majiayu000
majiayu000/claude-skill-registry
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/majiayu000/claude-skill-registry --skill llmops-fine-tuner命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
LLM token logprobs and calibration. Per-decision confidence, ECE, Brier, reliability diagrams, low-confidence triage.
Analyze LLM token logprobs and calibration. Use for per-decision confidence, ECE, Brier scores, reliability diagrams, and low-confidence triage.
回顾最近 N 天的 Claude Code 使用记录——扫描原始会话数据，按主题分组汇总"我都做了什么"，并从个人操作系统视角输出模式、风险与增删建议。当用户说 /recap、"看看我这几天做了什么"、"回顾一下我最近的会话"、"这两天我用 claude 干了啥"、"活动回顾" 时使用。
基于 SOC 职业分类
正在显示 SKILL.md
| sidebar_position | 0 |
| title | Finalize Your LLMOps Skill |
| description | Consolidate all Part 8 skills into a production-ready llmops-fine-tuner skill |
| keywords | ["llmops","skill consolidation","digital fte","fine-tuning","production"] |
| chapter | 72 |
| lesson | 0 |
| duration_minutes | 30 |
| skills | [{"name":"Skill Consolidation Architecture","proficiency_level":"B2","category":"Technical","bloom_level":"Create","digcomp_area":"Digital Content Creation","measurable_at_this_level":"Student designs a unified skill that composes 6+ specialized skills into cohesive intelligence"},{"name":"Intelligence Integration Patterns","proficiency_level":"B2","category":"Applied","bloom_level":"Evaluate","digcomp_area":"Problem-Solving","measurable_at_this_level":"Student evaluates which skill components belong in unified skill vs remain separate"},{"name":"Production Skill Validation","proficiency_level":"B1","category":"Technical","bloom_level":"Apply","digcomp_area":"Safety","measurable_at_this_level":"Student applies verification workflow to ensure consolidated skill produces consistent results"}] |
| learning_objectives | [{"objective":"Consolidate 6+ specialized LLMOps skills into one production-ready skill","proficiency_level":"B2","bloom_level":"Create","assessment_method":"Review consolidated skill structure, verify all capabilities preserved"},{"objective":"Evaluate skill composition decisions using cohesion principles","proficiency_level":"B2","bloom_level":"Evaluate","assessment_method":"Justify which components merged vs separated based on cohesion analysis"},{"objective":"Validate consolidated skill through comprehensive testing","proficiency_level":"B1","bloom_level":"Apply","assessment_method":"Execute skill on test scenarios, verify outputs match specialized skill quality"}] |
| cognitive_load | {"new_concepts":4,"assessment":"4 concepts (skill consolidation, cohesion analysis, capability preservation, production validation) within B1-B2 limit (7-10 concepts)"} |
| differentiation | {"extension_for_advanced":"Design skill versioning strategy and backwards compatibility approach","remedial_for_struggling":"Focus on consolidating just 3 skills (data, training, deployment) before adding remaining"} |
Throughout Part 8, you built specialized skills for each LLMOps capability:
| Chapter | Skill | Capability |
|---|---|---|
| 61 | llmops-decision-framework | When to fine-tune |
| 62 | llmops-compute-planner | VRAM budgeting |
| 63 | llmops-data-engineer | Dataset creation |
| 64 | llmops-fine-tuner | Training workflows |
| 67 | model-merging | Adapter combination |
| 68 | model-alignment | DPO safety |
| 69 | model-evaluation | Quality gates |
| 70 | model-serving | Deployment |
| 71 | agent-integration | Framework connection |
Now you will consolidate these into one production-ready llmops-fine-tuner skill that composes all capabilities. This is Layer 3 work: transforming specialized knowledge into reusable intelligence.
The Problem with 9 Separate Skills:
When you need to fine-tune a model for production, you currently must:
llmops-decision-framework to determine if fine-tuning is appropriatellmops-compute-planner to budget VRAMllmops-data-engineer to create datasetsllmops-fine-tuner for trainingmodel-evaluation for quality gatesmodel-serving for deploymentagent-integration for framework connectionThat is 7 separate skill invocations with manual handoffs between each. Errors compound. Context is lost.
The Solution: Unified Orchestration
A consolidated skill:
This transforms your skills from components into a product.
Your consolidated skill follows this architecture:
llmops-fine-tuner (Unified)
├── Decision Layer
│ └── When to fine-tune? (from Ch.61)
│
├── Planning Layer
│ └── Compute requirements (from Ch.62)
│
├── Data Layer
│ └── Dataset creation (from Ch.63)
│
├── Training Layer
│ ├── Fine-tuning (from Ch.64)
│ ├── Merging (from Ch.67)
│ └── Alignment (from Ch.68)
│
├── Validation Layer
│ └── Quality gates (from Ch.69)
│
└── Deployment Layer
├── Serving (from Ch.70)
└── Integration (from Ch.71)
Not all skills should merge. Ask:
| Question | Keep Separate If... | Merge If... |
|---|---|---|
| Usage frequency | Used independently often | Always used together |
| Domain coupling | Different expertise needed | Same expertise |
| Execution context | Different environments | Same environment |
| Failure isolation | Need to retry independently | Fail together, succeed together |
Analysis for LLMOps Skills:
| Skill | Verdict | Reasoning |
|---|---|---|
llmops-decision-framework | Merge | Always first step, gates everything |
llmops-compute-planner | Merge | Informs all training decisions |
llmops-data-engineer | Merge | Pipeline stage, not standalone |
llmops-fine-tuner | Core | Central skill, others compose around it |
model-merging | Merge | Optional but integrated |
model-alignment | Merge | Optional but integrated |
model-evaluation | Merge | Always required, gates deployment |
model-serving | Merge | Pipeline terminus |
agent-integration | Merge | Final step, produces Digital FTE |
Result: All skills merge into unified llmops-fine-tuner.
Create your consolidated skill:
mkdir -p .claude/skills/llmops-fine-tuner
SKILL.md Structure:
---
name: llmops-fine-tuner
description: "End-to-end LLMOps pipeline for fine-tuning, evaluating, and deploying custom models as Digital FTEs. Use when building production model deployments."
---
# LLMOps Fine-Tuner Skill
## Purpose
Complete pipeline from specification to deployed Digital FTE.
## When to Use
- Building custom models for specific domains
- Creating proprietary AI products
- Deploying fine-tuned models to production
## Pipeline Stages
### Stage 1: Decision (from Ch.61)
Evaluate whether fine-tuning is appropriate for this use case.
[Include decision framework here]
### Stage 2: Planning (from Ch.62)
Calculate VRAM budget and hardware requirements.
[Include compute planning here]
### Stage 3: Data (from Ch.63)
Create and validate training dataset.
[Include data engineering here]
### Stage 4: Training (from Ch.64, 67, 68)
Execute fine-tuning with optional merging and alignment.
[Include training workflows here]
### Stage 5: Evaluation (from Ch.69)
Run quality gates before deployment.
[Include evaluation criteria here]
### Stage 6: Deployment (from Ch.70, 71)
Deploy model and integrate with agent frameworks.
[Include deployment and integration here]
## Specification Template
When invoking this skill, provide:
- **Domain**: What expertise is this model encoding?
- **Base Model**: Which model to fine-tune
- **Dataset Source**: Where does training data come from
- **Quality Target**: What accuracy threshold for deployment
- **Deployment Target**: Where model will run (Ollama, API, etc.)
## Constraints
- Colab Free Tier compatible (T4 GPU)
- Total cost < $1
- Completion time < 4 hours
For each specialized skill, extract the core patterns and integrate:
From llmops-decision-framework:
# Decision criteria
def should_fine_tune(use_case):
"""
Returns True if fine-tuning is appropriate.
Fine-tune when:
- Prompting consistently fails (>30% error rate)
- Domain requires specialized vocabulary
- Latency requirements mandate smaller model
- Cost optimization requires local deployment
Don't fine-tune when:
- Prompting achieves 90%+ accuracy
- Task is one-off or experimental
- No clear evaluation criteria exist
"""
pass
From llmops-compute-planner:
# VRAM budget calculation
def calculate_vram_budget(model_size_b, batch_size=1, lora_rank=16):
"""
Estimate VRAM requirements for training.
Formula: VRAM = model_params * 4 bytes + optimizer_state * 8 bytes + activations
With LoRA: Reduce by ~80% (only training adapter weights)
T4 GPU (16GB):
- 1B params: Works with batch_size=4
- 3B params: Works with batch_size=2
- 7B params: Works with batch_size=1, gradient_checkpointing
- 8B params: Works with 4-bit quantization
"""
pass
From llmops-data-engineer:
# Dataset creation patterns
def create_training_dataset(examples, format="chat"):
"""
Create JSONL dataset for fine-tuning.
Format options:
- chat: {"messages": [{"role": "user", "content": ...}, ...]}
- instruct: {"instruction": ..., "input": ..., "output": ...}
- completion: {"text": "Input: ... Output: ..."}
"""
pass
Each stage produces artifacts consumed by the next:
Stage 1 (Decision) → decision_report.json
└── Inputs: use_case_description
└── Outputs: should_proceed, reasoning, constraints
Stage 2 (Planning) → compute_plan.json
└── Inputs: decision_report, model_choice
└── Outputs: vram_budget, batch_size, hardware_requirements
Stage 3 (Data) → dataset.jsonl + data_report.json
└── Inputs: raw_examples, format_spec
└── Outputs: training_data, validation_data, quality_metrics
Stage 4 (Training) → adapter_weights/ + training_report.json
└── Inputs: base_model, dataset, compute_plan
└── Outputs: trained_adapter, loss_curves, checkpoint_path
Stage 5 (Evaluation) → eval_report.json
└── Inputs: trained_model, test_dataset, quality_thresholds
└── Outputs: accuracy, pass_fail, regression_analysis
Stage 6 (Deployment) → deployed_model + integration_config.json
└── Inputs: trained_model, deployment_target
└── Outputs: model_endpoint, ollama_modelfile, agent_config
Test your consolidated skill end-to-end:
# Test with minimal example
claude --skill llmops-fine-tuner "
Create a Task API model:
- Domain: Task management tool-calling
- Base: Qwen 2.5 3B Instruct
- Data: 200 synthetic examples
- Target: 90% tool-calling accuracy
- Deploy: Local Ollama
"
Expected Output:
Stage 1: Decision PASS - Fine-tuning appropriate for tool-calling specialization
Stage 2: Planning COMPLETE - T4 compatible, batch_size=2, LoRA rank=16
Stage 3: Data COMPLETE - 200 examples generated, 80/20 train/val split
Stage 4: Training COMPLETE - Loss converged: 0.12, Epoch 3/3
Stage 5: Evaluation PASS - Tool accuracy: 94%, threshold: 90%
Stage 6: Deployment COMPLETE - Ollama model registered: task-api-qwen3b
Digital FTE Ready: task-api-qwen3b
Verify your consolidated skill preserves all original capabilities:
| Original Skill | Key Capability | Preserved? | Test |
|---|---|---|---|
llmops-decision-framework | Decision tree logic | [ ] | "Should I fine-tune for X?" |
llmops-compute-planner | VRAM calculation | [ ] | "What hardware for 8B model?" |
llmops-data-engineer | JSONL generation | [ ] | "Create dataset from examples" |
llmops-fine-tuner | Training workflow | [ ] | "Fine-tune with these params" |
model-merging | Adapter combination | [ ] | "Merge these LoRA adapters" |
model-alignment | DPO training | [ ] | "Align model for safety" |
model-evaluation | Quality gates | [ ] | "Evaluate against benchmarks" |
model-serving | Deployment | [ ] | "Deploy to Ollama" |
agent-integration | Framework connection | [ ] | "Connect to OpenAI SDK" |
Your consolidated skill is production-ready when:
Your consolidated llmops-fine-tuner skill:
| Capability | Source | Integration |
|---|---|---|
| Decision logic | Ch.61 | Stage 1 gate |
| VRAM budgeting | Ch.62 | Planning layer |
| Data creation | Ch.63 | Automatic dataset prep |
| Training workflows | Ch.64, 67, 68 | Core execution |
| Quality gates | Ch.69 | Pre-deployment validation |
| Deployment | Ch.70, 71 | Final stage automation |
This is intelligence accumulation: 11 chapters of knowledge compressed into one reusable skill.
I'm consolidating these LLMOps skills into one unified skill:
- Decision framework (when to fine-tune)
- Compute planning (VRAM budgeting)
- Data engineering (dataset creation)
- Training (fine-tuning, merging, alignment)
- Evaluation (quality gates)
- Deployment (serving, integration)
Help me design the SKILL.md structure. What sections should it have?
How should stages hand off to each other? What specification template
should users provide when invoking this skill?
What you're learning: Skill architecture design—how to structure reusable intelligence for production use.
I've consolidated my LLMOps skills. Help me create a test plan to verify
all capabilities are preserved. For each original skill, give me:
1. A test prompt that exercises that capability
2. Expected output that proves it works
3. A failure mode to watch for
What you're learning: Validation methodology—ensuring consolidation doesn't lose specialized capabilities.
I'm building a consolidated LLMOps skill for [your domain: legal, medical,
finance, etc.]. My original skills handle [list your specialized skills].
Help me analyze: Which skills should merge? Which should stay separate?
Ask me questions about how I use each skill to determine the right
composition.
What you're learning: Cohesion analysis—understanding when to merge vs. separate based on usage patterns.
When consolidating skills, preserve all safety mechanisms from individual skills. A unified skill must maintain the same guardrails as its components—never optimize away safety checks for convenience.