Cc

Cck – Auto-generate Claude.md so Claude Code remembers your project

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Cck – Auto-generate Claude.md so Claude Code remembers your project

Every Claude Code session starts fresh. You re-explain your project structure, build commands, and conventions. Every. Single. Time. CLAUDE.md solves this - Claude reads it at session start. But writing it manually is tedious and keeping it updated is worse. `cck sync` scans your codebase and generates CLAUDE.md automatically. It detects project type, languages, entry points, build commands, and conventions. Your handwritten sections outside the markers are preserved on re-sync. Two modes: - `cck sync` - CLAUDE.md for session-start context - `cck hook install` - UserPromptSubmit for per-turn reminders ```bash git clone https://github.com/takawasi/claude-context-keeper && cd claude-context-keeper pip install . cck sync ``` Built from insights running 300+ Claude Code sessions. Zero config, zero AI calls, pure static analysis. GitHub: https://github.com/takawasi/claude-context-keeper

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, context · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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