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374deff579 v0.41.7.0 feat: compact list-format resolver + 300-skill scaling tutorial (#1407 )

* feat(check-resolvable): parseResolverEntries accepts compact list format

Add the second parser branch alongside the existing markdown-table branch
so RESOLVER.md and AGENTS.md can use the OpenClaw-native list shape:

    - **skill-name**: trigger1 | trigger2 | trigger3
    - skill-name: trigger1 | trigger2

Constraints:
  - Skill names must be kebab-lowercase ([a-z][a-z0-9-]+). Bold names
    starting with an uppercase letter (e.g. **Note**, **Convention**)
    are deliberately skipped so prose bullets in real-world AGENTS.md
    files don't get mis-parsed as fake skill rows.
  - skillPath is always derived as skills/<name>/SKILL.md. An optional
    arrow suffix (Unicode -> or ASCII ->) is stripped from the trigger
    string but NOT honored as a path. Downstream consumers
    (routing-eval.ts skillSlugFromPath, the manifest check at line 367)
    assume the convention. For non-conventional paths, use the table
    format.
  - Multiple triggers fan out to one entry per trigger. checkResolvable
    dedupes by skillPath downstream, so the reachability count counts
    each skill once regardless of trigger fan-out.

The parser body is restructured to an if/else-if shape so the existing
'continue' on non-table rows no longer short-circuits the list branch.

Unit tests cover 11 new cases: bold + plain name shapes, multi-trigger
fan-out, Unicode and ASCII path-suffix strip, ellipsis filter, empty
pipe segments, mixed-shape files, section tracking, and two D4
regression cases (prose-bullet rejection + convention-violation
silent-skip).

Closes #1370 — credit @garrytan-agents for the original PR that flagged
the parser gap.

* test(check-resolvable): integration fixtures + regression suite for compact format

Two fixtures pin the v0.41.7.0 parser fix at the integration layer:

  test/fixtures/openclaw-compact-resolver/
    List-format only RESOLVER.md with 10 fictional skills (gift-advisor,
    flight-tracker, email-triage, etc.), each with valid frontmatter
    triggers. A trailing 'Notes' section embeds 4 prose bullets
    (- **Note**:, - **Convention**:, - **TODO**:, - **Important**:)
    that pin the D4 kebab-lowercase regex tighten: if the regex ever
    regresses to permissive [\w-]+, those prose bullets would surface
    as orphan_trigger warnings and the test fails loudly.

  test/fixtures/openclaw-mixed-merge/
    Tests the v0.31.7 D-CX-14 multi-resolver merge: workspace-root
    AGENTS.md (compact list, 3 skills) + skills/RESOLVER.md (table
    format, 5 skills). The merge dedups by skillPath and counts each
    skill once.

The regression test (test/check-resolvable-openclaw-compact.test.ts)
runs 8 assertions across both fixtures:

  1. unreachable === 0 on the compact fixture (the 'pre-v0.41.7.0
     reported 238 FAILs on a 306-skill OpenClaw, post-fix 0' headline).
  2. zero error-severity issues; report.ok === true.
  3. zero mece_gap warnings (every stub ships valid triggers).
  4. zero orphan_trigger warnings for the 4 prose-bullet names — D4
     regex regression guard at integration level.
  5. zero missing_file warnings.
  6. mixed-merge: total_skills === 8 (5 table + 3 list), all reachable.
  7. mixed-merge: errors.length === 0; report.ok === true.
  8. mixed-merge: each expected skill from BOTH shapes is non-unreachable
     (catches the bug where one shape silently swallows the other via
     dedup-by-skillPath).

* docs(guides): scaling-skills.md walkthrough for 300-skill agents

Three-tier architecture for agents that have outgrown the always-loaded
skill manifest:

  Tier A — always loaded (~35 skills, in the system prompt every turn)
  Tier B — resolver-routed (~85 skills, looked up via RESOLVER.md/AGENTS.md
            only when no Tier A match)
  Tier C — dormant (~180 skills, on disk but not injected into the prompt)

Real numbers from Garry's 306-skill OpenClaw: 25K tokens of skill
descriptions per turn collapsed to 4K tokens (~21K tokens freed per
turn) with zero capability loss. The compact list-format resolver
(v0.41.7.0) is the parser-level enabler for this pattern.

The guide covers:

  - The scaling wall (when the always-loaded manifest stops working)
  - The three tiers + per-turn token math
  - What the resolver actually does (routing-table-but-cheaper pattern)
  - The compact list format (kebab-lowercase contract, optional path
    suffix, mixed-shape support)
  - The 'gbrain doctor' / 'gbrain check-resolvable --strict' safety net
  - Implementation walkthrough (audit → tier → disable → resolver →
    doctor)
  - The scaling curve (50 → 100 → 200 → 300 → 1000, no ceiling)

Voice + privacy cleanup applied per CLAUDE.md rules:
  - Wintermute → 'Garry's OpenClaw' / 'your OpenClaw'
  - Unicode em dashes stripped; ASCII '--' preserved in command flags
  - Made-up 'check_resolvable' invocation replaced with real
    'gbrain doctor' and 'gbrain check-resolvable --json'/'--strict'
  - Blog-style 'Previous in this series' footer dropped

Wiring:
  - scripts/llms-config.ts registers the new guide in the curated
    array so 'bun run build:llms' picks it up. docs/UPGRADING_
    DOWNSTREAM_AGENTS.md excluded from the inlined bundle to stay
    under the 600KB FULL_SIZE_BUDGET after adding the new content.
  - docs/tutorials/README.md gains a one-line entry pointing at the
    guide under Related documentation.
  - llms.txt + llms-full.txt regenerated.

* chore: bump version and changelog (v0.41.7.0)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* docs: update CLAUDE.md for v0.41.7.0 compact-format resolver

Annotate the src/core/check-resolvable.ts entry with the v0.41.7.0
parseResolverEntries compact list-format support: kebab-lowercase name
gate (closes the prose-bullet false-positive class), path-suffix strip
contract (skillPath always derived as skills/<name>/SKILL.md so
routing-eval and the manifest check don't drift), multi-trigger fan-out
plus checkResolvable downstream dedupe, the 238 FAILs to 0 OpenClaw
headline, the two integration fixtures pinning the regression, and the
docs/guides/scaling-skills.md pointer for the tutorial context.

Regenerate llms-full.txt to match (CLAUDE.md edit chaser, per the
CLAUDE.md own rule about test/build-llms.test.ts catching drift).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

2026-05-25 13:58:26 -07:00

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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.

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, and gbrain founder scorecard on 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-candidates so 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-3 for embeddings, use gbrain code-def / gbrain code-refs / gbrain code-callers to 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.

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

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Tutorials

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Want to write one?

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