linkedin-studio/docs/second-brain/consolidation-loop.md
Kjell Tore Guttormsen 4fa411f13c feat(linkedin-studio): SB-S3a — wire strategy-advisor as first brain/profile.md reader [skip-docs]
The second brain now feeds generation: strategy-advisor reads brain/profile.md
as evidence-to-test (anti-sycophancy default, graceful absence on fresh installs).
First end-to-end proof of capture → consolidate → read-back-into-generation.

- agents/strategy-advisor.md: brain/profile.md added to Step 0 Load Context + a
  consumption subsection (Static/Dynamic layers, evidence_count/last_seen weighting,
  anti-sycophancy counter-pressure, silent degrade when the file is absent).
- scripts/test-runner.sh: new Section 16d (Brain Profile Reader) — 2 UNCONDITIONAL
  checks (non-vacuity self-test with legacy-path/lowercase decoy + exact-literal
  wiring grep on strategy-advisor.md), ASSERT_BASELINE_FLOOR 78→80, header
  enumeration extended. Gate 93→95/0/0; brain floor 82 untouched.
- docs/second-brain/consolidation-loop.md: reconciled the stale "no reader yet"
  honest-limit to the true partial state (one reader; broader consumption deferred).

TDD: RED (only the wiring grep failed, exit 1) → GREEN (95/0/0). READ-only — the
brain consolidate --apply --confirm gate stays the sole writer. SC4 (graceful
absence) verified by inspection; SC5 (read-back) deferred to a reloaded session
(honest — agent prompts load at session start). S3a scope held: one reader, no
parser, no id-threading.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01RigJBiRFNtFZKCz21qNbQ4
2026-06-23 18:23:04 +02:00

4.5 KiB

Consolidation loop — the compounding mechanism (SB-S2)

How the second brain turns the published gold signal into an ever-improving, drift-resistant brain/profile.md — operator-invoked, operator-gated, deterministic. Part of the second-brain arc (architecture.md); landed in SB-S2.

The shape

The loop is operator-invoked ("sleep-time" = when you run it), not automatic — the session-start hook is zero-dep and cannot run AI, so it only nudges when consolidation is due. The pass itself is:

brain consolidate --gather      # 1. dump new published deltas + the current profile
   → (the session reads them and extracts a Candidate[] JSON)
brain consolidate --propose --candidates cand.json   # 2. deterministic diff → brain/pending-diff.{md,json}
   → (you review brain/pending-diff.md — the [OPERATØR] gate)
brain consolidate --apply --diff brain/pending-diff.json --confirm   # 3. the ONLY write to profile.md

--apply records brain/consolidation-state.json {last_run}; the session-start nudge reads it + counts ingest/published/*.md to know when to nudge again. Roll back any apply via git.

The deterministic engine (scripts/brain/src/consolidate.ts)

proposeDiff({current, candidates, today, opts}) classifies each candidate (matched to existing facts by the candidate's key):

Rule Condition Effect
reject provenance: ai-draft dropped — never learns from the engine's own drafts (model-collapse guard)
add no matching fact, provenance published/human new dynamic fact, evidence_count: 1
evidence-bump matching fact, same value evidence_count++, last_seen = today
promote a dynamic fact reaches N = 3 observations dynamic → static
conflict matching key, different value keep both, timestamped, with DISTINCT ids; the old fact is untouched (no supersede in S2 — that's S3)
decay-flag a dynamic fact's last_seen > 90 days listed in staleFlags (informational; never auto-removed)

Id model (no duplicate ids): a concept's primary fact id is mintEntityId({kind:'observed', key}); a conflict alt fact id is mintContentId('observed-alt:'+key+'::'+value+'::'+date) — byte-distinct, so two facts never share an id. The SB-S0 folded profile-field static seeds use a different kind, so consolidation never mutates them (immutable in S2). applyDiff produces a ProfileDoc that round-trips exactly through the SB-S0 grammar; re-running is idempotent (bump, not duplicate).

Defaults: promoteThreshold = 3, decayDays = 90 (operator-confirmed).

The candidate file — the session↔engine contract

--propose --candidates <file.json> takes a JSON array of candidates; each is validated (malformed → non-zero exit, nothing written):

[
  { "key": "primary-expertise", "value": "AI governance in the public sector",
    "provenance": "published", "source": "published:1a2b3c4d5e6f", "observed_date": "2026-05-26" }
]
  • key, value, source, observed_date — non-empty strings; provenance ∈ {human, published, ai-draft}.
  • key and value must be single-line (no newline/CR — the profile grammar is one fact per line).
  • The session produces this from --gather's output. The engine guarantees the mechanics; the quality of the candidates is the session's job (see limits).

Honest limits

  • The loop's value depends on the session's extraction. The engine only guarantees threshold/conflict/ decay/provenance mechanics. Garbage candidates → a garbage diff. The operator gate + candidate-shape validation catch shape errors, not insight quality.
  • brain/profile.md has one reader as of SB-S3a. S2 evolved the profile motor-only; SB-S3a wired the first consumer — strategy-advisor reads it as evidence-to-test (anti-sycophancy: counter-pressured, never parroted). Broader consumption (more content agents, a hook-level prompt digest) remains later S3 work, and the profile is still mutated ONLY via brain consolidate --apply --confirm.
  • No supersede / no auto-demotion. Conflicts keep both; stale facts are flagged, never auto-removed — the operator (or S3) reconciles. Conflict alt facts persist until then.
  • No AI at session-start. The nudge is a deterministic file-count + sidecar read; the consolidation pass is always operator-invoked.
  • The session-start nudge is consolidation-due only — it counts published records + days since last run; it does not parse profile.md for per-fact staleness (that cost/parser is deferred).