How are you supposed to use V3?

[jpcoder2]

The docs seem to show a lot of command line commands but in Claude Code there are a bunch of commands you can use there plus the MCP. How are you actually supposed to use this thing? Does someone have an actual step by step walkthrough with a realistic use case building something more than a hello world app?

[braden-reap]

who knows the ai wrote it all in a loop i doubt the dev even really knows what most of hti sdoes ;-)

when i opened it with claude i asked is this useful and it said honestly probably not but still gonna play for a few more minutes

def needs a tui or ui of its own + the dev actually showing it being used instead of tons of keywords with feature names

[braden-reap]

It ran a bunch of commands and tests to see how it works and came back with this

⏺ OK. Now I have a very clear picture of what's real. Here's my pitch:

---

What's actually working and genuinely useful

After probing every tool category, here's what's real (not stubs):

┌──────────────────────────────────────┬───────────┬───────────────────────────────────────────────────────────────────────────────────┐
│ Capability │ Verdict │ Notes │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Semantic memory (HNSW vector search) │ Real │ 384-dim embeddings, ~5-8ms search, cosine similarity works correctly │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Embeddings (MiniLM-L6-v2) │ Real │ Local model, generates proper 384-dim vectors, comparison works │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ AI Defence (prompt injection/PII) │ Real │ Caught injection + PII in <2ms, fast boolean checks │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Trajectory learning (SONA/EWC++) │ Real │ Records decision trajectories, extracts patterns, persists with confidence scores │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Neural patterns │ Real │ Store/search with HNSW, pattern types, embedding-backed │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Browser automation │ Real │ Full Playwright-style: open/snapshot/click/fill/screenshot │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Diff analysis │ Available │ Risk scoring, classification, reviewer suggestion for git diffs │
├──────────────────────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────┤
│ Claims board │ Available │ Task claiming/stealing/rebalancing between agents │
└──────────────────────────────────────┴───────────┴───────────────────────────────────────────────────────────────────────────────────┘

The idea that would make you love this

Build a "learning codebase guardian" for your reap-monorepo.

Here's the concept: a self-improving system that learns your team's patterns over time and gets smarter the more you use it. Concretely:

What it does

1. Pattern memory across sessions — Every time you (or Claude) solve a bug, make an architectural decision, or write a pattern you like, it gets stored as a semantic embedding. Next session, when you hit a similar problem, it finds and surfaces the relevant past decision without
   you asking. Your reap-monorepo has tons of conventions — this remembers them all.
2. Pre-commit intelligence — Before any commit, it automatically:
   - Runs aidefence_scan on the diff to catch leaked secrets/PII (your CLAUDE.md has passwords in it right now btw)
   - Runs analyze_diff-risk to score the change risk
   - Searches memory for patterns related to the changed files and warns if you're violating a past decision
3. Trajectory-based learning — Every coding task records what decisions were made and whether they succeeded. Over time, the SONA system builds confidence about which approaches work for which kinds of tasks. "Last 3 times we built an API endpoint, we used zod + Result type and it
   worked well (92% confidence)" — it routes future similar tasks to the known-good approach.
4. Semantic code search that actually understands intent — Instead of grep for exact strings, you ask "where do we handle authentication failures?" and it searches embeddings across your stored patterns + code knowledge. The 0.90 similarity score on the TypeScript error handling
   query (vs 0.57 for unrelated patterns) shows this actually discriminates well.

Why it's compelling for you specifically

• You work across multiple repos (reap-monorepo, claude-bot, validate, etc.) — the memory persists and searches semantically, so patterns from one project inform another
• You have named browser sessions already set up — the browser tools integrate directly, so the guardian can verify UI state post-deploy
• The PII scanner would have immediately flagged the passwords sitting in your global CLAUDE.md
• The trajectory system means the more you code with Claude, the better it gets at routing to known-good patterns instead of reinventing solutions

What I'd build

A lightweight TypeScript project right here in /src with:

• A hooks integration that auto-scans commits and stores patterns
• A CLI command to query "what pattern should I use for X?"
• Pre-commit PII/secret detection
• A memory seeding script that ingests your existing CLAUDE.md conventions as baseline patterns

Want me to build it?