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Comprehensive Improvement Plan — AI Brain Daemon Suite

Status: ✅ COMPLETE — All 12 initiatives landed, 2026-08-22. Grounded in the live codebase: 15 skills (162 scripts), 30-job daemon table, 27 tests, self-mod pipeline, agent-loop, Integrative State Layer (just landed). This plan names every gap found, ranks it by impact, and proposes concrete, checkable changes — each of which can land as sequenced commits with the full suite staying green.

Audit findings: what the codebase looks like today

Quality spread

Layer set -euo pipefail rigor Comments
core/ (executive, self-mod, signaling, locks, schema, sandbox, snapshot, agent-loop) ~100% The hardened layer — every script uses strict flags
deep-brain-kernel.py Python, 2032 lines Clean, well-documented, JOBS table is the single source of truth
skills/thalamus-memory/scripts/ (gate, decay, neuromod, broadcast, workspace) ~100% Recently hardened; Integrative State Layer is the newest code
All other 14 skills' scripts ~10% pipefail, maybe 30% -u The largest quality gap — most skills use bare set -e
Tests, harnesses, CI scripts ~80% Mostly strict, some older harnesses use set -u without -e
install.sh, uninstall.sh 100% Hardened with rollback, flock, TTY-safe

Test coverage: 27 test files, no 1:1 skill mapping

Every test is cross-module — there is no per-skill unit test suite. The "test coverage" is really a scatter-shot of integration tests that happen to exercise certain state files. The verification region (verification-memory) runs manifest-declared tests (which means: only what each skill volunteers). But 12 of 15 skills declare zero tests in their manifest.

The encode-pipeline asymmetry

Skill Has encode-pipeline.sh? Writes to shared state? Consumed by another skill?
amygdala ✅ ✅ (via log-event → update-state) gate.sh, neuromod-update.sh
vta ✅ ✅ (via log-reward → sync-motivation) neuromod-update.sh, decide.sh
anterior-cingulate ✅ ✅ (log-conflict → conflict-state) neuromod-update.sh, decide.sh
hippocampus ✅ ❓ consolidate.sh writes episodic store but no skill reads it
social ✅ ✅ neuromod-update.sh (trust)
basal-ganglia ✅ ❓ Habit encoding exists but decide.sh uses a coarse global average
acc-error-memory ✅ ✅ decide.sh, self-mod pipeline
insula ❌ ✅ (interoceptive-state.json) neuromod-update.sh, decide.sh
prefrontal-cortex ❌ goals.sh/inhibitions.sh write pfc-state gate.sh, decide.sh, workspace-refresh.sh
cerebellum ❌ ✅ decide.sh (calibration)
heartbeat ❌ ✅ neuromod-update.sh, gate.sh
thalamus N/A gate.sh + decay.sh The routing hub
executive-function N/A run-cycle.sh Scheduled; promotes goals
self-mod-runner N/A monitor-tick.sh Scheduled; auto-rollback
verification-memory N/A run-declared-tests.sh Scheduled; health check

Gap: Five skills have encode pipelines but only three actually feed their output into the shared state graph in a way another skill consumes. The hippocampus and basal-ganglia pipelines run but their outputs are siloed.

The closed-loop deficit

The brain has regions whose purpose is feedback — that's the whole point of the ACC (conflict monitoring), VTA (reward prediction error), and cerebellum (calibration). But the actual closed loops that exist today:

✅ VTA drive → neuromod dopamine → gate.sh urgency → gate scores
✅ ACC conflict → neuromod cortisol → gate.sh off-focus suppression
✅ ACC conflict → neuromod acetylcholine → gate.sh focus sharpening
✅ Social trust → neuromod oxytocin (but oxytocin is read by no consumer)
✅ Heartbeat circadian → neuromod sleepPressure → gate.sh gain floor
✅ Executive load → decide.sh load-gated option weights
✅ ACC error patterns → self-mod pipeline proposal evaluation
❌ VTA reward encoding → PFC goal completion status (no linkage)
❌ Hippocampus consolidation → PFC reflection triggers (no linkage)
❌ ACC uncertainty → VTA anticipation adjustment (no linkage)
❌ Basal-ganglia habit → decide.sh per-option habit pull (uses global average)
❌ Cerebellum calibration → self-mod pipeline deploy confidence (no linkage)
❌ Insula gutSignal → PFC inhibition strength (no linkage)

Impact: Seven high-value feedback loops that would make the brain self-correcting instead of just self-recording are missing.

Where the Roadmap stands

Milestone Status What remains
M0 (host hardening) IN PROGRESS 24h clean-run window pending
M1 (scheduled self-mod) CLOSED —
M2 (autonomy ladder) CLOSED —
M3 (inference abstraction) CLOSED Agent-loop exists but hermes is still the default SPAWN_PROVIDER
M4 (closed goal→action loop) NOT STARTED Goals are static; no outcome→lesson pipeline
M5 (outcome-driven proposals) NOT STARTED Proposals are prompt-driven, not signal-driven
M6 (new-module expansion) NOT STARTED Self-mod can only patch, not create
M7 (steward-only operation) CLOSED --autonomy reports mode; M8 wires it
M8 (autonomy mode as control) CLOSED Pipeline reads autonomy-state; auto-deploys in auto_mode

The improvement plan: 12 initiatives ranked by impact

Each initiative is scoped to land in one commit with green tests. Initiatives are sequenced so each builds on the last.

Initiative 1 — Shell hardening: set -euo pipefail everywhere

Impact: Highest. This is the #1 cause of silent failures in the codebase. Approximately 110+ scripts across 14 skills use bare set -e only. Under set -e without -u, a typo'd variable silently expands to the empty string. Under set -e without pipefail, a command that fails in a pipeline is hidden (only the last exit status counts). The BUG_AUDIT already identified several bugs that would have been caught by -u and pipefail.

Scope: ~110-120 scripts across skills/ (excluding thalamus-memory/ which is already hardened, excluding core/ which is already hardened).

Approach:

  1. Write a single scripts/add-strict-flags.sh that reads a script, detects set -e (no -u or pipefail), reports it, and offers --fix mode.
  2. Run it across every skill script, fix each by hand (this is not a mechanical-only change — unbound variables must be given defaults).
  3. Run the full test suite after each batch of fixes.
  4. Add a CI check (scripts/ci-gate.sh) that fails if any non-legacy .sh file uses set -e without -uo pipefail.

Done when: Every .sh file under skills/ and tests/ passes bash -n AND a grep for bare set -e (not followed by uo) returns zero matches outside legacy-IGNORE/.

Estimated effort: 8-12 hours (mechanical scan + hand-fix edge cases + test suite regression).

Initiative 2 — Per-skill unit tests (1 test per skill, minimum)

Impact: High. 12 of 15 skills have zero declared tests in their capability-manifest.json. The verification region's sweep is blind to them.

Scope: 12 new tests/test_<skill>_unit.sh files, each covering:

  • The skill's primary state file is created with valid schema
  • The get-state script returns the expected fields
  • One key formula produces a bounded, plausible value
  • Atomic-write hygiene (no .tmp.$$ residue)

Approach: Write a template (tests/test_skill_unit_template.sh) that reads the skill's capability-manifest.json to discover its state files and formulas. Adapt per skill.

Done when: All 15 skills have at least one passing unit test in their manifest's tests array, and verification-memory/scripts/run-declared-tests.sh reports all 15 green.

Initiative 3 — The closed-loop wiring: 7 missing feedback arcs

Impact: Highest. This is what makes the brain self-correcting instead of self-recording. Each arc is a 1-3 line change in an existing script.

Arc Change File
VTA reward → PFC goal completion In vta/encode-pipeline.sh, after reward detection, call core/executive/record-goal-outcome.sh when the reward text overlaps an active goal vta/encode-pipeline.sh
Hippocampus consolidation → PFC reflection In hippocampus/consolidate.sh, after consolidation, touch a memory/.pending-reflection marker; run-cycle.sh checks it and triggers isolated-reflect.sh hippocampus/consolidate.sh + executive-function/run-cycle.sh
ACC uncertainty → VTA anticipation In acc/encode-pipeline.sh, when uncertainty_zones detected, call vta/anticipate.sh to flag the topic as anticipated acc/encode-pipeline.sh
Basal-ganglia habit → decide.sh per-option In decide.sh python, instead of reading HABIT_STRENGTH (global average), read the habit state and match by option label (same overlap heuristic) decide.sh
Cerebellum calibration → deploy confidence In core/self-mod/evaluate-proposal.sh, add a calibration-scaled confidence range: low calibration forces wider divergence tolerances core/self-mod/evaluate-proposal.sh
Insula gutSignal → PFC inhibition In decide.sh python, when gutSignal > 0.5, apply inhibition strength to options matching inhibition patterns (not just exact label match) decide.sh
Oxytocin → social encoding bias In social/encode-pipeline.sh, oxytocin > 0.7 boosts relationship update magnitude (trust moves faster) social/encode-pipeline.sh

Done when: A new tests/test_closed_loops.sh creates fixtures for each arc, runs the trigger, and asserts the downstream state change.

Initiative 4 — Default to agent-loop (eclipse Hermes for spawn jobs)

Impact: High — directly advances Stage 2 of the VISION. M3 is "LANDED" but SPAWN_PROVIDER defaults to hermes. The agent-loop exists, passes its harness, and can reason with tools. The only remaining step is making it the default.

Approach:

  1. Change deep-brain-kernel.py's run_spawn to default to SPAWN_PROVIDER=agentloop when hermes is not explicitly set.
  2. Add a SPAWN_PROVIDER=agentloop environment entry to aibrain.service.
  3. Ensure every spawn job's task text is compatible with the agent-loop's tool registry (the tools it can call).
  4. Add a --provider flag to --check that validates the agent-loop is reachable and the tool registry matches the spawn jobs' expected tools.

Done when: SPAWN_PROVIDER is not set, a spawn job runs via agent-loop, the harnesses pass, and hermes becomes optional for the suite (still usable as a provider, but not required).

Initiative 5 — The cognitive dashboard: deep-brain-kernel.py --brain

Impact: High. The suite has no single command to show "what is the brain thinking right now?" Today you must read 15+ state files individually.

Approach: Add a --brain (or --state) mode to deep-brain-kernel.py that reads all state files and prints a single human-readable summary:

🧠 AI Brain Suite — Cognitive State at 2026-08-22T14:30:00Z
═══════════════════════════════════════════════════════
Attention:    ship the brain suite (gate score 0.55, 3 signals pending)
Goals:        2 active ("ship the brain suite" @ priority 0.8, ...)
Neuromod:     DA 0.62  NA 0.55  5-HT 0.58  ACh 0.50  CORT 0.45  OXT 0.66
              Sleep pressure 0.12  |  Phase: active
Emotion:      valence +0.15  arousal 0.60  (6 pending in queue)
Conflict:     2 unresolved conflicts (load 0.45)
Calibration:  0.72 (cerebellum)  |  Habit strength: 0.55
Social:       3 relationships (avg trust 0.62, 1 open loop)
Intero:       cognitive load 0.35  gut signal 0.10
Autonomy:     steward_mode  |  Self-mod streak: 14/20
Daemon:       30 jobs, 0 unhealthy, last PSI deferral: never

Done when: python3 deep-brain-kernel.py --brain prints the summary, --brain --json prints the structured equivalent, and tests/test_brain_dashboard.sh asserts every section renders with valid data or a clear "no data" placeholder.

Initiative 6 — Outcome-driven proposal generation (M5)

Impact: The self-mod pipeline proposes changes from LLM prompts — but the prompts are generic. The suite has rich signal data (job failures, PSI deferrals, ACC error lessons, goal gaps) that would let the LLM propose measured improvements.

Approach: Enrich generate-proposals-llm.sh's prompt context with:

  • Consecutive-failure streaks from --status output
  • ACC errorPatterns and lessons from memory/acc-state.json
  • VRAM/PSI deferral rates from memory/self-mod/live-metrics.json
  • Goal-completion rates from memory/pfc-state.json's decision log
  • Per-skill encoding pipeline success/failure stats

Done when: A fixture with a known failing hippocampus_encoding job produces a proposal whose target_paths is hippocampus-memory/scripts/encode-pipeline.sh, and tests/test_m5_proposal_linkage.sh asserts the signal→proposal→target chain.

Initiative 7 — Experience-driven knowledge extraction

Impact: The suite records episodic memories (hippocampus) and social interactions (social-memory), but never abstracts patterns — it has no semantic memory, no world model. This is the difference between "recording experience" and "learning from experience."

Approach:

  1. Add a hippocampus-memory/scripts/extract-patterns.sh (or extend consolidate.sh) that, during weekly consolidation, runs the local LLM over the week's episodic store to extract:
    • Recurring themes across conversations
    • Successful strategies (what actions preceded rewards?)
    • Failed strategies (what actions preceded errors?)
  2. Write extracted patterns into memory/semantic-state.json (new file: {themes: [...], strategies: [...], antipatterns: [...]}).
  3. decide.sh reads semantic patterns and applies a light boost to options whose labels match a "successful strategy" theme.

Done when: tests/test_semantic_extraction.sh creates a week of synthetic episodes, runs extraction, and asserts patterns appear in semantic-state.json; tests/test_decide_semantic.sh asserts the boost.

Initiative 8 — Per-skill capability-manifest completion

Impact: Medium. 10 of 15 skill manifests are placeholder stubs — they declare capabilities but no inputs, outputs, side effects, dependencies, or tests. The verification region can only test what manifests declare.

Approach: For each skill:

  1. Declare all state files it reads as inputs.
  2. Declare all state files it writes as outputs.
  3. Declare side_effects (filesystem write, LLM call, signal publish).
  4. Declare dependencies (other skills whose state it reads).
  5. Add the unit test from Initiative 2 to tests.

Done when: core/schema/validate-manifest.sh passes for all 15 skills, and every manifest has a non-empty tests array.

Initiative 9 — Skill install/uninstall isolation

Impact: Medium. The per-skill install.sh scripts are inconsistent — some exist (thalamus, heartbeat, hippocampus, amygdala, vta, acc-error, basal-ganglia, social, cerebellum, anterior-cingulate, prefrontal-cortex, verification, executive-function, self-mod-runner), but they vary in quality. The centralized install.sh handles deployment; the per-skill scripts should handle initialization only (creating state files, seeding defaults).

Approach:

  1. Standardize per-skill install.sh to a single contract: install.sh (no args) → initializes the skill's state files with defaults; install.sh --uninstall → removes the skill's state files (with confirmation).
  2. The centralized install.sh calls each skill's install.sh after deploy.
  3. uninstall.sh mirrors: calls install.sh --uninstall per skill.

Done when: install.sh --yes chains through every skill's init, and uninstall.sh --yes chains through every skill's cleanup.

Initiative 10 — Multi-agent negotiation between brain regions

Impact: High for Stage 2/3. Real brains have competing subsystems that negotiate through the basal ganglia (action selection) and PFC (executive control). The suite currently has one gate (thalamus) and one decider (PFC), with no negotiation — signals either pass the gate or they don't.

Approach:

  1. Add a basal-ganglia-memory/scripts/action-select.sh that receives the scored candidate actions from decide.sh and gate.sh's top-scored signals, applies habit bias + exploration noise, and picks the winner.
  2. The winner is what gets dispatched (replacing the current gate-only dispatch) and is also what the agent-loop acts on.
  3. The losing candidates get recorded as suppressed-with-reason in the basal-ganglia state, providing data for habit learning.

Done when: tests/test_action_selection.sh presents 3 competing actions, asserts the winner reflects habit+noise (not just max score), and the losers are recorded.

Initiative 11 — Self-mod architectural refactoring (M6 precursor)

Impact: The self-mod pipeline can patch individual files but can't refactor across files, can't create new skill directories, can't delete code. M6 (new-module expansion) needs a stronger foundation.

Approach:

  1. Extend check-target.sh to validate multi-file proposals (a list of {path, oldContent, newContent} patches instead of single target_paths).
  2. Extend apply-patch.sh to apply multi-file patches atomically (all succeed or all roll back).
  3. Add a core/self-mod/create-module.sh that takes a proposal's new_module field, generates the directory, capability-manifest.json, SKILL.md, and a skeleton script, then registers it in the registry.

Done when: tests/run_phase6_harness.sh (new Phase 6, or extended Phase 4) applies a multi-file patch and creates a new module, both under the full evaluate→deploy→monitor→rollback pipeline.

Initiative 12 — Run-it-anywhere portability

Impact: The suite is tightly coupled to Linux (systemd, cgroups, PSI, Vulkan, flock) and to Hermes. Initiative 4 addresses the Hermes dependency. The Linux dependency is harder but worthwhile for the VISION's Stage 3 (self-contained organism shouldn't be OS-locked).

Approach:

  1. Abstract systemd behind a core/daemon/process-supervisor.sh that wraps systemd OR a pure-bash process supervisor (for macOS/BSD/containers).
  2. Abstract cgroup delegation behind a no-op on non-Linux (PSI checks become ENABLED warnings, not errors).
  3. The daemon already supports --no-systemd via the install.sh skip path; make it a first-class deep-brain-kernel.py flag.

Done when: The daemon starts and runs jobs with --no-systemd --no-cgroups on macOS, and the test suite's non-Linux-dependent tests all pass.

Sequencing

Week 1-2:  Initiative 1  (shell hardening — highest bang for buck)
           Initiative 2  (per-skill unit tests — can parallelize)
           
Week 2-3:  Initiative 3  (closed-loop wiring — builds on hardened scripts)
           Initiative 5  (cognitive dashboard — standalone, high user value)

Week 3-4:  Initiative 4  (default agent-loop — Stage 2 acceleration)
           Initiative 6  (outcome-driven proposals — builds on M4 loop)

Week 4-5:  Initiative 7  (semantic knowledge extraction)
           Initiative 8  (manifest completion — doc work, can parallelize)

Week 5-6:  Initiative 10 (multi-agent negotiation — Stage 3 architecture)
           Initiative 9  (install/uninstall isolation)

Week 6-7:  Initiative 11 (self-mod architectural refactoring — M6 precursor)
           Initiative 12 (portability — low urgency, high vision alignment)

What NOT to do (antibodies)

  1. Don't rewrite any skill from scratch. The existing scripts are battle-tested through the daemon; Initiative 1 hardens them in place.
  2. Don't add new dependencies. The suite's dependency set is: bash, jq, bc, python3, flock, date, sed, awk. No new tools.
  3. Don't touch legacy-IGNORE/. It's deliberately frozen.
  4. Don't change the JOBS table minutes mid-stream. The 30-job table is collision-free; adding jobs uses the "verified unique" pattern.
  5. Don't gate on M0 completion. M0 (host hardening) can proceed in parallel with Initiatives 1-3.

---## Acceptance for the whole plan

Every initiative lands as sequenced commits. After each commit:

  • bash -n on all touched files passes.
  • All 28 existing tests + any new tests pass.
  • python3 deep-brain-kernel.py --check reports 0 problems.
  • The three phase harnesses used in CI (run_phase2_harness.sh, run_phase5_harness.sh, run_skill_unit_tests.sh) stay green.

Progress log

Initiative Status Commit Summary
1 — Shell hardening ✅ COMPLETE 8194561 116 scripts → set -euo pipefail; 4 unbound-$1 bugs fixed
2 — Per-skill unit tests ✅ COMPLETE (pre-existing) 30/30 tests pass; all 15 manifests declare tests
3 — VTA→PFC goal loop ✅ COMPLETE 4e83b83 vta/encode-pipeline.sh calls record-goal-outcome.sh on accomplishment overlap
3b — Remaining 6 closed loops ✅ COMPLETE 4607eee Hippocampus→PFC, ACC→VTA, BG→decide, cerebellum→deploy, insula→PFC, oxytocin→social
4 — Default agent-loop ✅ COMPLETE 4e83b83 SPAWN_PROVIDER default: hermes → agentloop
5 — Cognitive dashboard ✅ COMPLETE 4e83b83 python3 deep-brain-kernel.py --brain (--json supported)
6 — Outcome-driven proposals ✅ COMPLETE (pre-existing) M5: test_m5_proposal_linkage.sh passes 6/6, generate-proposals-llm.sh reads health-context
7 — Semantic knowledge extraction ✅ COMPLETE f345531 extract-patterns.sh: heuristic pattern extraction from episodic store → semantic-state.json; decide.sh semantic boost
8 — Manifest completion ✅ COMPLETE (pre-existing) All 15 manifests already had tests/inputs/outputs/side_effects/dependencies
9 — Install/uninstall isolation ✅ COMPLETE 885c0de 13 per-skill installers delegate --uninstall to skill-cleanup.sh; init-all-skills.sh + cleanup-all-skills.sh chains
10 — Multi-agent negotiation ✅ COMPLETE 131be64 action-select.sh: per-option habit bias + epsilon-greedy exploration + loser suppression recording
11 — Self-mod architectural refactoring ✅ COMPLETE c83dafd create-module.sh: new_module → complete skill dir; check-target + apply-patch already handle multi-file
12 — Run-it-anywhere portability ✅ COMPLETE c83dafd deep-brain-kernel.py --no-systemd --no-cgroups flags; PSI/cgroups become passive no-ops

The plan is done when Initiatives 1-12 are all committed and pushed, and the docs/2026-08-22-comprehensive-improvement-plan.md itself has a final ✅ COMPLETE marker with the commit hashes.