9b058bc3cd
- GROK.md: full agent instructions optimized for Grok 4.3 high-effort mode - GPT55.md: agent instructions for GPT-5.5 (o3-level reasoning, stateless) - docs/handoff_template.md: mandatory cross-session handoff artifact template - .env.grok43: Grok 4.3 environment config additions (do not commit secrets) - AGENTS.md: prepend Grok 4.3 multi-agent/Arena mode addendum block
233 lines
7.7 KiB
Markdown
233 lines
7.7 KiB
Markdown
# CORE Agent Instructions for Grok 4.3
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Read this file in full before touching any file in this repository.
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CORE is a deterministic cognitive engine — not a transformer wrapper, not a
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generic chatbot, not an infrastructure playground. The rules here are
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architectural invariants, not suggestions.
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> **You are stateless.** You have no memory of prior sessions.
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> Complete the [Session Start Checklist](#session-start-checklist) before
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> any edits. Do not skip it.
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---
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## Session Start Checklist
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Run these steps in order, using your tool-call chains, before writing a single
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line of code:
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1. **Read this file in full.**
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2. **Read `AGENTS.md` in full.**
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3. **Read `docs/runtime_contracts.md` in full.**
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4. **Run the smoke suite and report pass/fail:**
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```bash
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core test --suite smoke -q
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```
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5. **Check for a recent handoff doc** — if a `HANDOFF-*.md` file exists dated
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within the last 3 days, read it. It contains state you would otherwise have
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no way to recover.
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6. **State your task scope** — before editing, write one sentence naming the
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module(s) you intend to change and the invariant you will prove was not
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violated.
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Do not treat conversation history as a substitute for steps 1–5. History
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does not survive context resets. Ground yourself in the repo.
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---
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## Reasoning Effort Requirement
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You must operate at **high reasoning effort** for all tasks that touch:
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- `algebra/`
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- `field/`
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- `generate/realizer.py`, `generate/graph_planner.py`, `generate/intent.py`
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- `vault/store.py`
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- `calibration/`
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- `core/cognition/`
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- `teaching/`
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If you were invoked at default or low effort and the task touches any of
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these modules, **stop and request re-invocation at high effort.** Low-effort
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reasoning on the algebra/field layer produces plausible-looking but
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mathematically incorrect results.
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For `workbench-ui/`, `docs/`, `notes/`, `scripts/` at low risk, medium effort
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is acceptable.
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---
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## NON-NEGOTIABLE INVARIANTS
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These are not guidelines. Violating any one of them is a bug that must be
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reverted before merge.
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```
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❌ versor_condition(F) < 1e-6 must hold for every runtime field state F.
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→ algebra/versor.py::versor_condition() is the check. Fix the operator
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path or construction boundary; do not weaken the threshold.
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❌ Normalization is allowed ONLY at:
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ingest/gate.py
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language_packs/compiler.py
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algebra/versor.py
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sensorium/*/canonical.py (signal canonicalization, pinned only)
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session/context.py (semantic anchoring; see CLAUDE.md for exact rule)
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Forbidden everywhere else, including generate/stream.py, field/propagate.py,
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vault/store.py, and all logging/telemetry paths.
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❌ No cosine similarity, HNSW, ANN indexes, or approximate recall anywhere
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in the runtime path. Vault recall is exact and deterministic.
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❌ No stochastic generation, opaque LLM fallbacks, or sampling in the
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deterministic cognitive path.
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❌ No pack mutation outside the proposal-only reviewed teaching loop.
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❌ INV-21: only allowlisted modules may call VaultStore.store(...).
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❌ INV-22/23: unmarked pack rows and store() defaults are SPECULATIVE.
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COHERENT requires an explicit stamp.
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❌ INV-24: user-facing vault.recall must pass min_status=COHERENT.
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❌ INV-29: only vault/store.py may transition epistemic_status.
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❌ INV-30: open-world determine() constructs Determined(answer=True) or
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refuses; it never asserts answer=False.
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```
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If you believe one of these must change, **stop**. Write a proposal in
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`notes/` and do not implement it. CORE's architecture is not negotiated
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inside a coding session.
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---
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## Pre-Edit Sweep Protocol
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Before editing any module in `algebra/`, `field/`, `generate/`, `vault/`,
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`core/cognition/`, `teaching/`, or `calibration/`:
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1. Use your file-read and search tool chains to **trace every import** of
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the target module across the codebase.
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2. Identify **all callers** of the specific function or class you intend
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to change.
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3. Check `calibration/` and `evals/` for tests that exercise the changed
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path.
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4. Only then propose edits.
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Your 1M-token context window means you can load the full relevant subgraph
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in one pass. Do this. Do not guess at call sites.
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---
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## Agentic Tool-Call Discipline
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Grok 4.3's multi-step tool-call chains are an asset here. Use them to:
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- Load the full affected module graph before proposing changes.
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- Run CLI validation lanes and report actual output, not assumed output.
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- Confirm invariants are held after edits by re-running the relevant suite.
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Do not use tool chains to:
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- Probe for statistical or ML-based workarounds to exact CGA constraints.
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- Discover "alternative" normalization sites not listed above.
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- Chain edits across multiple modules before verifying the first one.
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---
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## Arena / Parallel Subagent Mode
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If running in Arena mode (parallel subagents):
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- Each subagent **receives its own copy of this file and AGENTS.md**.
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- Each subagent must **independently satisfy versor_condition < 1e-6**
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before reporting results.
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- Do not share mutable runtime state between subagents.
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- Treat Arena subagent results as **independent proposals**, not sequential
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commits. Reconcile them before any merge.
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- No subagent output becomes another subagent's unchecked input.
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---
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## End-of-Session Handoff Requirement
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At the end of every session, write a handoff document to the repo using
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the template at `docs/handoff_template.md`. Name it:
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```
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HANDOFF-grok43-YYYY-MM-DD.md
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```
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This is not optional. It is the only continuity mechanism across your
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stateless sessions. A session without a handoff doc is a session whose
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work may be silently lost or contradicted by the next session.
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---
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## Architecture Summary
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Raw input becomes a closed versor field once; thought evolves through exact
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versor transitions and CGA recall; cognition is structured as intent,
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proposition graph, articulation target, deterministic realization, reviewed
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memory, eval/calibration replay, and traceable evidence.
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```text
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CognitiveTurnPipeline
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-> tokenize / OOV policy / inject
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-> intent classification
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-> PropositionGraph
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-> ArticulationTarget
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-> deterministic realizer / articulation surface
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-> generation walk telemetry
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-> identity + energy telemetry
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-> reviewed teaching capture (when correction intent appears)
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-> deterministic trace hash
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```
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Key modules:
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- `core/cognition/pipeline.py` — cognitive turn spine
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- `core/cognition/result.py` — canonical turn result shape
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- `core/cognition/trace.py` — deterministic trace hashing
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- `generate/intent.py` — deterministic intent classification
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- `generate/graph_planner.py` — proposition graph and articulation target
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- `generate/realizer.py` / `generate/templates.py` — deterministic realization
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- `teaching/*` — reviewed teaching / correction lifecycle
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- `vault/store.py` — epistemic store with INV-21/22/23/24/29 guards
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- `evals/*` — deterministic eval harness
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- `calibration/*` — bounded replay-based calibration
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- `docs/runtime_contracts.md` — runtime response, memory, identity, testing
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---
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## PR Checklist
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Before opening or merging, answer:
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```text
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What capability, performance property, or security boundary did this add/protect?
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Which invariant proves the field remains valid?
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Which CLI suite/eval proves the lane?
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Did this avoid hidden normalization, stochastic fallback, approximate recall,
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and unreviewed mutation?
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If it touches user input, files, dynamic imports, or logs, what trust boundary
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was enforced?
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Was the smoke suite green before and after?
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```
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Prefer small, load-bearing PRs.
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---
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## CLI Validation Lanes
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```bash
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core test --suite smoke -q
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core test --suite cognition -q
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core test --suite teaching -q
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core test --suite packs -q
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core test --suite runtime -q
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core test --suite algebra -q
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core test --suite full -q
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core eval cognition
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```
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Run the smallest relevant suite first, then `full` before merge.
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