4 power-user skills for the AI-coding era. Save real money on token costs, make your codebase legible to AI agents, and keep memory across sessions.
These aren't security patterns or framework boilerplate — they're the invisible infrastructure that makes AI-driven development actually work on serious codebases.
"AI agents are pattern-matching machines. The patterns I match against decide what they produce. These skills give them better patterns."
| # | Skill | What it solves | Who needs it |
|---|---|---|---|
| 1 | ai-codebase-rules-card |
"Claude / Cursor doesn't follow my conventions" | Anyone with a CLAUDE.md < 100 lines |
| 2 | graphify-codebase-digest |
"AI burns 20K tokens re-exploring my codebase every session" | Anyone with > 50 source files |
| 3 | ai-session-memory |
"AI forgets everything between chats" | Anyone doing multi-week AI-assisted work |
| 4 | ai-multi-agent-audit |
"One AI agent can't audit a serious codebase deeply" | Anyone needing comprehensive code review |
Typical savings on a mid-size codebase (~200-400 source files):
| Metric | Without these | With these | Δ |
|---|---|---|---|
| Tokens per AI session (exploration alone) | ~20,000 | ~500 | -95% |
| Minutes lost re-explaining context per session | 5-10 | 0-1 | -90% |
| AI-driven commits with inconsistent style | ~30% | ~5% | -83% |
| Full audit wall-clock time | 30+ min | 3-5 min | -85% |
At $3/M tokens and 100 sessions/month, that's $50-100/month saved on AI bills alone — before counting the time saved. Your numbers will vary by codebase size and session complexity.
These skills work with Claude Code (and most concepts apply to Cursor, Aider, etc. — adapt the file paths).
# Windows PowerShell
Copy-Item -Recurse * "$env:USERPROFILE\.claude\skills\"# macOS / Linux
cp -r */ ~/.claude/skills/ls ~/.claude/skills/ | grep -E "ai-|graphify"
# ai-codebase-rules-card
# ai-multi-agent-audit
# ai-session-memory
# graphify-codebase-digestType any of these in chat to invoke:
/ai-codebase-rules-card— when starting a CLAUDE.md / .cursorrules/graphify-codebase-digest— when AI keeps re-exploring your code/ai-session-memory— when AI keeps forgetting context/ai-multi-agent-audit— when you need a deep audit
Or just describe the problem naturally — the trigger keywords route automatically.
┌─────────────────────────────┐
│ ai-codebase-rules-card │ ← rules AI must follow
│ (the LAW of your codebase)│
└──────────────┬──────────────┘
│
▼
┌─────────────────────────────┐
│ graphify-codebase-digest │ ← AI's map of your codebase
│ (the GEOGRAPHY) │
└──────────────┬──────────────┘
│
▼
┌─────────────────────────────┐
│ ai-session-memory │ ← AI's history with you
│ (the RELATIONSHIP) │
└──────────────┬──────────────┘
│
▼
┌─────────────────────────────┐
│ ai-multi-agent-audit │ ← AI's team for deep work
│ (the FLEET) │
└─────────────────────────────┘
Together: AI knows your rules, your map, your history, and works in parallel.
If you're starting fresh:
ai-codebase-rules-card— set the rules first (1-2 days for full system)graphify-codebase-digest— auto-generate the map (4-6 hours)ai-session-memory— build memory as you go (incremental, weeks)ai-multi-agent-audit— when you have something worth deeply auditing
Don't try to do all 4 in one weekend. Each rewards investment over time.
These patterns came from a real production codebase development workflow on a multi-hundred-file React + Vite + Firebase SPA:
- ~80 AI-driven commits over 6 months
- 3 multi-version security audits
- Continuous memory supersession discipline across releases
- Auto-updated architecture digest on every release
- 5-agent parallel audit pattern producing high-confidence verdicts in 3 minutes
Every pattern survived the question "is this paying for its maintenance cost?"
These skills follow a few principles. Read them before deciding to deviate:
- AI is a pattern-matching machine — match against good patterns, get good output
- Make context cheap to load — every token AI spends exploring is a token not solving
- Make memory structured, not vibes — typed memory files > one giant scratchpad
- Verify before reporting — multi-agent audits without verification are noise machines
- Auto-update or it dies — manual maintenance of context = nobody maintains it
- The doc IS the law — but only if there's enforcement; otherwise it's aspirational
- Not a CLAUDE.md template — they're a SYSTEM to BUILD your CLAUDE.md
- Not a one-shot install — they reward incremental investment
- Not framework-specific — patterns apply to any codebase, examples assume JS/TS
- Not magic — they tilt odds, don't eliminate AI mistakes
If you want a 5-minute setup with no thinking required, look elsewhere. If you want patterns that pay back 10x over months, this is it.
Patterns extracted from one production codebase. Feedback welcomed:
- "Skill X doesn't apply to my stack" → patterns are usually generalizable, file an issue
- "I built variant Y of these" → link it, will reference
- "These saved me $N this month" → would love to know
MIT. Use freely. Attribution appreciated but not required.
If these skills help you ship faster + cheaper with AI, that's the entire point.
Patterns extracted from a real production solo-dev workflow on a multi-hundred-file React + Vite + Firebase SPA. Battle-tested across ~80 AI-driven commits and multiple multi-agent audits.
Toolkit version: 1.0.0 Skills: 4 · Total guidance: ~1,500 lines Setup cost: ~1-4 days · Payback: 1-2 weeks at moderate AI usage
"In 2026, the value isn't in the skills you have — it's in the systems you've built for your AI tools to use them."