Hey everyone, Typical workflow: I’d fire up an AI CLI harness (mainly CC) with a vague idea, drop a quick paragraph, and watch the model confidently generate boilerplate using implicit defaults that didn’t fit my stack. Cue the next hour of prompt-engineering it back on track. The root cause was garbage-in, garbage-out: the initial context was too sparse, forcing the model to guess my intent. So I built promptPrimer — a meta-prompt system that runs inside your agentic CLI harness and turns the agent into a prompt generator for a fresh session. How it Works Classify: You describe a scrambled idea; it classifies the task into one of nine domains (coding, data, writing, research, documentation, business, education, creative, general). Consult: It loads domain-specific best practices and asks 3–8 focused clarifying questions in a single batch. Generate: It writes a tailored prompt file you hand to a new agent session to actually do the work. Scaffold: That second session builds a planning scaffold, sized to task complexity, and stops for your review before any deliverable work begins. Note: It does not do the work. It prepares the work. Why I’m posting this Two things make promptPrimer different from “a prompt library”:
- Every type module is anchored to a named domain framework Every best practice, artifact, and failure mode is concrete and enforceable, not platitudinal: * Documentation: Anchors to Diátaxis. * Education: Anchors to Bloom’s taxonomy and Wiggins/McTighe backward design. * Research: Anchors to PRISMA discipline. * Business: Anchors to Minto’s pyramid principle. * Data: Anchors to schema-first practices. * Writing: Uses a concrete 19-phrase AI-slop ban list. * Creative: Anchors to named anti-references (e.g., “don’t resemble Blue Bottle’s stark minimalism”).
- Every type module is A/B tested I ran a controlled multi-agent experiment: 9 units, 3 conditions per unit, 27 producer subagents, and 9 blind evaluator subagents scoring on a 5-criterion rubric. * Evidence-based: Eight of nine augmentations won or tied. * Self-correcting: One was rejected because the experiment showed it actively hurt scaffold quality (coding + inline worked-examples diluted the plan). * Audit Trail: The complete experimental audit trail is reproduced in the PDF report appendices. Other things that might interest you Token efficiency: Every generated prompt bakes in an “autonomy block.” The downstream agent decides-documents-proceeds on reversible choices instead of drip-asking, saving context in long sessions. Compaction resilience: Includes a STATE.md snapshot file with a fixed 8-section schema (1–2 KB budget). It survives harness compaction without quality loss. Harness-agnostic: Works in Claude Code, Gemini CLI, Codex CLI, OpenCode, Cursor, Aider, etc. The repo ships CLAUDE.md, GEMINI.md, and AGENTS.md for automatic pickup. Beginner-friendly: Ten explicit steps for CLI novices and a “two folders” mental model FAQ. Contribution-ready: Use knowledge/new_type_workflow.md to add new domains. No new module ships without evidence that it beats the general fallback. Links Repository: https://github.com/SeidSmatti/promptPrimer Full Report (PDF): Download Report Contribution guide: CONTRIBUTING.md in the repo root. License: MIT. What I’m asking for Feedback, criticism, bug reports, and contributions. Especially: Module Improvements: If you have a change, open a PR. Note: The template requires A/B testing evidence. New Domains: Should I add legal, music composition, scientific modeling, or translation? Use the new_type_workflow.md to submit. Onboarding: If the README is confusing to a beginner, please let me know. UX Stories: If you use it, I’d love to hear whether it helped or hindered your workflow. Thanks for reading! submitted by /u/sMASS_
Originally posted by u/sMASS_ on r/ClaudeCode