Training (Recommended)
  12. Docker SFT Training
  13. Utilities
  14. Configuration
Ships with 1 file
  • metadata.json
Commands it runs
cd .pi/skills/create-intent-map
cp .env.example .env
Edit .env with HF_TOKEN, CHUTES_API_KEY, ARANGO_* credentials
View training progress
TensorBoard (if enabled)
More from claude-skill-registry
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About this skill
What does the create-intent-map skill do?

Train LoRA adapters for converting natural language queries into structured QuerySpec JSON.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill create-intent-map --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going