Local AIPROPRO REQUIREDFC-AI-011
Deployment Claw
deployment-claw
Local AI deployment: Docker, GPU allocation, inference servers, and model serving.
Deployment Claw sets up local AI deployment with Docker, GPU allocation, inference servers (vLLM, TGI), and model serving configurations.
OpenClaw
Hermes
Claude Code
Codex
Cursor
Windsurf
Aider
OllamaPRIMARY ACTION
Unlock with ProCOMPATIBLE WITH
OpenClawHermesClaude CodeCodex+4
OpenClaw is the default target. Cursor example below.
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 explicit gate list for this agent in the current dataset.
4 GATES DEFINED
Expected Outputs
Native exports per tool
OpenClaw10 files
openclaw/AGENTS.mdopenclaw/SOUL.mdopenclaw/TOOLS.md+7 moreHermes5 files
hermes/skills/flickclaw/deployment-claw/SKILL.mdhermes/skills/flickclaw/deployment-claw/references/workflow.mdhermes/skills/flickclaw/deployment-claw/references/quality-gates.md+2 moreClaude Code6 files
claude-code/CLAUDE.mdclaude-code/.claude/skills/deployment-claw/SKILL.mdclaude-code/.claude/skills/deployment-claw/references/workflow.md+3 moreCodex5 files
codex/AGENTS.mdcodex/.flickclaw/agents/deployment-claw/codex.mdcodex/.flickclaw/agents/deployment-claw/workflow.md+2 moreCursor3 files
cursor/.cursor/rules/flickclaw-deployment-claw.mdccursor/.cursor/rules/flickclaw-deployment-claw-workflow.mdccursor/.cursor/rules/flickclaw-deployment-claw-quality-gates.mdcWindsurf3 files
windsurf/.windsurf/rules/flickclaw-deployment-claw.mdwindsurf/.windsurf/rules/flickclaw-deployment-claw-workflow.mdwindsurf/.windsurf/rules/flickclaw-deployment-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 deployment-claw --target openclawDownload as ZIP
Example Prompt
Try this prompt with Deployment Claw to see what it can do:
Optimize the local model setup for performance. Benchmark current config and suggest improvements for .Example Output
IllustrativeWhat a typical Deployment Claw report looks like:
# Deployment Claw — Assessment Report **Project**: ollama-deployment **Context**: a local LLM deployment running Llama 3.1 8B on consumer GPU hardware **Generated**: 2026-05-26 ## Executive Summary The Deployment 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 - [✓] no_fake_claims - [✓] docker_configuration_valid - [✓] gpu_allocation_documented ## Outputs Generated ## Validation - [x] All quality gates passed (3/3) - [x] 3 findings documented with severity and remediation - [x] 0 output sections generated - [x] Evidence collected and referenced --- *This is an illustrative example output from FlickClaw. Results vary based on project context.*