# portfolio-optimiser-claude

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-%E2%89%A53.10-blue.svg)](pyproject.toml)
[![Built on Claude Agent SDK](https://img.shields.io/badge/built%20on-Claude%20Agent%20SDK-D97757.svg)](https://github.com/anthropics/claude-agent-sdk-python)

Sibling implementation of the portfolio-optimiser method on the **Claude Agent SDK**
(decision D7). An open, generic Python framework that finds cost savings *inside* each
project in a portfolio of independent projects: agents generate candidate measures, a
mandatory deterministic validator gates the numbers, domain experts judge via
human-in-the-loop, and the system learns from the verdicts.

> **Status:** the D7 build (S5–S10) is complete, and the deterministic **ingest layer**
> (CSV and SQL source types) has since been added in front of the loop. The deterministic
> backbone, the agentic loop, the learning loop, and the ingest connectors are wired seam by
> seam, each proven by load-bearing tests (521 tests, all running offline without an API
> key). The programme's single budgeted **live model run has been executed and validated** —
> its artifacts are committed under [`runs/s10/`](runs/s10/) (see below).

> **Disclaimer — technical framework only.** The deployer owns DPIA, risk assessment, and
> the legal basis for any processing. The framework ships only the technical
> preconditions: local-only operation, first-class provenance, no silent data egress.

## Built from the spec, not the sibling

The method itself is framework-neutral and lives in
[`portfolio-optimiser-commons`](https://git.fromaitochitta.com/ktg/portfolio-optimiser-commons)
(consumed here as a git subtree under [`shared/`](shared/)): the normative method spec
(RFC 2119), the OKF bundle-navigation contract, the golden/conformance suite — the *only*
oracle for the validator — and the shared expert-reviewer persona skill. This repo
implements that spec on the Claude Agent SDK; it deliberately does **not**
reverse-engineer the MAF sibling
([`open/portfolio-optimiser`](https://git.fromaitochitta.com/open/portfolio-optimiser)).
Two independent implementations of one spec, compared afterwards, is the point of D7.

## Architecture — the seams

Everything below the run layer is pure config/file logic and runs deterministically,
offline. Module by module:

**Deterministic backbone** (method-spec §3 step 4, §7–§10)
- `ir.py` — the typed cost-IR of a candidate measure (§7.1).
- `validator.py` — the deterministic validator; blocking, and frozen by the shared golden
  suite (§7.2), which is the only fasit it answers to.
- `provenance.py` — the first-class provenance stamp (§9); authoritative data, not
  after-the-fact logging.
- `contracts.py` — fail-fast startup contracts (§10): stop criteria and budget caps are
  required at startup, and the model map (`data/model_map.json`, role → Claude model id
  per backend profile) is validated before anything runs.

**Context seam** (§3 step 1)
- `okf.py` — read-context built by **navigating** the project's OKF bundle (`index.md` +
  frontmatter + cross-links, progressive disclosure) — never keyword chunk-stuffing. The
  `type: verdict` layer is excluded from the read-context.
- `experience.py` — the ExpeL-style experience seam: store, structural retrieval, and the
  gated fold. A prior expert verdict reaches the next hypothesis *only* through the fold,
  never by leaking through context.

**Agentic loop** (§3 steps 2–5, §8)
- `budget.py` — the budget meter: no unbounded loop exists anywhere in the framework. On
  top of the post-charge token/round caps sits an optional pre-call run-total USD belt that
  refuses the next model call once the run has crossed its USD budget.
- `loop.py` — generate, maker–checker debate, gate, and informed refinement: the
  validator's previous rejection reason is fed into the next bounded attempt, so the
  model corrects against the falsification instead of re-answering identically.

**Learning loop** (§3 steps 7–8, §4–§6)
- `inbox.py` — the async verdict-file contract: an expert drops a plain-JSON verdict into
  an inbox folder after a run; a later run ingests it tolerantly and merges it before the
  fold. The shippable entrance for that later run is `run.py`:
  `uv run python -m portfolio_optimiser_claude.run --bundle <dir> --inbox <dir>`.
- `promotion.py` — the promotion gate, **fail-closed**: only an approved verdict is
  lifted into the OKF context layer; anything else raises and writes nothing.
- `persona.py` — the expert-reviewer persona sourced from the shared artifact in
  [`shared/skills/expert-reviewer/`](shared/skills/expert-reviewer/) at call time, so the
  shared persona is genuinely consumed and cannot rot silently.

**Value layer** (stack-local contract — mirrored from the sibling plan's capability
description, never from its code)
- `ledger.py` — the typed savings ledger: realized savings enter the book **only** through
  the fail-closed expert gate (an approved verdict + a named expert + an explicit
  timestamp), and the sum key is dimension-free, so the same realized saving surfaced via
  two dimensions is never double-counted. Persistence is deterministic JSON.
- `goals.py` — the goal contract: absolute savings target, hard/soft. A hard goal reached
  raises a structured stop event, never a silent stop; the percent-goal baseline is
  D-E-gated and refused explicitly.

**Run layer** (the only part that touches the network)
- `sdk_client.py` — the Claude Agent SDK client, isolated from local configuration
  (`setting_sources=[]`) so no user/project config can leak into a run.
- `artifacts.py` — §9 citations plus deterministic run-artifact persistence, including on
  structured stops (a budget stop still leaves artifacts behind).
- `outbox.py` — the outbox output layer (S2.1): each completed run persists a `run_id`-named
  proposal/outcome pair — the system's own output, which it writes freely (the role split
  governs the inbox and wiki, not this). The outcome carries the inbox join key (`verdict_id`,
  minted the same way the inbox mints an expert verdict's id) so outstanding verdicts can be
  tracked and live artifacts captured; bytes reuse the deterministic house JSON writer, and
  the S10 artifact formats are untouched.
- `run.py` — the generic run entrance: composes merge-inbox → seed → fold (§5) and drives
  the loop under the budget meter, persisting artifacts on both outcomes — a structured
  budget stop included. The model client is injected, so the offline suite proves the
  same orchestration with a scripted client; only the CLI's default constructs the SDK
  client. `--live-dry-run` is the **live-run drill** (K8): it builds everything a real run
  would (contracts → compose → client construction → preflight) and captures a `run_id`-named
  `runconfig` + `preflight` pair to the outbox (model-id, parameters, caps — no wall-clock, so
  the bytes stay deterministic), then **stops before the first model call**. It exits 0 when
  the preflight is clear and non-zero when it refused, but captures the artifacts and makes
  zero model calls either way — a future operator-gated live run is rigged and rehearsed
  offline, with no spend.
- `portfolio.py` — the sequential multi-project run and learning loop: `run_portfolio` drives
  N projects from a schema-validated reference config, composing each project's context afresh
  (re-entrant, fresh debate state per run) and collecting one result per project in config
  order. Two things are deliberately shared portfolio-wide: the §8 budget meter (the cap) and a
  single learning `VerdictStore` — a verdict available when project k composes survives into
  project k+1's fold (cross-project threading, §5), and an optional `verdict_dir` is the
  portfolio-level expert inbox the system reads before each fold. The default failure policy
  raises (a stack-local choice until D-D flips it to collect-and-continue).
- `run_s10.py` — the programme's ONE live run (cost discipline D6); run-path only.
- `costsim.py` — pre-run cost simulation (**offline** — the one Run-layer module that never
  touches the network): a deterministic UPPER-BOUND USD estimate for a (portfolio-)run
  *before* any spend, a what-if over the models in `model_map.json` × effort levels. Pricing
  is schema-validated config (`data/pricing.example.json`): a per-Mtok rate per model, each
  with a required source + date so a stale rate is visible, never silent. A model configured
  with no price fails fast — there is no hardcoded rate anywhere (a grep-guard proves it), and
  the figure is marked `ESTIMAT` (the whole cap billed at the rate is an upper bound; real runs
  cost less). `uv run python -m portfolio_optimiser_claude.costsim`.
- `preflight.py` — the SDK/API preflight (**offline** — the boundary the operator crosses
  *before* any spend): everything that can be validated without a model call is checked here,
  so a broken config stops cheaply instead of on the first billed call. Four checks — a
  credential is present and not a placeholder form (`ANTHROPIC_API_KEY`, or the bundled CLI's
  own `CLAUDE_CODE_OAUTH_TOKEN`; the value itself is *never* validated online), the model_map
  profile exists and every id it resolves to is real, `claude_agent_sdk` imports and its
  bundled CLI is present on disk, and the §8 stop/budget contract is set. It never calls the
  API — a green preflight implies no more than that (§1). Each deficiency is a structured,
  actionable refusal. `uv run python -m portfolio_optimiser_claude.preflight`.

### Load-bearing tests (§11)

Every seam is proven by a test that goes **red when the seam is detached** — green-but-dead
is the failure mode the rule exists for. Among them: `test_step1_expel_loadbearing.py`
(the verdict signal reaches the prompt via the fold, and only via the fold),
`test_checker_gate_loadbearing.py` (an explicit checker reject blocks a validated
proposal), `test_step5_refine_loadbearing.py` (the rejection reason verifiably reaches the
retry prompt, and the loop still stops at the cap), `test_step7_async_loop_loadbearing.py`
(a verdict dropped after run A reaches run B's prompt through the file loop, with an
empty-inbox control), `test_step8_promotion_loadbearing.py` (the gate refuses non-approved
verdicts; the promoted signal stays out of the read-context),
`test_portfolio_learning_loadbearing.py` (a verdict available at project k survives into
project k+1's fold via the shared store, with a marker-absent control),
`test_outbox_loadbearing.py` (a completed run's `run_id`-named outbox pair is written on the
entrance path, with a no-outbox control, and the outcome carries the inbox join key),
`test_preflight.py` (a missing credential and a placeholder model id are each refused before
any spend, and the preflight carries no network path of its own),
`test_dry_run_loadbearing.py` (the live-run drill captures its `runconfig` + `preflight`
artifacts and stops before the first model call — a call-counting client proves zero calls,
red the moment the stop seam is detached), and
`test_sdk_isolation.py` (local config cannot capture the checker).

## The ingest layer — CSV and SQL, in front of the loop

The method spec forbids query-time retrieval against the bundle (§3 Step 1), so **data
reaches the model only via OKF bundles.** The ingest layer is the deterministic step that
satisfies that: a connector reads a real source, and the extract is materialized as an OKF
bundle the existing 8-step loop then consumes **unchanged**. It makes **zero model calls**,
touches no network, and `ingest.py` imports nothing from the SDK — it is pure standard
library. Built from the shared [`ingest-spec.md`](shared/ingest-spec.md) alone.

D7 implements the two conformance-required source types:

- **`file`** — a local CSV catalogue; extraction paths are boundary-checked fail-closed
  against the source `root` (the OKF path rule).
- **`sql`** — a local SQLite database, opened **read-only** (`mode=ro`), one SELECT per
  extraction; the connection location is resolved at run time from a named environment
  variable (`connection_ref`), never stored in the manifest.

Both are frozen by byte-identical golden extractions
([`examples/ingest-golden-file/`](examples/ingest-golden-file/),
[`examples/ingest-golden-sql/`](examples/ingest-golden-sql/)) and by the load-bearing seam
tests (provenance stamping, navigability through the unchanged `okf.py`, the reserved
verdict layer, and re-ingest safety over a promoted verdict).

**Honesty rule (§1):** the `http` source type is an **optional extension point, not built in
D7** — a manifest naming it is rejected fail-fast at validation
(`test_malformed_manifest_is_rejected`), never silently accepted. The HTTP/MCP extension
point is demonstrated only in the MAF sibling (against a local mock, behind an opt-in
network flag); this repo ships no network connector and no live-source integration. How the
layer works and how one would extend it is documented in [`docs/extending.md`](docs/extending.md).

## The live run — S10, executed and validated

The loop's closure is proven offline by a scripted two-run simulation
(`simulation.py`: run A → persona verdict → §6 promotion gate → run B on a fresh store —
the marker crosses runs via the gate, and never without it). On top of that offline proof
sits the programme's single budgeted **real** run (D6: exactly one live API run in the
whole programme), executed 2026-07-03 against the micro bundle
[`shared/examples/bygg-energi-mikro/`](shared/examples/bygg-energi-mikro/):

- exit 0 · validator `validated` · checker `approve` on the first attempt · 2 of 12
  rounds · 36 791 of 150 000 budgeted tokens · **cost $0.127514** (Haiku 4.5, per the
  model map), under a first-class `max_budget_usd` cap.
- The proposal claimed a deliberately conservative 30 000 NOK saving against the
  bundle's p10–p90 band of 68.5k–121k — and validates.
- All four artifacts are committed as fixed reference output in [`runs/s10/`](runs/s10/):
  `proposal.json`, `provenance.json` (with §9 citations), `run_result.json`, `usage.json`.

Honesty rule (§1): everything else in the repo is deterministic and offline; nothing here
claims more live behaviour than that one documented run.

## Stack

Python ≥3.10 · [`claude-agent-sdk`](https://pypi.org/project/claude-agent-sdk/) ≥0.2
(bundles the Claude Code CLI; an API key is needed only at actual `query()` time) ·
`uv` · Pydantic for contract validation.

## Development

```bash
uv sync          # install dependencies
uv run pytest    # 521 tests — run without any API key and without network
uv run ruff check . && uv run ruff format --check .
uv run mypy src  # strict
```

The offline invariant is deliberate: everything below the run layer is pure config/file
logic, so the full suite (including every load-bearing seam proof) runs with no key and
no network.