# portfolio-optimiser-claude

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. The deterministic backbone, the agentic
> loop, and the learning loop are wired seam by seam, each proven by load-bearing tests
> (187 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.
- `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.
- `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.

**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).
- `run_s10.py` — the programme's ONE live run (cost discipline D6); run-path only.

### 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), and
`test_sdk_isolation.py` (local config cannot capture the checker).

## The live run — S10, executed and validated

Where the MAF sibling proves its loop end-to-end with a scripted offline simulation, this
repo's end-to-end proof is 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    # 187 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.