Local AIPROPRO REQUIREDFC-AI-014
LLMOps Claw
llmops-claw
v0.2.0May 25, 2026LLM operations — monitoring, cost tracking, latency budgets, and production readiness for language model deployments
LLMOps is not just CI/CD for models — it is the operational discipline of shipping stochastic software. I care about prompt versioning with semantic diffing, A/B rollout with statistical significance guards (not "looks better"), guardrail latency budgets that must not exceed your p99 serving target, and cost attribution so you can trace every $0.003 inference to a specific feature. I design evaluation gates that run in your CI pipeline, rollout strategies with automated rollback triggers, and cost dashboards with per-endpoint, per-model, and per-tenant breakdowns.
OpenClaw
Hermes
Claude Code
Codex
Cursor
Windsurf
Aider
OllamaPRIMARY ACTION
Unlock with ProCOMPATIBLE WITH
OpenClawHermesClaude CodeCodex+4
8 compatible tools4 output formats
When to Use
- Run local models with private workflows
- Tune inference for local hardware
- Choose effective GGUF variants
- Benchmark practical latency and quality
Compatible Frameworks
OpenClawHermesClaude CodeCodexCursorWindsurfAiderOllama8 TOOLS
Quality Gates
- no fake claims
- request logging configured
- cost tracking enabled
- performance dashboards defined
4 GATES DEFINED
Expected Outputs
LLMOps Pipeline Specification with Evaluation Gates and Rollback TriggersA/B Rollout Protocol with Statistical Significance Calculator and Guard MetricsCost Attribution Dashboard Schema with Per-Endpoint/Model/Tenant BreakdownPrompt Version Audit Trail with Semantic Diff and Deprecation Schedule
Native exports per tool
OpenClaw10 files
openclaw/AGENTS.mdopenclaw/SOUL.mdopenclaw/TOOLS.md+7 moreHermes5 files
hermes/skills/flickclaw/llmops-claw/SKILL.mdhermes/skills/flickclaw/llmops-claw/references/workflow.mdhermes/skills/flickclaw/llmops-claw/references/quality-gates.md+2 moreClaude Code6 files
claude-code/CLAUDE.mdclaude-code/.claude/skills/llmops-claw/SKILL.mdclaude-code/.claude/skills/llmops-claw/references/workflow.md+3 moreCodex5 files
codex/AGENTS.mdcodex/.flickclaw/agents/llmops-claw/codex.mdcodex/.flickclaw/agents/llmops-claw/workflow.md+2 moreCursor3 files
cursor/.cursor/rules/flickclaw-llmops-claw.mdccursor/.cursor/rules/flickclaw-llmops-claw-workflow.mdccursor/.cursor/rules/flickclaw-llmops-claw-quality-gates.mdcWindsurf3 files
windsurf/.windsurf/rules/flickclaw-llmops-claw.mdwindsurf/.windsurf/rules/flickclaw-llmops-claw-workflow.mdwindsurf/.windsurf/rules/flickclaw-llmops-claw-quality-gates.mdAider3 files
aider/CONVENTIONS.mdaider/aider.mdaider/.aider.conf.ymlOllama4 files
ollama/Modelfileollama/system-prompt.mdollama/template.md+1 moreInstall Commands
Install the FlickClaw CLI, then select your AI agent framework below to get the correct install command.
Step 1: Install CLI (one-time)
npm install -g @flickclaw/cli@latestStep 2: Select Framework
OpenClaw
npm exec --yes @flickclaw/cli@latest -- install llmops-claw --target openclawDownload as ZIP
Example Prompt
Try this prompt with LLMOps Claw to see what it can do:
Optimize the local model setup for performance. Benchmark current config and suggest improvements for LLMOps Pipeline Specification with Evaluation Gates and Rollback Triggers, A/B Rollout Protocol with Statistical Significance Calculator and Guard Metrics, Cost Attribution Dashboard Schema with Per-Endpoint/Model/Tenant Breakdown.Example Output
IllustrativeWhat a typical LLMOps Claw report looks like:
# LLMOps Claw — Assessment Report **Project**: ollama-deployment **Context**: a local LLM deployment running Llama 3.1 8B on consumer GPU hardware **Generated**: 2026-07-10 ## Executive Summary The LLMOps Claw completed its review of ollama-deployment (a local LLM deployment running Llama 3.1 8B on consumer GPU hardware). 3 findings were identified with concrete remediation steps. All quality gates were verified before delivery. ## Findings | # | Severity | Area | Finding | Recommended Action | |---|----------|------|---------|-------------------| | 1 | **P1** | Performance | Inference latency spikes to 8s under concurrency | Enable continuous batching and set max_batch=4 | | 2 | **P2** | Memory | Model consumes 18GB VRAM, headroom insufficient | Switch to Q4_K_M quantization, target <12GB | | 3 | **P2** | Setup | No Modelfile with system prompt defined | Create Modelfile with role, constraints, and templates | ## Quality Gates - [✓] prompt_version_semantic_diff_verified - [✓] ab_rollout_statistical_significance_gate - [✓] guardrail_latency_budget_under_p99 ## Outputs Generated - **LLMOps Pipeline Specification with Evaluation Gates and Rollback Triggers**: Included in the report above. - **A/B Rollout Protocol with Statistical Significance Calculator and Guard Metrics**: Included in the report above. - **Cost Attribution Dashboard Schema with Per-Endpoint/Model/Tenant Breakdown**: Included in the report above. - **Prompt Version Audit Trail with Semantic Diff and Deprecation Schedule**: Included in the report above. ## Validation - [x] All quality gates passed (3/3) - [x] 3 findings documented with severity and remediation - [x] 4 output sections generated - [x] Evidence collected and referenced --- *This is an illustrative example output from FlickClaw. Results vary based on project context.*
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