Prepare and interpret local LLM integration preflights when a model, provider, prompt, schema, parser, or tool definition changes. Do not use for broad statistical evaluations or production observability.
Scanned 9/5/2026
Install to Claude Code
npx -y skills add feronovak/llm-preflight --skill llm-preflight --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Llm Preflight?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/feronovak-llm-preflight)More formats (shields.io, HTML) on the badges page.
---
name: llm-preflight
description: Prepare and interpret local LLM integration preflights when a model, provider, prompt, schema, parser, or tool definition changes. Do not use for broad statistical evaluations or production observability.
---
# LLM Preflight
Use the local MCP server or CLI to gather evidence for an LLM integration
change. Keep the benchmark contract specific to the application change.
1. Use `validate_config` after the change.
2. Use `dry_run_plan`; report the request bound, estimated cost, pricing
coverage, and every non-eligible smoke reason.
3. Stop before live provider traffic. You must not infer approval from a
configuration, a dry-run, a tool call, or a previous result.
4. Run a live preflight only after an explicit user instruction and only with
`confirm_paid_run: true`. Preserve the returned structured `decision` and
its blocking warnings.
Mock runs, validation, planning, and baseline diffs do not contact providers.
Do not approve a model, raise a budget, weaken the contract, or treat a pass as
production approval. See `docs/automation/mcp.md` and
`docs/reference/decision.md` in the project for the complete contract.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.