Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Get Started Free →

Finalize Your LLMOps Skill · majiayu000 | DecimalAI

ai mlby majiayu000

Finalize Your LLMOps Skill

majiayu000/finalize-your-llmops-skill

Source majiayu000/claude-skill-registry ↗License MIT

Consolidate all Part 8 skills into a production-ready llmops-fine-tuner skill

SkillScore68Maintained

Automatic

AI Engineer pack

Last verified 9/3/2026

What it changes

Passed 15 of 22 test cases with the skill, and 8 of 22 without it.

Without skill36%

With skill68%

Checks it passed that the model alone missed

The pipeline section defines exactly 6 stages.
The hardware execution specifies T4 GPU compatibility.
The hardware constraint specifies T4 GPU compatibility.

and 12 more in the full report

8 fixed7 passed either way6 still failing1 broke

Pass-rate lift: +32 pp
Tokens: +73%
Agent turns: 0%
Response time: -15%

Model gemini-3.6-flashMeasured 9/3/2026v115 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/majiayu000-finalize-your-llmops-skill/SKILL.md https://app.decimal.ai/s/majiayu000-finalize-your-llmops-skill/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add majiayu000-finalize-your-llmops-skill, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/majiayu000-finalize-your-llmops-skill/ and pins it in .decimal/skills.lock

decimalai skills add majiayu000-finalize-your-llmops-skill

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/majiayu000-finalize-your-llmops-skill/SKILL.md https://app.decimal.ai/s/majiayu000-finalize-your-llmops-skill/SKILL.md

Raw file: /s/majiayu000-finalize-your-llmops-skill/SKILL.md ↗ · machine-readable summary at /s/majiayu000-finalize-your-llmops-skill.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 8

Prompt

List the operational constraints and resource limits (hardware, cost, and execution duration) enforced across the unified fine-tuning pipeline.

What was checked, without and with the skill
Checked	Without skill	With skill
The hardware constraint specifies T4 GPU compatibility.	Missed	Passed
The financial constraint specifies maximum cost under $1.	Missed	Passed
The execution time limit specifies maximum duration under 4 hours.	Missed	Passed

case-16 · gemini-3.6-flash · 9/3/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md387 linessingle file

Finalize Your LLMOps Skill

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.

Why Consolidation Matters

The Problem with 9 Separate Skills:

When you need to fine-tune a model for production, you currently must:

Invoke llmops-decision-framework to determine if fine-tuning is appropriate
Invoke llmops-compute-planner to budget VRAM
Invoke llmops-data-engineer to create datasets
Invoke llmops-fine-tuner for training
Invoke model-evaluation for quality gates
Invoke model-serving for deployment
Invoke agent-integration for framework connection

That is 7 separate skill invocations with manual handoffs between each. Errors compound. Context is lost.

The Solution: Unified Orchestration

A consolidated skill:

Accepts a single specification
Orchestrates all pipeline stages internally

Track record

Benchmark

15 / 22

test cases passed, vs 8 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

majiayu000 / claude-skill-registry

skills/ai-llm/72-capstone-end-to-end-llmops/SKILL.md

View on GitHub ↗

License MITImported 8/10/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Finalize Your LLMOps Skill

Why Consolidation Matters

Track record

Benchmark

Adoption

SkillSafety

Source

Related skills