radekamirko

radekamirko / C.R.I.S.P

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CRISP — The missing layer between vibe coding and building something people actually want.

ai-agents ai-agents-framework claude-code claude-code-skill claude-code-skills
17
3
100% credibility
Found Mar 30, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

CRISP is an open-source collection of templates and a five-phase methodology to guide AI builders from vague client requests to detailed, ready-to-implement project specifications.

How It Works

1
🔍 Discover CRISP

You find this helpful guide while searching for a better way to plan AI projects from fuzzy ideas to clear plans.

2
💡 Clarify the Problem

You start by writing down the main problem, checking options like buying ready tools, and deciding if it's worth building.

3
🎯 Define Success

You list everyone involved, measure the current situation, and set clear targets for what good looks like.

4
🗺️ Map Journeys

You sketch out how people will use the system, their steps, feelings, and any pain points.

5
📋 Create Your Plan

You pull everything together into a detailed blueprint with designs, priorities, risks, and exact steps for each building phase.

6
✅ Check Results

You review if your plan will truly move the needle from where things are now.

🚀 Build Confidently

With your complete guide in hand, you start building the right AI solution without wasting time on the wrong thing.

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Star Growth

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AI-Generated Review

What is C.R.I.S.P?

C.R.I.S.P is a structured framework for AI project managers and builders, turning vague client briefs—like those from @p_r_i_s_c_i_la or crisp chat scenarios—into Claude Code-ready specs via five phases: Clarify, Results, Investigate, Spec, and Prove. It solves the top AI failure mode: skipping problem definition, baselines, and validation, ensuring you measure success before building. Users get pre-filled templates for stakeholder maps, success metrics, UX flows, tech stacks with pinned versions, and sprint plans, all in Markdown for easy Claude integration.

Why is it gaining traction?

It stands out by chaining phases explicitly—Clarify feeds Results, no reinventing—enforcing "no spec, no build" with locked AI briefs and security rules like Bearer scans on PRs. Developers hook on the elicitation tricks (hypothesis + client correction) that surface real needs faster than open questions, plus HVLE MVP prioritization for live client talks. Early buzz around avoiding "anne crisp missing" style misalignments in AI gigs.

Who should use this?

AI agency owners scoping client automations, solo devs using Claude or Cursor for agentic builds, consultants productizing discovery (think crisp controller workflows), and PMs defining AI-native products with HITL zones and risk logs.

Verdict

With 17 stars and 1.0% credibility, it's raw and unproven—great docs but zero tests or production miles. Grab it if you're tired of robert crisp missing dayton ohio-level brief gaps in AI; otherwise, wait for traction.

(178 words)

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