$ loading_
Analyze and compile prompts for LLMs with intent and cost awareness.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "signalforge" yet — see the docs or source repo.
Analyze this prompt for GPT and Claude, identify unclear intent, missing context, and token waste, then generate optimized versions for each model.
Returns a diagnosis checklist and clearer, more cost-efficient prompts tailored to each model.
I want an LLM to summarize user interviews. Compile a task-specific prompt based on my goal, output format, and constraints, and tell me what key information is missing.
Generates a ready-to-use interview summary prompt plus suggested missing context to add.
Evaluate the cost risks of this workflow prompt set, identify repeated instructions, bloated context, and low-value steps, and suggest leaner alternatives.
Provides cost risk analysis, waste explanations, and a more efficient streamlined prompt set.
Draft and propose Signal messages with explicit human approval before sending.
Manage task records and seeded documents locally through controlled MCP tool functions.
Ingest and semantically search local documents, chunks, and code blocks.
Build, debug, and manage software tasks with natural language across LLMs.
Delegate summarization, classification, extraction, and drafting tasks to a local LLM.
Give AI coding assistants memory, code graph insight, and safe multi-agent coordination.