Chat FormatCAUTION
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Overview
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
ef7d4f0535e5OBSERVED · 2026-09-25What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: chat-format description: Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval argument-hint: "<prompt> [--provider anthropic|openai|gemini|ollama|cohere]" allowed-tools: mcp__plugin_ruflo-core_ruflo__ruvllm_chat_format mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route mcp__plugin_ruflo-core_ruflo__ruvllm_status Bash --- # Chat Format Format prompts for multi-provider LLM inference with context retrieval. ## When to use When preparing prompts for different LLM providers (Claude, GPT, Gemini, Ollama) or building RAG pipelines with HNSW-powered context retrieval. ## Steps 1. **Format chat** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_chat_format` with messages and target provider 2. **Create HNSW index** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create` for context retrieval 3. **Add documents** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add` to index documents 4. **Route query** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route` to find relevant context 5. **Check status** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_status` for provider availability ## Supported providers - Anthropic (Claude) — native format - OpenAI (GPT) — chat completion format - Google (Gemini) — generative AI format - Ollama — local model format - Cohere — generate/chat format
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__chat-format.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-25 | ef7d4f0535e5 | CAUTION | B | 89 | first audit |
Questions
What does the Chat Format skill do?
🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Is Chat Format safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Chat Format access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (ef7d4f0535e5), read on 2026-09-25. The repository is watched, and a new audit runs when it changes — this is the first audit.