Garry TanandClaude Opus 4.7
c39dbdb1ec
There was no tutorial for skillopt — only a reference guide
(docs/guides/skillopt.md) that opens at --bootstrap-from-routing and
assumes you already understand benchmarks, and an agent-facing SKILL.md.
README had ZERO skillopt mention. The one thing a user must hand-author
(the benchmark JSONL) was taught nowhere with a worked example.
New: docs/tutorials/improving-skills-with-skillopt.md — Diataxis tutorial
(learning-oriented), copy-pasteable end to end:
1. mental model in two sentences (SKILL.md is the trainable param, the
agent is frozen)
2. write your first benchmark from scratch — a complete 15-task rule-judge
starter you paste and run, with the full check-op table
(contains/regex/section_present/max_chars/min_citations/tool_called/
tool_not_called)
3. --dry-run cost preview (and that it exits 2 by convention, not failure)
4. real run + reading accepted(0)/no_improvement(1)/aborted(2) with the
actual stderr output shape
5. where output lands (best.md, versions/, history.json, rejected.json,
audit jsonl)
6. accept/reject — bundled vs user skills, --no-mutate vs
--allow-mutate-bundled
7. iterate by sharpening the benchmark
The load-bearing fix the tutorial makes that the reference guide got wrong:
the DEFAULT --split 4:1:5 needs ~50 tasks before it runs (sel = N/10, floor
5). A first-time author writing 10-15 tasks hits `D_sel has N task(s)
(need >=5)` and bounces. The tutorial ships 15 tasks + `--split 1:1:1`
(clean 5/5/5) so the copy-paste path actually works. Verified against the
real loadBenchmark + splitBench: the exact shipped block parses 15 unique
tasks and splits 5/5/5 with sel>=5; the system's own error message confirms
"need ~50 total for 4:1:5".
Discoverability (Diataxis cross-linking):
- README.md tutorials section: new entry (was zero skillopt mention)
- docs/tutorials/README.md: added under ## Shipped
- docs/guides/skillopt.md: "New to this? Start with the tutorial" callout
Every claim devex-verified against source: exit-code map from
skillopt.ts (accepted:0/no_improvement:1/aborted:2/errored:2), stderr
format from skillopt.ts:286-292, check ops from score.ts, output paths
from SKILL.md, split math from benchmark.ts.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Tutorials
Step-by-step walkthroughs that take you from zero to a working outcome. Concrete commands, real numbers, no abstraction-first jargon. Each tutorial assumes no prior GBrain knowledge.
Shipped
- Set up your personal AI agent + brain from zero — the canonical solo install. Two GitHub repos, a Telegram bot, AlphaClaw on Render, OpenClaw + GBrain + Supabase. End-to-end in about 2 hours; about $100 to $150 a month sustained. The full-stack install I'd run today.
- Set up GBrain as your company brain — federated, multi-user, OAuth-scoped institutional memory for a 10-50 person team. Three sources (shared / customers / internal-only), per-user scope, first synthesized query as a teammate. About 90 minutes end-to-end, about $5 in API calls for the demo, under $100 a month sustained for a 25-person company.
- Auto-improve a skill with
gbrain skillopt— treat aSKILL.mdas the trainable parameter of a frozen agent. Write your first benchmark from scratch (the part everyone gets stuck on), preview the cost, run the optimizer, read accepted vs no_improvement vs aborted, and accept a measurably better skill. About 20 minutes, about $1 in API calls. Reference:../guides/skillopt.md.
In progress
These are the next tutorials on the roadmap. Open an issue if one of them is the one you need most; that's how we'll prioritize.
-
Connect GBrain to your existing agent — for users who already run OpenClaw, Hermes, Claude Code, Cursor, or any MCP-aware client. Wire GBrain in as the memory layer, scaffold the 43 skills, see brain-first lookup fire on the next message your agent gets.
-
Set up GBrain for VC dealflow — the operator's recipe. People pages for founders, companies with typed Facts fence carrying ARR / team-size / runway across dates, meetings auto-ingested, deal pages linking everything. Shows
gbrain whoknows,gbrain find_trajectory, andgbrain founder scorecardon real workflows. -
Migrate your existing vault into GBrain — for Notion / Obsidian / Roam users with a vault that doesn't match GBrain's default layout. Walks through
gbrain schema detect→suggest→review-candidatesso the brain learns your shape instead of forcing you to learn its. -
Index your codebase as a code brain — for developers. Initialize a brain in a code repo, swap to
voyage-code-3for embeddings, usegbrain code-def/gbrain code-refs/gbrain code-callersto navigate the codebase semantically from any MCP-aware editor. -
Run GBrain fully local with Ollama or llama.cpp — for privacy-first deployments. No cloud calls, no API keys, no telemetry. Trades some retrieval quality for full local control. Useful for regulated industries, air-gapped environments, or just paranoia.
-
Set up the dream cycle — the overnight enrichment daemon that makes the brain self-maintaining. Fixes citations, dedupes people pages, surfaces contradictions, generates founder scorecards on the schedule you configure. The piece that turns a static knowledge base into a brain that gets smarter while you sleep.
Want to write one?
Tutorials follow the Diataxis tutorial pattern: learning-oriented, walks a learner from zero to a working result in one session, every step produces a visible change. If you've used GBrain for something interesting and want to write the walkthrough, the existing company-brain.md is the model. Open a PR.
Related documentation
- Reference:
docs/architecture/— system design, topologies, retrieval theory - How-to:
docs/guides/— task-oriented runbooks (sub-agent routing, minion deployment, skill development, brain-first lookup, idea capture, diligence ingestion). Highlight: scaling skills past 300 — the three-tier architecture for agents that have outgrown the always-loaded skill manifest. - Integrations:
docs/integrations/— connecting external data sources (voice, email, calendar, embedding providers) - MCP setup:
docs/mcp/— per-client setup (Claude Desktop, Code, Cursor, ChatGPT, Perplexity, Cowork) - Install paths:
docs/INSTALL.md— every install path, end to end