noether-harness

name

noether-harness

description

Generate conservation-constrained agentic coding harnesses from the Noether Dual-Loop kernel — a μ meta-meta-prompt that emits a λ meta-prompt, which emits a concrete harness (typed conservation lens, four role prompts, per-framework fragments) for LangGraph, Aider, Cursor, OpenHands, AutoGen, ReAct+tools, or custom loops. Use whenever the user wants a self-refining coding agent where tests, type-checks, API stability, and architecture act as conserved invariants; a propose→tailor→commit→reflect dual loop with a bounded inner refinement loop and an outer invariant-learning loop; prediction-time "tailoring" (Alet et al.) transposed to LLM agents; or adaptation with ZERO weight updates / no fine-tuning. Also use to instantiate, extend, or explain such a harness. Trigger on "Noether harness", "conservation lens", "dual-loop agent", "tailoring loop", "tests-as-invariants harness", or a reference to the Noether meta-meta-prompt — even phrased only as "stop my agent breaking tests when it refactors."

Noether Harness — Conservation-Constrained Agentic Coding Systems

This skill packages a μ-level meta-meta-prompt (assets/meta-meta-prompt.txt) that generates harnesses in which a coding agent's edit trajectory is confined to a conservation pre-image — the set of states that keep chosen invariants (tests green, types clean, public API stable, architecture respected) approximately fixed. It is the LLM-agent transposition of Noether Networks and Tailoring (Alet et al., NeurIPS 2021): learn a useful conserved quantity, enforce it at prediction time by tailoring, prefer approximate conservation — pushed to its limit with zero weight updates.

When sheaf-* skills are loaded, they occupy ACV: exact CSP Φ decides; relative H¹ ranks. Command /sheaf-run (alias /sheaf-run course for a CETI explainer run). See references/SHEAF-ACV.md.

Depth lives in the references; read them, don't reinvent them:

• references/THEORY.md — where every construct comes from (Noether → Noether Networks → Tailoring → the categorical kernel → the honest theorem-vs-discipline framing → the agent lineage). Read this before explaining why the loop is shaped this way.
• references/APPLICATION.md — the four role prompts, lens-design patterns, the soft-judge template, the state schema, and copy-pasteable fragments for all six harness targets, plus a worked example and a failure-mode table. Read this before building anything.
• references/BIBLIOGRAPHY.md — annotated sources; cite from here, and attribute precisely.
• references/SHEAF-ACV.md — sheaf family as exact ACV for /sheaf-run and /sheaf-run course.
• assets/ — the verbatim μ generator, the fillable conservation_lens.schema.yaml, and the closure-verification.md gate block that every emitted harness must carry.

The three-level tower — keep it straight

Level	Name	Emits
μ	assets/meta-meta-prompt.txt (generator of generators, domain-agnostic)	a λ meta-prompt
λ	meta-prompt (bound to one harness + domain)	a concrete harness spec
·	concrete spec (lens YAML + role prompts + harness fragments)	commits under conservation

Most users want level · (a harness they can run). Some want level λ (a reusable generator for a whole class of their projects). Decide which before you produce anything (Step 1 below).

When to use / when not

Use it when the deliverable is a self-correcting coding loop with explicit invariants — the user wants an agent that refactors/edits without silently breaking things and that learns what to guard. Also use it to explain, extend, audit, or port such a harness, or to instantiate the attached μ prompt.

Don't use it for a one-shot code edit, a plain Q&A about an algorithm, or a non-agentic task — there is no loop to constrain. And do not use it to train anything: the harness's defining constraint is zero weight updates (see Kernel §8). If a request wants fine-tuning/LoRA, this is the wrong skill.

The kernel (summary — full treatment in THEORY.md §IV)

Every emitted harness must realize and verify these eight structures. This is the compressed form; do not operate from it alone for anything subtle — go to THEORY.md.

1. Lens category L (monoidal). Lenses are objects; reweightings are morphisms. ⊗ combines them: critical lenses by logical AND (exact veto), soft lenses by an associative aggregation of (score × confidence). Unit = the null lens. The associator ⇒ judge order-independence.
2. State S / proposal P. State = (codebase, history, current lens, Meta 2-cell log). Morphisms = edits, tool calls, reflection steps.
3. Conservation embedding g_φ (internal hom). Exact S→C (deterministic tools) and soft S→(C×[0,1]) (judges + confidence). Its soft pre-image must be non-empty and proper (0 < m < d) — constrains some but not all degrees of freedom.
4. Tailoring family (approximate retracts). Tailor_k : P→P, each step moves the proposal closer to the pre-image (or inside the tolerance ball); sequential closure holds; a budget identity morphism makes the inner loop terminate at INNER_BUDGET; flaky critical tools get a retry + quarantine endomorphism.
5. Outer Meta = Free ⊣ Forgetful. Unit η proposes new invariants from observed failure/success; counit ε prunes/demotes with a complexity penalty. Meta is a 2-cell; its history is persistent state.
6. Dual-loop morphism. Propose ▹ Tailor* ▹ Commit ▹ Meta, preserving the pre-image up to tolerance. Joint stop: commit ⇔ (all critical pass) ∧ (soft ≥ τ) ∧ (progress > 0).
7. Soft escape (controlled deformation). A TTL-bounded, logged enlargement of the tolerance ball for ONE soft lens — permitted only when all critical pass and progress is positive. Prevents the freeze; critical lenses are never escapable.
8. Hard negative constraint — ZERO weight updates. No fine-tuning, LoRA, or preference-opt, ever. All adaptation is context, memory, lens, or state. Absolute.

Workflow

Step 1 — Fix the level and the inputs

Decide: does the user want the λ meta-prompt (a reusable generator) or a concrete harness (the runnable thing)? Default to concrete unless they ask for the generator. Then gather the μ inputs — infer from the conversation/codebase where you can; ask only for what you genuinely can't infer:

• TARGET_HARNESS — LangGraph | Aider | Cursor | OpenHands | AutoGen | ReAct+tools | custom
• TASK_DOMAIN — language(s), critical modules, existing tests, architectural constraints, noise level
• SEED_INVARIANTS — any critical/soft quantities they already know they want (optional)
• INNER_BUDGET — default 3
• SOFT_HARD_BALANCE — default 0.3 (exact dominates; raise for refactor/exploratory work)
• DEPTH_MODE — minimal (kernel + protocols) or full (complete categorical constructions)
• THEORETICAL_DEPTH — how much of THEORY.md to surface in the justification section

If the user is vague ("make my agent stop breaking tests"), pick sensible defaults, state them inline, and proceed — don't stall on an interview.

Step 2 — Run the generation procedure (THEORY.md + APPLICATION.md are your source)

1. Domain analysis — map their codebase onto the kernel: natural objects in S, the morphisms, which critical tools and soft judges realize g_φ, and a measurable progress proxy. Use the critical-vs-soft heuristics in APPLICATION.md §2 (deterministic + fast + unambiguous + merge-blocking ⇒ critical; valuable-but-not-mechanical ⇒ soft).
2. Fill the lens — start from assets/conservation_lens.schema.yaml; set tau, SOFT_HARD_BALANCE, inner_budget, the escape policy, the progress proxy, and flaky_policy.
3. Emit the four role prompts — Proposer, Conservator, Tailorer, MetaReflector (APPLICATION.md §3), bound to their domain. The Conservator asymmetry is the linchpin: critical = veto (checked first, no averaging), soft = gradient. Never let soft override a critical veto.
4. Emit the soft-judge template returning strict {score, confidence, diagnosis, suggested_repair} (§4), and the state schema including the append-only meta_2cell_log (§5).
5. Emit the harness fragments for TARGET_HARNESS (§6.1–6.7) — real, copy-pasteable code/config.
6. Attach the ACV block (assets/closure-verification.md) — the generation-time checklist and the R1–R10 runtime checks — wired into the harness so the loop checks its own preservation each cycle.
7. Write the theoretical-justification section at the requested THEORETICAL_DEPTH, mapping each construct back to Noether / dimensionality reduction / prediction-time tailoring / approximate conservation / meta-inductive bias — using THEORY.md, and honestly (Step 4).

Step 3 — Deliver as files

A harness is something the user saves and runs, so write files to /mnt/user-data/outputs/ and present them: the conservation_lens.yaml, a roles/ set (or a single roles doc), the harness fragment(s) for their framework, and a short README explaining how the pieces fit and how to run the ACV checks. If they asked for the λ meta-prompt instead, deliver that as a single self-contained, loadable block (a file), following the μ generator's output contract.

Step 4 — Self-verification gates (run before handing over)

Confirm the emitted harness passes the generation-time checklist in assets/closure-verification.md:

□ Dual-loop closed under the kernel        □ Soft pre-image non-empty & proper (0 < m < d)
□ Tailor steps are approximate retracts    □ Budget identity morphism present
   with sequential closure                 □ Meta is Free ⊣ Forgetful (η AND ε)
□ Soft escape stays a controlled retract   □ Zero weight-update constraint absolute
□ Meta 2-cell history is in state          □ Concrete harness fragments emitted
□ ACV (closure-verification) block present

If any line fails, fix the spec before delivering — an un-closed harness is not Noether-compliant.

Two rules that keep this honest

• Present structure as a contract with gates, not a proof. In the LLM setting the categorical "guarantees" are runtime-checked design invariants, not theorems about the sampler (THEORY.md §V). This is more useful and more truthful — the abstraction is a compiler from "principled loop" to "runtime assertions." Do not claim it proves the model will behave.
• Zero-retrain is absolute. If any part of a request or a generated fragment implies weight updates, stop and route the adaptation into context/memory/lens/state instead. This is the constraint the whole lineage exists to honor.

Command

/sheaf-run
/sheaf-run course

Tagline: Glue what the dual-loop conserves.

See references/SHEAF-ACV.md and ceti-explainer/COURSE-E0.md.