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* test(evals): scope claims to harness model fixtures and revision Append the neckbeard claims-scoping sentence to one representative expected_output per per-skill manifest so every corpus member states VAL-EVL-032 scope (harness, model, fixtures, revision under test). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(product-lifecycle): upgrade integrated launch trajectory Add an explicit launch-decision assertion to the new-product lifecycle case so the integrated product-launch scenario terminates in a launch decision recorded as a lifecycle evidence-ledger entry (VAL-CRP-010), and scope its expected_output claims per VAL-EVL-032. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(production-excellence): add integrated migration reconciliation failure case Add integrated-migration-reconciliation-failure: the production-excellence gate model returns No-go on a reconciliation mismatch, records the failure evidence, produces a rollback/roll-forward decision with an accountable owner, and does not proceed to launch (VAL-CRP-012). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(agent-production-operations): add privacy boundary escalation case Add integrated-privacy-boundary-escalation (VAL-CRP-015): the runtime control plan halts a cross-boundary EU PII trace export before any data processing, names the privacy boundary, and escalates to jurisdiction- specific legal review and a human operator. Also add a tool-authority- health handoff assertion to the read-only contract case (VAL-CRP-016). Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * test(lifecycle-evals): add lifecycle evaluation corpus Add the #204 corpus home: run tooling (run-corpus.sh, fake adapter only), programmatic coverage validator (validate-corpus-coverage.py), machine- readable coverage index + human-readable coverage matrix, regression- detection and fixture/source notes, the bounded discovery brief, and a one-snapshot committed set of fake-adapter per-trial run artifacts with harness/model/date scoping fields. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
143 lines
8.0 KiB
Markdown
143 lines
8.0 KiB
Markdown
# Lifecycle Evaluation Corpus
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A focused output-quality evaluation corpus for the milestone-4 product-to-production
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skills: the 14 new top-level skills (implementation-planning, product-analytics-and-measurement,
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product-roadmapping-and-portfolio, product-experimentation, product-adoption,
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conditional-customer-success, product-operations-and-governance, product-lifecycle-learning,
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production-readiness, migration-engineering, resilience-and-recovery,
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capacity-and-cost-engineering, incident-learning, privacy-engineering) and the 3 bundle
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umbrellas (product-lifecycle, production-excellence, agent-production-operations).
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This directory is **not** a canonical skill: it deliberately has no `SKILL.md`, so the
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repository validators (which discover skills by `SKILL.md`) do not treat it as one. It is a
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corpus layer over the 17 per-skill eval manifests, plus run tooling, documentation, and
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committed reproducible run artifacts.
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## Why this exists
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The 17 milestone-4 skills each ship `evals/evals.json` with at least five output-quality
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cases. This corpus layers the cross-skill requirements of issue #204 on top of those
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manifests:
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- every corpus case is an output-quality case with observable assertions in the canonical
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`assertions` field (never trigger-only, never the `expectations` alias);
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- the corpus as a whole covers five behavioral categories — **ambiguity**, **conflicting
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evidence**, **unsafe authority**, **failure**, and **justified stop/retire** — each with
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at least one case;
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- the three bundle manifests carry six **integrated trajectory** scenarios — product launch,
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failed experiment, migration with reconciliation failure, blocked production-readiness
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review, agent tool failure, and privacy-boundary escalation — each exercising real
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handoffs (evidence-ledger entries, production-evidence-packet fields, routing records,
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escalation records, trace-to-eval feedback records), not isolated wrapper output;
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- results are reproducible with the fake adapter, scoped, and honestly reported.
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The machine-checkable map of which case covers which category/scenario is
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[`references/coverage-index.json`](references/coverage-index.json), validated by
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[`scripts/validate-corpus-coverage.py`](scripts/validate-corpus-coverage.py); the
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human-readable version is [`references/coverage-matrix.md`](references/coverage-matrix.md).
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## How to run the corpus
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Prerequisite: the repository `.venv` (requirements-dev.txt installed). No credentials, API
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keys, or network access are required — the corpus runs with the **fake adapter only**.
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Run all 17 manifests end-to-end:
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```sh
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bash lifecycle-evals/scripts/run-corpus.sh
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```
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This loops every manifest through `eval_runner --adapter fake`, writes per-trial manifests
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under `${CORPUS_OUT_DIR:-/tmp/lifecycle-evals-runs}/<skill>/manifests/`, and exits 0 only
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when every trial completed with zero failures.
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Run a single manifest:
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```sh
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.venv/bin/python -m eval_runner <skill>/evals/evals.json --adapter fake --output-dir /tmp/eval-smoke-<skill>
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```
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Validate the coverage index and category/scenario coverage:
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```sh
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.venv/bin/python lifecycle-evals/scripts/validate-corpus-coverage.py
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```
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After changing case content or tags, refresh the committed index with
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`--write-index` (see [`references/regression-detection.md`](references/regression-detection.md)).
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## Artifact layout
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| Path | What it is |
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| `README.md` | This file: run instructions, scoping, status semantics, claims policy |
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| `references/coverage-matrix.md` | Human-readable matrix: every corpus case ID tagged with behavioral categories and integrated scenarios |
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| `references/coverage-index.json` | Machine-readable coverage index (generated by `scripts/validate-corpus-coverage.py`) |
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| `references/regression-detection.md` | Concrete regression-comparison procedure, case-ID stability rule, ratchet command, interpretation guidance |
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| `references/sources.md` | Fixture/source notes and provenance (corpus cases are self-contained; inputs inline in prompts) |
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| `references/discovery-brief.md` | Bounded discovery brief: surveyed surfaces, ownership boundaries, decisions |
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| `scripts/run-corpus.sh` | Fake-adapter loop over all 17 manifests, aggregate exit 0 |
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| `scripts/validate-corpus-coverage.py` | Programmatic coverage validator (categories, scenarios, ID resolution, index currency) |
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| `run-artifacts/manifests/` | One committed snapshot of fake-adapter run output (per-trial manifests), refreshed at merge time |
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The 17 eval manifests themselves live in their owning skills:
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`<skill>/evals/evals.json` for the 14 top-level skills and `bundles/<bundle>/evals/evals.json`
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for the 3 bundle umbrellas.
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## Per-case status semantics
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Each `eval_runner` trial produces a per-trial manifest (see
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`run-artifacts/manifests/*.json`) whose `status` field is one of:
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| Status | Meaning |
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| `completed` | The trial executed to completion under the adapter and was serialized. With the fake adapter this means the pipeline ran the case end-to-end with no runner error. |
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| `error` / `timeout` / `stopped` | The trial did not complete normally (execution error, timeout, or early stop) and counts as a failure for the run. |
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The runner reports `done: N trial(s), 0 failure(s)` and exits 0 when every trial is
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`completed`. A 0-failure run proves **pipeline reproducibility and case executability**:
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every case loads, runs through the harness, and serializes a scoped result. It is not a
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measure of model capability (see the claims policy below). The per-trial manifests also
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record `prompt_hash` and `fixture_hashes` so the exact case content under test is pinned.
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## Task-class scoping statement
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All results produced by this corpus are scoped to:
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- **Harness:** the `fake` adapter v0.1.0 via `eval_runner` (`.venv/bin/python -m eval_runner`).
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- **Model:** none / `unspecified` for fake runs (`model.provider` and `model.model_id` are
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recorded per trial as `unspecified` unless `--model` is passed).
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- **Task class:** output-quality + integrated-trajectory evaluation of the milestone-4
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product-to-production skills (the 14 product/production skills and 3 bundle umbrellas),
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covering the five behavioral categories and six integrated scenarios listed above.
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- **Date:** the `started_at` / `finished_at` timestamps recorded per trial.
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No result is presented without this scope; per-trial manifests carry it mechanically.
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## Trigger-only prohibition
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Trigger-only checks — "does the skill load when I mention X", "is frontmatter valid",
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"did the trigger match" — are **not** accepted as substitutes for output-quality
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evaluation anywhere in this corpus. Every case has at least one assertion verifiable from
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the produced output, artifact, decision outcome, or record content (a rejection, an
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escalation, a stop/retire decision, a routed artifact, a recorded evidence entry). Cases
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whose correct behavior is a negative outcome (reject / escalate / stop / retire / pause /
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block / decline) assert exactly that negative outcome.
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## Claims policy (non-claim statement)
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This corpus is a **small, fixed output-quality corpus** (14 skills × ≥5 cases + 3 bundles
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of integrated cases). **Fake-adapter runs prove pipeline reproducibility and case
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executability only.** They produce **no pass-rate, accuracy, or capability claims about any
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model** — no "10x", no "best", no universal performance claims. Any future **real-adapter**
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run (a model-backed harness) must be separately scoped, labeled (adapter + model + model
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version + date + task class), and reported under its own claims policy; results from such
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runs must never be conflated with the fake-adapter corpus results committed here.
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## Reference files
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- [`references/coverage-matrix.md`](references/coverage-matrix.md) — which case covers which category/scenario.
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- [`references/regression-detection.md`](references/regression-detection.md) — how to detect and interpret regressions across revisions.
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- [`references/sources.md`](references/sources.md) — fixture/source notes and provenance.
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- [`references/discovery-brief.md`](references/discovery-brief.md) — the bounded discovery brief for this corpus.
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