0e9f8814a5
* refactor(mcp): extract buildToolDefs helper for subagent tool registry reuse
The inline operations.map(...) block in src/mcp/server.ts became the only
source of truth for agent-facing tool definitions. Extract into a reusable
exported helper so the v0.15 subagent tool registry can call it with a
filtered OPERATIONS subset instead of duplicating the shape.
Byte-for-byte equivalence regression pinned in test/mcp-tool-defs.test.ts —
legacy inline mapping kept verbatim inside the test so any future drift
between the new helper and the pre-extraction MCP schema fails loudly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(operations): subagent-aware OperationContext + put_page namespace
Adds three optional fields to OperationContext:
- jobId?: number — the currently running Minion job id
- subagentId?: number — the owning subagent job id for tool-dispatched calls
- viaSubagent?: boolean — FAIL-CLOSED flag for agent-path gating
put_page now enforces a namespace rule when invoked on the subagent tool
dispatch path (viaSubagent=true): writes MUST target
`wiki/agents/<subagentId>/...`. Anchored, slash-boundary enforced so a
collision like `wiki/agents/12evil/...` can't impersonate subagent 12.
The check runs BEFORE the dry-run short-circuit so preview calls surface
the same rejection. Fail-closed: a missing subagentId with viaSubagent=true
rejects every slug rather than letting a dispatcher bug open a hole.
Existing callers unaffected — all three fields are optional and the legacy
put_page behavior is unchanged when viaSubagent is undefined/false.
12 regression + namespace tests pin:
- local CLI writes (viaSubagent unset) accept arbitrary slugs
- MCP writes (remote=true, viaSubagent unset) accept arbitrary slugs
- subagent-path: anchored prefix accepted, wrong id rejected, prefix-
collision defeated, leading-slash rejected, bare-prefix rejected,
fail-closed on missing/NaN subagentId, permission_denied code emitted
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(schema): v0.15.0 subagent runtime tables + migration orchestrator
Adds three new tables for the durable LLM agent runtime:
subagent_messages — Anthropic message-block persistence.
Parallel tool_use blocks in one assistant
message live in content_blocks JSONB, not
across rows (fixes the (job_id, turn_idx, role)
misdesign codex caught in v0.13 drafting).
subagent_tool_executions — Two-phase tool ledger. INSERT pending before
execute, UPDATE complete/failed after. Replay
re-runs pending rows only if the tool is
idempotent (v1 ships only idempotent tools so
this is preventive).
subagent_rate_leases — Lease-based concurrency cap for outbound
providers (e.g. anthropic:messages). Stale
leases auto-prune on next acquire so crashed
workers can't strand capacity.
All DDL uses CREATE TABLE/INDEX IF NOT EXISTS — order-independent vs
PR #244's initSchema() reorder, and idempotent across fresh-install +
upgrade paths. Shipped in both src/schema.sql (Postgres) and
src/core/pglite-schema.ts (PGLite); schema-embedded.ts regenerated.
Migration orchestrator v0_15_0.ts (phases: schema → verify → record).
v0_14_0.ts is a no-op stub so the registry's version sequence stays
gapless (v0.14.0 shipped shell-jobs — code change, no DB migration).
10 unit tests for registry wiring, ordering, dry-run phase behavior, and
schema-embedded table presence. test/apply-migrations.test.ts updated for
the two new registry entries.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): emit child_done on every terminal + max_stalled per-job + terminal set fix
Three correctness fixes the v0.15 subagent aggregator spine depends on:
1. child_done emission on ALL terminal transitions, not just success.
- completeJob already emitted on success — now also tags outcome='complete'.
- failJob newly emits on terminal 'failed' or 'dead' (outcome='failed'|'dead',
error=<text>), BEFORE the parent-terminal UPDATE so the EXISTS guard on
the inbox INSERT doesn't skip it on fail_parent paths (codex catch).
- cancelJob now emits outcome='cancelled' per descendant with a parent.
- handleTimeouts now emits outcome='timeout' per timed-out child.
ChildDoneMessage gains optional { outcome, error } — backwards compatible
(legacy writers omitted them; consumers treat absent outcome as 'complete').
2. Parent-resolution terminal set now includes 'failed'.
Pre-v0.15 the `NOT EXISTS (... status NOT IN ('completed','dead','cancelled'))`
guard treated a failed child as still-pending, stranding aggregator parents
that chose on_child_fail='continue' or 'ignore' in waiting-children forever.
Expanded to {completed, failed, dead, cancelled} everywhere parent resolution
reads child status (completeJob inline, failJob remove_dep + continue,
cancelJob sweep, handleTimeouts sweep, and the resolveParent method itself).
3. MinionJobInput.max_stalled threads through MinionQueue.add() on INSERT.
Column exists with default 1 — that is "first stall → dead", which defeats
crash recovery for long-running handlers. Subagent children will set
max_stalled: 3 to survive mid-run worker kills. Second-submitter under an
idempotency-key hit does NOT mutate the existing row (codex-flagged
footgun — first-submit options are load-bearing state).
13 unit tests pin: emission on each of completeJob/failJob/cancelJob/
handleTimeouts, insertion order on fail_parent, terminal-set expansion with
continue policy, max_stalled default + override + idempotency behavior.
E2E tier 1 (Postgres) passes 141 tests unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): rate-leases + waitForCompletion infra for v0.15 subagent
Two infrastructure modules the subagent handler spine depends on:
rate-leases.ts — lease-based concurrency cap for outbound providers
(anthropic:messages, openai:*, etc.). Counter-based limiters leak capacity
on worker crash; leases are owner-tagged rows with expires_at that
auto-prune on the next acquire. Two-phase: txn-scoped pg_advisory_xact_lock
guards the check-then-insert so concurrent acquires can't both win the
"last slot". renewLeaseWithBackoff retries 3x (250/500/1000ms) for mid-
call DB blips — on persistent failure the LLM-loop caller aborts with a
renewable error so the worker re-claims and the rate invariant is
preserved. Owner FK cascades clean up leases on job deletion.
wait-for-completion.ts — poll-until-terminal helper for CLI callers.
Minions' NOTIFY is worker-side only; `gbrain agent run --follow` polls
getJob() until status is {completed, failed, dead, cancelled}. TimeoutError
carries jobId + elapsedMs and does NOT cancel the job — the user can
inspect via `gbrain jobs get <id>` later. Supports AbortSignal for Ctrl-C
without throwing. Default pollMs is 1000 on Postgres, 250 on PGLite (inline
CLI has no network RTT).
21 unit tests cover: single/multi acquire under cap, rejection past cap,
release frees slot, different keys are independent, stale prune, cascade
on owner delete, renew bumps expires_at, renew on missing is false,
backoff path success + pruned short-circuit. waitForCompletion: fast-path
terminal, transitions mid-wait (completed/failed/cancelled), TimeoutError
shape, abort-signal early exit, non-existent job error.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): subagent ToolDef types + brain-tool registry (v0.15)
Types first so the handler has a stable contract:
- SubagentHandlerData / AggregatorHandlerData — the two job.data shapes
- ToolCtx (engine, jobId, remote, signal) + ToolDef (name, description,
input_schema, idempotent, execute) — Anthropic-envelope, distinct from
the MCP McpToolDef extraction landed earlier
- ContentBlock discriminated union for subagent_messages.content_blocks
- SubagentStopReason + SubagentResult emitted on terminal completion
brain-allowlist.ts derives one ToolDef per allow-listed OPERATION. Reuses
the ParamDef → JSONSchema shape from the MCP extraction in a local helper
(Anthropic's input_schema field diverges from MCP's inputSchema by a
character). The 11-name allow-list is read-safe + put_page — every
destructive / filesystem / identity-mutating op stays off by default.
put_page gets a namespace-wrapped tool schema: `slug` pattern = anchored
`^wiki/agents/<subagentId>/.+`. The server-side check in put_page op
(shipped in prior commit) is still the authoritative gate — the schema
just helps the model write correct slugs first-try. `subagentId` is
plumbed into the ToolCtx so the viaSubagent=true fail-closed path lights
up on every tool-dispatched put_page.
filterAllowedTools narrows a registry by subagent_def's allowed_tools
frontmatter field. Rejects unknown names at load time (no silent drop —
typos in a skills/subagents/*.md would otherwise ship to prod with a
tool silently missing).
18 tests pin: every allowlist name exists in OPERATIONS (catches upstream
rename), Anthropic name regex, put_page namespace pattern per-subagent,
execute() routes through the op handler with viaSubagent=true, out-of-
namespace put_page throws permission_denied, filter passes prefixed +
unprefixed names, rejects unknowns, deduplicates.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): subagent-audit JSONL + transcript renderer
Two small plumbing pieces the v0.15 subagent handler + `gbrain agent logs`
depend on:
subagent-audit.ts — JSONL-rotated audit log mirroring the shell-audit
pattern. Two event flavors: submission (one line per job submit) and
heartbeat (one line per turn boundary — llm_call_started / completed /
tool_called / tool_result / tool_failed). Heartbeats fix the "--follow on
a long Anthropic call shows nothing for 30 seconds" problem codex flagged.
Never logs prompts or tool inputs (PII risk — subagent input_vars may
carry user-supplied free text); DOES log tokens, ms_elapsed, tool_name,
first 200 chars of error text. Rotates weekly via ISO week. `readSubagent
AuditForJob` is the readback path for `gbrain agent logs` — scans the
current + prior week file so job boundaries across weeks still resolve.
`GBRAIN_AUDIT_DIR` overrides the default ~/.gbrain/audit/ for container
deploys.
transcript.ts — renders subagent_messages + subagent_tool_executions to
markdown. Message order is authoritative; tool rows splice under their
owning assistant tool_use by tool_use_id. Handles text, tool_use (with
pending / complete / failed execution rows), tool_result (skipped if
we already rendered the owning tool_use — avoids double-printing), and
unknown block types (fenced JSON dump for diagnostics). Output is
UTF-8-safe truncated at maxOutputBytes.
21 unit tests: ISO week filename rotation (incl. 2027-01-01 → W53-2026
boundary), submission + heartbeat write shapes, 200-char error cap, best-
effort write failure doesn't throw, readback filters by job_id and
sinceIso. Transcript: empty input, ordering, token line, tool_use +
complete/failed/pending execution rendering, truncation, unknown-block
diagnostic dump.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): subagent LLM-loop handler with crash-resumable replay
The main event: runs one Anthropic Messages API conversation with tool
use, persists every turn + tool execution, and resumes cleanly after a
worker kill anywhere in the loop.
Design points that carry the v0.15 guarantees:
1. Two-phase tool persistence. INSERT status='pending' before dispatch,
UPDATE to 'complete' or 'failed' after. subagent_messages rows are
the canonical conversation; subagent_tool_executions rows are the
canonical "did this tool run + what did it return". Either DB commit
is atomic, so replay has a single source of truth.
2. Replay reconciliation. If the last persisted message is an assistant
with tool_use blocks AND no following synthesized user message, we
crashed mid-dispatch. On resume, finish those tools first (respecting
idempotent flag for 'pending' rows), synthesize the user turn, and
THEN call the LLM again. Non-idempotent pending rows abort the job
with a clear error — v0.15 ships only idempotent tools so this is
preventive.
3. Rate lease around every LLM call. acquireLease before, releaseLease
after (both success and error paths). acquired=false throws
RateLeaseUnavailableError — the worker treats it as a renewable
error and re-claims later, so a temporary capacity cap doesn't fail
the job terminally.
4. Anthropic prompt caching. system block gets cache_control=ephemeral;
the LAST tool def gets it too (Anthropic caches everything up to and
including the marked block). ~10x cost reduction on multi-turn
agents per the plan.
5. Dual-signal abort. AbortSignal.any merges ctx.signal (timeout / lock
loss / cancel) with ctx.shutdownSignal (worker SIGTERM). Both feed
the Anthropic call's AbortSignal; mid-turn abort bails before the
next LLM call with whatever turns are already persisted. Node ≥ 20
has AbortSignal.any; older runtimes get a manual-merge polyfill.
6. Injectable Anthropic client. The real SDK implements MessagesClient
structurally; tests inject a FakeMessagesClient that scripts
responses.
12 unit tests pin: no-tool happy path, single tool_use complete, tool
throws → failed row + loop continues, unknown tool name rejection,
max_turns cap, crash-then-resume with partial state, replay skips already-
complete tool execs without re-invoking execute, non-idempotent pending
rejects on resume, lease acquire + release roundtrip, RateLeaseUnavailable
under cap-full, missing prompt validation, allowed_tools unknown-name.
NOT in v0.15: refusal detection (stop_reason + content shape), stop_reason
=max_tokens partial recovery, mid-call lease renewal with backoff loop.
All three are documented as P2 items in the plan file.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): subagent_aggregator handler with mixed-outcome rendering
Claims AFTER all subagent children resolve — by then Lane 1B's queue
changes have posted one child_done message per terminal transition into
this job's inbox (complete / failed / dead / cancelled / timeout). The
aggregator reads those, builds a deterministic markdown summary, and
returns it as the handler result.
Not an LLM call in v0.15 — output is reproducible concatenation so
fan-out runs stay comparable. v0.16+ can add an LLM synthesis pass
behind an opt-in flag.
Contract:
- empty children_ids → `(no children)` marker
- missing child_done (shouldn't happen under v0.15 invariants but
possible if a terminal-state path slipped past Lane 1B) → counted as
failed with "no child_done message observed" error
- non-complete outcomes: result is null in the output so no payload
leaks alongside a failure label
- children appear in the order children_ids was supplied
- custom aggregate_prompt_template replaces the markdown header
13 unit tests cover: empty input, all-success, mixed outcomes, result
suppression on failure, missing child_done handling, order preservation,
custom template, progress + log emission, stringified JSONB payload
parsing, non-child_done inbox filtering, legacy-writer outcome fallback,
and internal helper edges.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): GBRAIN_PLUGIN_PATH loader + plugin-authors guide (v0.15)
Plumbing that makes Wintermute (and future downstream agents) day-1
usable on v0.15. Host repos drop a `gbrain.plugin.json` + `subagents/`
directory somewhere, set GBRAIN_PLUGIN_PATH (colon-separated like \$PATH),
and their custom subagent defs load at worker startup.
Path policy is strict: absolute paths only. Relative, ~-prefixed, and
URL-style (https://, file://) all rejected with warnings — the user
controls where plugins live. Non-existent paths and files (not dirs) are
warned and skipped so a typo doesn't crash worker startup.
Collision policy: left-wins. If two plugins ship a subagent with the same
name, the first one in GBRAIN_PLUGIN_PATH keeps it and the other gets a
warning naming both sources. Deterministic + debuggable.
Trust policy: plugins ship subagent defs ONLY. Cannot declare new tools,
cannot extend the brain allow-list, cannot override safety flags. The
subagent def's `allowed_tools:` frontmatter MUST subset the derived
registry — validation happens at load time (worker startup), not at
dispatch time, so a typo in a skill gives a loud startup error instead
of silently "tool never fires at 3am."
Manifest `plugin_version: "gbrain-plugin-v1"` locks the contract. Unknown
versions rejected. `subagents` field escape attempts (`../../../etc` etc)
rejected. gray-matter handles the markdown frontmatter parse — subagent
defs don't conform to the page schema, so we don't use parseMarkdown.
docs/guides/plugin-authors.md is the Wintermute-facing walkthrough.
Covers the minimum viable plugin shape, the three policies, the
frontmatter fields, known caveats (audit JSONL is local-only, tool calls
always run remote=true, put_page is namespace-scoped).
22 unit tests pin path rejection, missing/invalid manifest, unsupported
version, escape-attempt, basename fallback for missing frontmatter.name,
allowed_tools round-trip, unknown-tool rejection with validAgentToolNames,
empty env, multi-path, collision warning with left-wins, trimmed paths,
manifest-rejection as warning.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(cli): gbrain agent run + logs + worker registration (v0.15 Lane 4H)
Three integration seams wired:
src/commands/agent.ts — \`gbrain agent run\`. Submits subagent jobs (or a
fan-out of N + aggregator) under the trusted-submit flag so the
PROTECTED_JOB_NAMES guard doesn't reject. Fan-out path creates the
aggregator first (so children can reference its id as parent), submits
each child with on_child_fail='continue' (required by Lane 1B's terminal-
set + child_done machinery), then jsonb_set's the aggregator's
children_ids. Short-circuits a 1-entry manifest to a single subagent
with no aggregator. Follow mode runs agent-logs streaming + waitFor
Completion in parallel and exits on terminal status; detach prints the
job id and exits. Ctrl-C is handled as detach, not cancel — the job
keeps running, consistent with durability invariants.
src/commands/agent-logs.ts — \`gbrain agent logs\`. Merges ~/.gbrain/audit/
subagent-jobs-*.jsonl (heartbeats + submissions) with subagent_messages
(persisted conversation) in one chronological stream. --follow polls at
1s and exits when the job hits terminal. --since accepts ISO-8601 OR
relative shorthand (5m / 1h / 2d). Writes transcript tail (full message
+ tool tree) only for terminal jobs, so mid-run --follow doesn't spam a
half-rendered transcript.
src/commands/jobs.ts registerBuiltinHandlers — matches the shell-handler
opt-in shape. GBRAIN_ALLOW_LLM_JOBS=1 registers the subagent +
subagent_aggregator handlers, then loads plugins from GBRAIN_PLUGIN_PATH
with validAgentToolNames pulled from BRAIN_TOOL_ALLOWLIST. Every plugin
warning + loaded-plugin line prints to stderr, mirroring the openclaw-
seam startup convention.
src/core/minions/protected-names.ts — subagent + subagent_aggregator
join the protected set. MCP submit_job returns permission_denied; only
trusted-CLI callers (with allowProtectedSubmit) can insert these rows.
src/cli.ts — adds 'agent' to CLI_ONLY + dispatches it like 'jobs'.
Test fallout: subagent-handler.test.ts + subagent-transcript.test.ts
helpers now submit under allowProtectedSubmit (they insert rows named
'subagent' directly against the queue). 23 new tests in agent-cli.test.ts
cover: flag parsing (including --detach implies !follow, --tools comma
split, -- terminator, unknown flag throw), --since parse (ISO, relative
5m/2h/1d, unparseable error), protected-name guard for all three names,
trusted-submit gate, and a fan-out integration check that verifies the
aggregator + children shape after --fanout-manifest.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(e2e): rename max_children test's spawned jobs off the protected 'subagent' name
The spawn-storm test submitted 50 literal-string 'subagent' children to
exercise the max_children row-lock serialization. In v0.15 'subagent' is
a PROTECTED_JOB_NAME (CLI-only; trusted submit required), so the old
literal submission now throws before reaching the row-lock check.
The test is about max_children semantics, not the v0.15 subagent runtime
specifically — rename the child name to 'child_worker' so the test
exercises the exact same queue.add path without tripping the new guard.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(ship): v0.15.0 — VERSION, CHANGELOG, README, upgrading-agents, CLAUDE.md
Bumps VERSION → 0.15.0 and package.json → 0.15.0 (resolves the pre-existing
drift — on master, VERSION=0.14.0 but package.json=0.13.1; src/version.ts
reads package.json, so this is what the binary prints now).
CHANGELOG lands the release-summary entry in the GStack voice + the full
itemized change list (11 new modules, 3 new tables, queue correctness
fixes, trust-model additions, 159 new unit tests). Voice rules respected
— no em dashes, no AI vocabulary, real file names + real numbers.
README gets a "Durable agents: `gbrain agent` (v0.15)" section next to
the Minions block, with the three canonical CLI shapes (single run,
fanout-manifest, logs --follow) and a pointer to plugin-authors.md.
docs/UPGRADING_DOWNSTREAM_AGENTS.md gets a full v0.15.0 section covering
the four adoption steps downstream agents (Wintermute and similar) need:
(1) worker opt-in via GBRAIN_ALLOW_LLM_JOBS, (2) moving custom subagent
defs to a plugin repo, (3) replacing ephemeral subagent runs with durable
`gbrain agent run`, (4) the put_page namespace rule for agent-driven writes.
CLAUDE.md updated with concise per-file descriptions for every new module:
the handler, aggregator, audit, rate-leases, wait-for-completion,
transcript, plugin-loader, brain-allowlist, tool-defs extraction, agent
CLI + logs CLI, and the registerBuiltinHandlers wiring for subagent
handlers + plugin-loader.
Verified: binary builds (940 modules, 89ms compile), prints `gbrain 0.15.0`,
`gbrain agent --help` shows the new subcommand shape. 170 new tests pass
(full v0.15 surface). Full unit suite passes bar one parallel-load
flake on a pre-existing E2E (graph-quality, passes in isolation).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(minions): drop GBRAIN_ALLOW_LLM_JOBS flag — subagent handlers always-on
The env flag was ceremony. Shell jobs need the flag because they execute
arbitrary CLI commands (RCE surface). Subagent jobs don't — they call the
Anthropic API with whatever ANTHROPIC_API_KEY is in env, so the key is
already the cost gate (no key → SDK fails on the first turn). And
who-can-submit is already protected by PROTECTED_JOB_NAMES +
TrustedSubmitOpts: MCP callers get permission_denied; only `gbrain agent
run` with allowProtectedSubmit can insert subagent / subagent_aggregator
rows. The flag added nothing the existing guards didn't already give us.
registerBuiltinHandlers now always registers subagent + subagent_aggregator
and loads GBRAIN_PLUGIN_PATH plugins. Worker startup prints:
[minion worker] subagent handlers enabled
instead of the conditional enabled/disabled pair. Plugin discovery runs
unconditionally — empty PATH is a no-op.
README, CHANGELOG, docs/UPGRADING_DOWNSTREAM_AGENTS, CLAUDE.md, agent CLI
help text, and subagent handler docstring all updated to drop the flag
reference. Shell handler's GBRAIN_ALLOW_SHELL_JOBS gate is untouched —
separate concern (RCE, not billing).
Full suite: 1859 pass, 0 fail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: scrub private agent-fork name from all public artifacts
Enforces the rule added to CLAUDE.md (privacy section): never say
`Wintermute` in any CHANGELOG, README, doc, PR, or commit message.
Reader-facing copy says `your OpenClaw` (the term covers every
downstream OpenClaw deployment — Wintermute, Hermes, AlphaClaw — in
one umbrella the reader already recognizes). First-person /
origin-story copy says `Garry's OpenClaw` (honest that this is the
production deployment driving the feature, without exposing the
private agent's name).
Swept across:
CHANGELOG.md (v0.15 entry + 4 historical mentions)
README.md
TODOS.md
docs/UPGRADING_DOWNSTREAM_AGENTS.md
docs/guides/plugin-authors.md (including example plugin names)
docs/guides/plugin-handlers.md
docs/guides/minions-fix.md
docs/designs/KNOWLEDGE_RUNTIME.md (27 refs, mostly analytical)
docs/benchmarks/2026-04-18-minions-vs-openclaw-production.md
skills/migrations/v0.11.0.md
skills/skillpack-check/SKILL.md
scripts/skillify-check.ts
src/commands/doctor.ts
src/commands/migrations/v0_15_0.ts
src/commands/skillpack-check.ts
src/core/enrichment/completeness.ts
src/core/minions/plugin-loader.ts
src/core/operations.ts
src/core/output/scaffold.ts
Intentionally kept (these mentions define/test the rule itself):
CLAUDE.md — the privacy rule section necessarily uses the literal
name to define the restriction and examples
test/plugin-loader.test.ts — fixture name in a plugin-loading test;
renaming risks breaking assertion logic
test/integrations.test.ts — the word appears in a privacy-regex
test that explicitly enforces name redaction
test/doctor-minions-check.test.ts — a comment referencing the rule
CEO plan artifact at ~/.gstack/projects/… — private, not distributed
Binary builds (941 modules), 198/198 relevant tests pass, `gbrain --version`
prints `0.15.0`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: gitignore bun --compile artifacts with a glob, not specific hashes
Each `bun build --compile` emits a fresh hash-named `.*-*.bun-build` file
in cwd. The prior entries listed two specific hashes that were already
stale, so every build after those created a new untracked file requiring
manual cleanup.
Replace the two stale entries with `*.bun-build` so any current or future
compile artifact is ignored automatically.
Verified: ran `bun build --compile`, got two new `.*-*.bun-build` files,
`git status` stays clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(ship): rename v0.15.0 → v0.16.0
gbrain master is at 0.14.2. Other 0.15.x PRs may land before/after
this one — we bump the minor (new capability) and lock to 0.16.0 so
ordering with concurrent work doesn't matter.
Touches:
- VERSION: 0.15.0 → 0.16.0
- package.json: 0.15.0 → 0.16.0
- Rename src/commands/migrations/v0_15_0.ts → v0_16_0.ts (+ all
version strings inside + import in index.ts registry)
- Rename test/migrations-v0_15_0.test.ts → migrations-v0_16_0.test.ts
- test/apply-migrations.test.ts: skippedFuture lists now reference
'0.16.0'
- test/put-page-namespace.test.ts + test/mcp-tool-defs.test.ts: Lane
comment refs updated
- src/schema.sql + src/core/pglite-schema.ts: "v0.15.0" section
comment updated; src/core/schema-embedded.ts regenerated
- CHANGELOG.md: top entry renamed to [0.16.0]; inline v0_15_0 /
v0.15.0 refs swept
- docs/UPGRADING_DOWNSTREAM_AGENTS.md: section heading v0.15.0 → v0.16.0
Verified: `gbrain --version` prints 0.16.0, migration registry /
buildPlan / put_page / mcp-tool-defs / handlers tests all green
(49/49).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* docs: reframe v0.16 durability headline around OpenClaw crashes
"Laptop closed mid-run" framing implied a consumer workflow. Real pain is
OpenClaw subagents dying daily on worker kill, memory blip, or timeout.
Headline + README copy match the body now.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore: regenerate llms-full.txt after README copy change
Regen drift guard caught the README edit from 83beec4.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
27 KiB
TODOS
P1 (BrainBench v1.1 — categories deferred from PR #188)
BrainBench Cat 5: Source Attribution / Provenance
What: Eval that gbrain correctly cites the right page when claiming fact F, and resolves source-conflict cases (3 sources disagree on $5M raise — which wins?). 200 queries across citation/provenance/conflict sub-categories on a 300-entity dataset with deliberately-conflicting sources.
Why deferred from PR #188: Needs ~$100-200 of Opus tokens to generate the conflict-graph dataset. v1 scope was procedural-only.
Threshold: citation_recall > 90%, citation_precision > 85%, conflict_resolution > 70%.
Depends on: Identity Resolution (Cat 3) shipped — uses same world generator pattern.
BrainBench Cat 6: Auto-link Precision under Prose (at scale)
What: Cat 10 (Robustness/Adversarial) covered code-fence leak and false-positive substrings on 22 hand-crafted cases. v1.1 extends this to 500+ prose-heavy pages with realistic narrative noise. Tests link precision in the wild, not just edge cases.
Why deferred from PR #188: Needs prose-heavy generated corpus (~$100-150 Opus). Existing 22-case eval already caught + fixed the code-fence leak bug.
Threshold: link_precision > 95% on prose, type_accuracy > 80% on varied phrasing.
BrainBench Cat 8: Skill Behavior Compliance
What: Replays 100 inbound signals through a real LLM agent loop with gbrain skills loaded. Measures: brain-first lookup compliance, back-link iron-law adherence, citation format compliance, tier escalation correctness.
Why deferred: Needs real LLM API loop (~$2K total — most expensive single category).
Threshold: brain_first_compliance > 95%, back_link_compliance > 90%, citation_format > 95%.
BrainBench Cat 9: End-to-End Workflows
What: 50 end-to-end scenarios across meeting ingestion, email-to-brain, daily-task-prep, briefing generation, sync cycle. Rubric-graded (10-15 criteria each).
Why deferred: Needs LLM agent loop (~$1K). Plus 50 hand-built rubrics.
Threshold: 80% scenario pass rate per workflow.
BrainBench Cat 11: Multi-modal Ingestion
What: PDF/image/audio/video ingestion accuracy. 50 PDFs, 30 images, 20 audio files, 10 videos, 30 HTML pages. Per-modality recall and fidelity metrics.
Why deferred: Needs licensed real datasets (Common Voice for audio etc.). Dataset curation is the bulk of the work.
Threshold: PDF text fidelity > 95% (text-based) / > 80% (scanned), audio WER < 15%, entity_recall > 80% post-ingestion.
BrainBench Cat 1+2 at full scale
What: Existing benchmark-search-quality.ts (29 pages, 20 queries) and benchmark-graph-quality.ts (80 pages, 5 queries) currently pass at small scale. v1.1 extends both to 2-3K rich-prose pages generated via Opus to surface scale-dependent failures (tied keyword clusters, hub-node fan-out, prose-noise extraction precision).
Why deferred from PR #188: Needs ~$200-300 of Opus tokens for the rich corpus. The 80-page version already proves algorithmic correctness; scale-up proves it survives real-world load.
Threshold: maintain v1 metrics at 30x scale.
v0.10.4: inferLinkType prose precision fix
Shipped in PR #188. BrainBench Cat 2 rich-corpus type accuracy went from 70.7% → 88.5%. Fix: widened verb regexes (added "led the seed/Series A", "early investor", "invests in", "portfolio company", etc.), tightened ADVISES_RE to require explicit advisor rooting (generic "board member" matches investors too), widened context window 80→240 chars, added person-page role prior (partner-bio language → invested_in for outbound company refs only). Per-type after fix: invested_in 91.7% (was 0%), mentions 100%, attended 100%. works_at 58% and advises 41% are next iteration's residuals.
v0.10.5: inferLinkType residuals (works_at, advises)
What: After the v0.10.4 fix, two link types still under-perform on rich prose. Drive these to >85% type accuracy in next iteration.
works_at: 58% type accuracy. Engineer/employee pages use varied phrasings the regex doesn't catch ("spent some time at", "joined the team", narrative "is currently at" without a verb). Approach: extend WORKS_AT_RE; consider employee-role page prior similar to partner prior.
advises: 41% type accuracy. Advisor pages often describe board roles without using the word "advisor" explicitly ("on Beta Health's board", "joined Beta as a board member"). The v0.10.4 fix tightened ADVISES_RE to require "advisor" rooting to avoid false positives from investors. Need a tighter signal that distinguishes "advisor on board" from "investor on board" — likely an advisor-role page prior plus verb-pattern combinations.
Threshold: Cat 2 rich-prose type accuracy > 92% (currently 88.5%).
v0.10.4: gbrain alias resolution feature (driven by Cat 3)
What: Add an alias table to gbrain so "Sarah Chen" / "S. Chen" / "@schen" / "sarah.chen@example.com" resolve to one canonical entity. Schema: aliases (id, slug, alias_text) with a unique index. Search blends alias matches into hybrid scoring.
Why: BrainBench Cat 3 measured 31% recall on undocumented aliases — that's the v0.10.x baseline. With alias table, should jump to 80%+.
Depends on: Cat 3 baseline (shipped in PR #188).
P1
Minions shell jobs — Phase 2 scheduling (deferred from v0.13.0)
What: minion_schedules table + autopilot-cycle scanner that submits due shell jobs.
Why: v0.13.0 moves shell scripts to Minions but still leaves scheduling in the host crontab. Your OpenClaw's scripts/service-manager.sh + crontab is the only piece left on the host side. A DB-driven scheduler would mean a single gbrain autopilot --install replaces the host crontab entirely, scheduling is visible via gbrain jobs list --scheduled, and downtime-on-one-machine tolerance improves (schedule is shared DB state, not per-host crontab).
Pros: Canonical host-agnostic deployment. No more host-specific crontab.
Cons: Cross-engine migration complexity (new table on both PGLite + Postgres). Autopilot-cycle scanner needs to handle missed-schedule semantics (fire-once-on-startup or skip-if-past-now), and this is where every other cron-like system has historically accrued bugs.
Depends on: v0.13.0 shell jobs shipped. ✅
gbrain crontab-to-minions <file> migration helper (deferred from v0.13.0)
What: Parse an existing crontab file, emit a proposed rewrite using gbrain jobs submit shell ... for each deterministic entry, keep LLM-requiring entries as-is.
Why: Hand-rewriting ~14 OpenClaw cron entries is error-prone and one-shot. A helper would make the migration reversible and auditable (diff the before/after crontab, dry-run the first N, commit).
Pros: Removes the "rewrite 14 lines by hand" tax every agent operator pays on adoption.
Cons: Crontab parsing is historically fiddly (5-field vs 6-field, @hourly aliases, Vixie extensions, env vars in crontab). Could misrewrite entries with shell substitution.
Depends on: v0.13.0 shell jobs shipped. ✅
Batch the DB-source extract read path (deferred from v0.12.1)
What: extractLinksFromDB and extractTimelineFromDB at src/commands/extract.ts:447, 504 issue one engine.getPage(slug) per slug after engine.getAllSlugs(). On a 47K-page brain that's still 47K serial reads over the Supabase pooler.
Why: v0.12.1 fixed the write-side N+1 with batched INSERTs (~100x fewer round-trips). The read side still does serial getPage() calls — each fetches compiled_truth + timeline + frontmatter (tens of KB per page). On a 47K-page Supabase brain that's ~10-20 minutes of read latency before any work happens. The v0.12.0 orchestrator's backfill uses --source db, so this stays slow until fixed.
Pros: Mirrors the write-side fix on the read path. Combined with batched writes, full re-extract on a 47K-page brain should drop from "minutes" to "seconds" end-to-end. Eliminates the implicit listPages-pagination-mutation learning risk by giving you a snapshot read.
Cons: New engine method (getPagesBatch(slugs: string[]) → Promise<Page[]> or a streaming cursor) needs to land on both PGLite and Postgres. Memory budget — a 47K-page brain with ~30KB/page is ~1.4GB if loaded all at once; needs chunked iteration (e.g., 500 slugs/query, stream-process).
Context: Codex's plan-time review and the testing/performance specialists at ship time both flagged this. Filed during v0.12.1 to ship the bug fix without scope creep. Approach: add getPagesBatch(slugs) returning chunked results, then update the 4 DB-source extract paths to consume it.
Depends on: v0.12.1 ships first.
Batch embedding queue across files
What: Shared embedding queue that collects chunks from all parallel import workers and flushes to OpenAI in batches of 100, instead of each worker batching independently.
Why: With 4 workers importing files that average 5 chunks each, you get 4 concurrent OpenAI API calls with small batches (5-10 chunks). A shared queue would batch 100 chunks across workers into one API call, cutting embedding cost and latency roughly in half.
Pros: Fewer API calls (500 chunks = 5 calls instead of ~100), lower cost, faster embedding.
Cons: Adds coordination complexity: backpressure when queue is full, error attribution back to source file, worker pausing. Medium implementation effort.
Context: Deferred during eng review because per-worker embedding is simpler and the parallel workers themselves are the bigger speed win (network round-trips). Revisit after profiling real import workloads to confirm embedding is actually the bottleneck. If most imports use --no-embed, this matters less.
Implementation sketch: src/core/embedding-queue.ts with a Promise-based semaphore. Workers await queue.submit(chunks) which resolves when the queue has room. Queue flushes to OpenAI in batches of 100 with max 2-3 concurrent API calls. Track source file per chunk for error propagation.
Depends on: Part 5 (parallel import with per-worker engines) -- already shipped.
P0
Fix bun build --compile WASM embedding for PGLite
What: Submit PR to oven-sh/bun fixing WASM file embedding in bun build --compile (issue oven-sh/bun#15032).
Why: PGLite's WASM files (~3MB) can't be embedded in the compiled binary. Users who install via bun install -g gbrain are fine (WASM resolves from node_modules), but the compiled binary can't use PGLite. Jarred Sumner (Bun founder, YC W22) would likely be receptive.
Pros: Single-binary distribution includes PGLite. No sidecar files needed.
Cons: Requires understanding Bun's bundler internals. May be a large PR.
Context: Issue has been open since Nov 2024. The root cause is that bun build --compile generates virtual filesystem paths (/$bunfs/root/...) that PGLite can't resolve. Multiple users have reported this. A fix would benefit any WASM-dependent package, not just PGLite.
Depends on: PGLite engine shipping (to have a real use case for the PR).
ChatGPT MCP support (OAuth 2.1)
What: Add OAuth 2.1 with Dynamic Client Registration to the self-hosted MCP server so ChatGPT can connect.
Why: ChatGPT requires OAuth 2.1 for MCP connectors. Bearer token auth is NOT supported. This is the only major AI client that can't use GBrain remotely.
Pros: Completes the "every AI client" promise. ChatGPT has the largest user base.
Cons: OAuth 2.1 is a significant implementation: authorization endpoint, token endpoint, PKCE flow, dynamic client registration. Estimated CC: ~3-4 hours.
Context: Discovered during DX review (2026-04-10). All other clients (Claude Desktop/Code/Cowork, Perplexity) work with bearer tokens. The Edge Function deployment was removed in v0.8.0. OAuth needs to be added to the self-hosted HTTP MCP server (or gbrain serve --http when implemented).
Depends on: gbrain serve --http (not yet implemented).
Runtime MCP access control
What: Add sender identity checking to MCP operations. Brain ops return filtered data based on access tier (Full/Work/Family/None).
Why: ACCESS_POLICY.md is prompt-layer enforcement (agent reads policy before responding). A direct MCP caller can bypass it. Runtime enforcement in the MCP server is the real security boundary for multi-user and remote deployments.
Pros: Real security boundary. ACCESS_POLICY.md becomes enforceable, not advisory.
Cons: Requires adding sender_id or access_tier to OperationContext. Each mutating operation needs a permission check. Medium implementation effort.
Context: From CEO review + Codex outside voice (2026-04-13). Prompt-layer access control works in practice (same model as Garry's OpenClaw) but is not sufficient for remote MCP where direct tool calls bypass the agent's prompt.
Depends on: v0.10.0 GStackBrain skill layer (shipped).
P1 (new from v0.7.0)
Constrained health_check DSL for third-party recipes
Completed: v0.9.3 (2026-04-12). Typed DSL with 4 check types (http, env_exists, command, any_of). All 7 first-party recipes migrated. String health checks accepted with deprecation warning + metachar validation for non-embedded recipes.
P1 (new from v0.11.0 — Minions)
Per-queue rate limiting for Minions
What: Token-bucket rate limiting per queue via a new minion_rate_limits table (queue, capacity, refill_rate, tokens, updated_at), with acquire/release in claim().
Why: The #1 daily OpenClaw pain is spawn storms hitting OpenAI/Anthropic rate limits. max_children caps fan-out per parent, but a queue with 50 ready jobs will still slam the API. Every Minions consumer currently reinvents token-bucket in user code.
Pros: First-class rate limiting means no consumer has to roll their own. Composes with max_children (which is per-parent) to give two orthogonal throttles.
Cons: Adds a write hotspot on the rate-limit row. Mitigate by keeping it a simple UPDATE ... WHERE tokens > 0 RETURNING that fails fast and puts the claim back in the pool.
Effort: ~2 hours. Deferred from v0.11.0 to keep the parity PR at a reviewable size.
Depends on: Minions (shipped in v0.11.0).
Minions repeat/cron scheduler
What: BullMQ-style repeatable jobs. queue.add(name, data, { repeat: { cron: '0 * * * *' } }).
Why: Idempotency keys (shipped in v0.11.0) are the foundation. Consumers currently use launchd/cron to fire gbrain jobs submit, but a native scheduler inside the worker would be cleaner and portable across deployments.
Pros: One mental model for both immediate and scheduled work. Idempotency prevents double-fire.
Cons: Every cron library has edge cases (DST, missed intervals on worker restart). Use a battle-tested parser.
Effort: ~1 day.
Depends on: Idempotency keys (shipped in v0.11.0).
Minions worker event emitter
What: worker.on('job:completed', handler) / worker.on('job:failed', ...) instead of polling.
Why: Consumers currently poll getJob(id) to watch state changes. An event API is the ergonomic BullMQ has and Minions doesn't.
Effort: ~4 hours.
waitForChildren(parent_id, n) / collectResults(parent_id) helpers
What: Convenience wrappers over readChildCompletions for common fan-in patterns.
Why: The child_done inbox primitive shipped in v0.11.0. Now add the ergonomic API on top so orchestrators don't have to write the polling loop.
Effort: ~2 hours.
Depends on: child_done inbox primitive (shipped in v0.11.0).
P2
Minions: gbrain jobs stats --orphaned (deferred from v0.13.0)
What: New CLI flag / output column surfacing jobs that are waiting with no registered handler on any live worker.
Why: v0.13.0 adds shell jobs that require GBRAIN_ALLOW_SHELL_JOBS=1 on the worker. If an operator submits a shell job but no worker with the flag is running, the row sits in waiting silently. The CLI's starvation warning + docs help at submit time; this TODO surfaces the problem at operational-check time.
Pros: Closes the "did my cron actually run" ambiguity for multi-machine deployments.
Cons: Knowing "no worker has this handler registered" requires worker heartbeat tracking, which Minions doesn't have yet (it's stateless at DB level beyond lock_token). Could be approximated by "no jobs of this name have completed in last N minutes AND count of waiting is > 0."
Depends on: v0.13.0 shell jobs shipped. ✅
Minions: AbortReason plumbing on MinionJobContext (deferred from v0.13.0)
What: Handlers today can't distinguish whether ctx.signal.aborted fired due to timeout, cancel, or lock-loss. v0.13.0 derives this at worker-catch-time from abort.signal.reason, but the handler can't see it directly. Expose ctx.abortReason?: 'timeout' | 'cancel' | 'lock-lost' | 'shutdown' on the context.
Why: Shell handler's kill-sequence today can't decide "retry this" (lock-lost) vs "don't retry, user cancelled" (cancel) — they look the same. A typed AbortReason lets handlers make that decision for themselves.
Pros: Handlers get richer signals.
Cons: Small surface-area addition to the handler API. Not strictly required since the worker already makes the retry/dead decision for them.
Depends on: v0.13.0 shell jobs shipped. ✅
Minions: blocking-mode audit log for true forensic integrity (deferred from v0.13.0)
What: Opt-in mode for shell-audit where appendFileSync failures DO block submission instead of logging-and-continuing.
Why: v0.13.0 ships the audit log in best-effort mode, which means a disk-full attacker can silently disable the forensic trail. Acceptable for v0.13.0 because the primary use is operational ("what did this cron do last Tuesday"), not security forensics. Operators who want fail-closed semantics should have a flag.
Pros: Enables true forensic integrity for deployments that need it.
Cons: Fail-closed means a transient disk issue blocks shell submissions, which can be worse than a missing log line for most operators. Opt-in is the right shape but adds surface area.
Depends on: v0.13.0 shell jobs shipped. ✅
Minions: configurable per-job output buffer sizes (deferred from v0.13.0)
What: Add max_stdout_bytes / max_stderr_bytes to ShellJobParams; override the 64KB/16KB defaults.
Why: 64KB/16KB covers typical OpenClaw scripts today but a verbose benchmark or a debug-dump script could need more.
Depends on: First shell-job author who actually needs it. Don't pre-build the flag.
Security hardening follow-ups (deferred from security-wave-3)
What: Close remaining security gaps identified during the v0.9.4 Codex outside-voice review that didn't make the wave's in-scope cut.
Why: Wave 3 closed 5 blockers + 4 mediums. These are the known residuals. Each is an independent hardening item that becomes trivial as Runtime MCP access control (P0 above) lands.
Items (each a separate small task):
- DNS rebinding protection for HTTP health_checks. Current
isInternalUrlvalidates the hostname string; DNS resolution happens later insidefetch. A malicious DNS server can return a public IP on first lookup and an internal IP on the actual request. Fix: resolve hostname viadns.lookupbefore fetch, pin the IP with a customhttp.Agentlookupoverride, re-validate post-resolution. Alternative: usessrf-req-filterlibrary. - Extended IPv6 private-range coverage. Block
fc00::/7(Unique Local Addresses),fe80::/10(link-local),2002::/16(6to4),2001::/32(Teredo),::/128. Current code covers::1,::, and IPv4-mapped (::ffff:*) via hex hextet parsing. - IPv4 shorthand parsing.
127.1(legacy 2-octet form = 127.0.0.1),127.0.1(3-octet), mixed-radix with trailing dots. Current code handles hex/octal/decimal integer-form IPs but not these shorthand variants. - Broader operation-layer limit caps.
traverse_graphdepthparam, plusget_chunks,get_links,get_backlinks,get_timeline,get_versions,get_raw_data,resolve_slugs— all currently accept unboundedlimit/depth. Wave 3 only clampedlist_pagesandget_ingest_log. sync_brainrepo path validation. Therepoparameter accepts an arbitrary filesystem path. Same threat model asfile_uploadbefore wave 3. AddvalidateUploadPath(strict) for remote callers.file_uploadsize limit.readFileSyncloads the entire file into memory. Trivial memory-DoS from MCP. Add ~100MB cap (matches CLI's TUS routing threshold) and stream for larger files.file_uploadregular-file check. Reject directories, devices, FIFOs, Unix sockets viastat.isFile()beforereadFileSync.- Explicit confinement root (H2).
file_uploadstrict mode currently usesprocess.cwd(). Move toctx.config.upload_root(or derive from where the brain's schema lives) so MCP server cwd can't be the wrong anchor.
Effort: M total (human: ~1 day / CC: ~1-2 hrs).
Priority: P2 — deferred consciously. Wave 3 closed the easily-exploitable paths. These are the defense-in-depth follow-ups.
Depends on: Security wave 3 shipped. None are blockers for Runtime MCP access control, but all three security workstreams (this, that P0, and the health-check DSL) converge on the same zero-trust MCP goal.
Community recipe submission (gbrain integrations submit)
What: Package a user's custom integration recipe as a PR to the GBrain repo. Validates frontmatter, checks constrained DSL health_checks, creates PR with template.
Why: Turns GBrain from a single-author integration set into a community ecosystem. The recipe format IS the contribution format.
Pros: Community-driven integration library. Users build Slack-to-brain, RSS-to-brain, Discord-to-brain.
Cons: Support burden. Need constrained DSL (P1) before accepting third-party recipes. Need review process for recipe quality.
Context: From CEO review (2026-04-11). User explicitly deferred due to bandwidth constraints. Target v0.9.0.
Depends on: Constrained health_check DSL (P1) — SHIPPED in v0.9.3.
Always-on deployment recipes (Fly.io, Railway)
What: Alternative deployment recipes for voice-to-brain and future integrations that run on cloud servers instead of local + ngrok.
Why: ngrok free URLs are ephemeral (change on restart). Always-on deployment eliminates the watchdog complexity and gives a stable webhook URL.
Pros: Stable URLs, no ngrok dependency, production-grade uptime.
Cons: Costs $5-10/mo per integration. Requires cloud account.
Context: From DX review (2026-04-11). v0.7.0 ships local+ngrok as v1 deployment path.
Depends on: v0.7.0 recipe format (shipped).
gbrain serve --http + Fly.io/Railway deployment
What: Add gbrain serve --http as a thin HTTP wrapper around the stdio MCP server. Include a Dockerfile/fly.toml for cloud deployment.
Why: The Edge Function deployment was removed in v0.8.0. Remote MCP now requires a custom HTTP wrapper around gbrain serve. A built-in --http flag would make this zero-effort. Bun runs natively, no bundling seam, no 60s timeout, no cold start.
Pros: Simpler remote MCP setup. Users run gbrain serve --http behind ngrok instead of building a custom server. Supports all 30 operations remotely (including sync_brain and file_upload).
Cons: Users need ngrok ($8/mo) or a cloud host (Fly.io $5/mo, Railway $5/mo). Not zero-infra.
Context: Production deployments use a custom Hono server wrapping gbrain serve. This TODO would formalize that pattern into the CLI. ChatGPT OAuth 2.1 support depends on this.
Depends on: v0.8.0 (Edge Function removal shipped).
P2 (knowledge graph follow-ups)
Auto-link skipped writes generate redundant SQL
What: When gbrain put is called with identical content (status=skipped), runAutoLink still does a full getLinks + per-candidate addLink loop. On N identical writes of a 50-entity page that's 50N round trips.
Why: Defensive reconciliation catches drift between page text and links table, but on truly idempotent writes it's wasted work.
Pros: Lower DB load on cron-style re-syncs. Keeps put_page latency tight under bulk MCP usage.
Cons: Need to track whether links could have drifted independent of content (e.g., a target page was deleted). Conservative approach: only skip auto-link reconciliation if status=skipped AND existing links match desired set (which still requires the getLinks call).
Context: Caught in /ship adversarial review (2026-04-18). Acceptable for v0.10.3 because auto-link runs in a transaction with row locks, so amplification cost is bounded.
Effort estimate: S (CC: ~10min) Priority: P2 Depends on: Nothing.
Audit extract --source db against auto_link config flag
What: gbrain extract links --source db writes to the same links table that auto_link=false is supposed to opt out of. The two are conceptually distinct (extract is intentional batch op, auto_link is implicit on write), but a user who turned off auto_link expecting "no automatic link writes" might be surprised.
Why: Either the behavior should match (extract checks auto_link too) or the docs should explicitly state extract is a superset.
Pros: Less surprise for users who treat auto_link as a master switch.
Cons: Some users want extract to work even when auto_link is off (e.g. one-time backfill).
Context: Caught in /ship adversarial review (2026-04-18). Documenting for now.
Effort estimate: S (CC: ~10min for docs OR ~20min for code change). Priority: P2 Depends on: Nothing.
Doctor --fix polish from v0.14.1 adversarial review
What: Six deferred findings from v0.14.1 ship-time adversarial review on src/core/dry-fix.ts:
- TOCTOU between read and write.
attemptFixreads once, writes later. Concurrent editor saves silently overwritten. Fix: re-read immediately before write and compare snapshot, orO_EXCLtempfile + rename. - Fence detection misses 4-backtick and
~~~fences.isInsideCodeFenceonly catches^```$. CommonMark-legal alternates slip through. expandBulletwalk-up is dead code. Loop breaks immediately becausebaseIndentmatches the current line. Remove or make it actually walk up.- Multi-match guard too strict. Skills with the pattern in a table-of-contents AND body get
ambiguous_multiple_matchesforever. Consider: fix first, re-scan, repeat until fixed-point. - Subprocess spam.
getWorkingTreeStatusspawnsgit statusN×M times perdoctor --fix. Cache per-skill per-invocation. doctor --fix --jsonswallows the auto-fix report.printAutoFixReportreturns early onjsonOutput; agents don't see fix outcomes. Emitauto_fixas a top-level key.
Why: None are ship-blockers; all surfaced during v0.14.1 Codex adversarial review. Bundle into one follow-up PR.
Pros: Closes the adversarial findings loop. Better correctness under concurrent edits and JSON-consumer agents.
Cons: Concurrent-edit test is finicky.
Context: v0.14.1 shipped with the 4 critical fixes (shell-injection via execFileSync, no-git-backup detection, EOF newline preservation, proximity-window consistency). These six are the deferred remainder.
Effort estimate: M (CC: ~45min for all six + tests). Priority: P2 Depends on: Nothing.
Completed
Implement AWS Signature V4 for S3 storage backend
Completed: v0.6.0 (2026-04-10) — replaced with @aws-sdk/client-s3 for proper SigV4 signing.