Roadmap item 4. After WHAT applies / WHAT is missing / WHICH first, the GF asks HOW. The
Implementation Playbook renders, for one capability, the full journey — why / which regulations
it closes / tools / process / evidence / controls — and chains the Optimization Roadmap into
per-measure playbooks. Another renderer over the same Capability spine (ADR-003/004), not a new
engine: ~95% of the data already exists, it just needs a different rendering.
- compliance/playbook/: build_playbook() + playbooks_for_plan() (chains optimization -> playbook,
acyclic; reuses leverage for "closes which regulations"). Capabilities without curated content
render as honest status:missing stubs — the content-owed signal.
- knowledge/implementation_playbooks/: curated knowledge layer (Reasoning Knowledge Acquisition),
two deep expert drafts (SBOM, CVD/PSIRT, status draft, expert-draft-not-normative) + README.
The bottleneck is now CONTENT, not software; Playbook (own knowledge) != regulatory domain.
- ADR-004: Implementation Playbooks = renderer + knowledge layer; content is the bottleneck.
- reference suite: "Implementation Playbook" section renders the SBOM journey + Roadmap->Playbook
table (high-leverage caps flagged "fehlt (Inhalt)" — content backlog, highest leverage first).
- refactor: extracted markdown helpers to reference_scenarios/_helpers.py to keep generate.py
under the 500-LOC budget.
9 playbook tests (40 with optimization+transition+company), mypy --strict clean, check-loc 0.
Product code with no app caller + knowledge/ADR/reference = non-runtime -> no deploy (ADR-001).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Roadmap item 5. GAP analysis and measure-prioritisation are the SAME computation: Required −
Known = the Capability Delta. The Capability Delta Engine (RS-005) computes it once; renderers
read that ONE delta. Interview Renderer (missing info → questions) was already built; this adds
the Roadmap/Management Renderer (missing capabilities → measures ranked by regulatory leverage).
- compliance/optimization/: regulatory_leverage() + select_within_budget() (pure leverage math)
+ roadmap_from_delta(assessment, ...) — the keystone binding optimization to the RS-005 delta
(dependency optimization → transition_reasoning, acyclic; the delta engine stays hermetic).
leverage(measure) = number of regulatory requirements it closes at once (e.g. patch management
→ CRA+MaschinenVO+IEC62443+ISO27001 = 4). No new corpus, no new meta-model class (freeze v1.0).
- Welt-1 honesty: percentages are exact count ratios over the IDENTIFIED requirements (the known
delta), never "% gesetzeskonform".
- reference suite: "Regulatory Optimization" section runs the SAME convergence delta → ranked
measures + budget answer + the management sentence "of N identified requirements you close M
with the top-K measures (X%) — highest regulatory leverage".
- ADR-003: Capability Delta Engine — one delta, many renderers; rename Gap → Capability Delta.
13 optimization tests (31 with transition+company), mypy --strict clean, check-loc 0.
Product code with no app caller + ADR/reference = non-runtime → no deploy (ADR-001).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Aligns the spec with RS-005 v0: the Transition Planning Engine owns the INFORMATION
GAPS (TransitionQuestionRequest), not the questions. Chain: Planning Engine ->
TransitionQuestionRequest -> Question Renderer (RS-005.1) -> Interview. RS-005.1
(renderer/templates) deliberately deferred; GeneratedQuestion reframed as the renderer's
output (a swappable policy layer), not part of the engine.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
v1.1: interview questions are GENERATED from the existing (Master) Controls, not
hand-written. Three building blocks: Control->question_intent (corpus/Execution),
~30-40 Master Question Templates (Reasoning), Transition-Prioritization (certs decide
which generated questions can be skipped; 217->19 funnel, reuses Company 2A + cert map).
v1.2: knowledge production. LLMs produce the first expert DRAFT (the prioritization per
transition); BreakPilot reviews + versions + OWNS the canonical library (in Git, not the
AI; model-independent, MDQ-00127 v4). Offline multi-model workflow, NOT runtime
(deterministic-first: LLM offline-propose, never online-mutate). Hard boundary: the
library is an expert DRAFT, not a normative/legal proof -- "cert probably covers X" is
Welt-1 (ClaimCoverage), never "erfuellt" (anti-fake-evidence).
Reframes the 100 seed questions as validation/template-extraction set. Spec only, no
code; non-runtime docs -> no deploy (ADR-001).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Second reasoning mode (extends, does not replace): BreakPilot answers MIGRATION
questions (start state -> target state -> delta), not regulation Q&A. New package
compliance/transition_reasoning/ (spec only). Transition Reasoning is RCI
generalized; reuses Company 2A (have), Master Capability Registry (MCAP) and RCI.
MDQ Registry = 4th identity-machine instance (after Master Controls/Obligations/
Capabilities): every Master Delta Question is a versioned, identifiable knowledge
unit (verifies MCAP, supports obligations, transition patterns, evidence types,
information gain, confidence impact, follow-up). Transition Patterns hold only MDQ
references -> reuse across transitions. Delta interview = information-gain
optimization, not a sequential questionnaire.
ADR-002: transitions are DATA (patterns + capability/MDQ knowledge), never engine
or metamodel extensions. 100 seed questions captured as v1.
Spec only (no code; freeze-respecting: additive package, no new graph/base class/
meta-model class). Non-runtime docs -> no deploy (ADR-001).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
A dev deploy must always have a verifiable runtime effect. Deploy only on
runtime/API/data-model/reasoning/security changes; docs, reference suites, ADRs,
board and ownership texts are merged to origin/main but NOT pushed to dev (no Orca
build). Keeps the CI/CD history meaningful: every build == a runtime change.
Architecture/release decision (not a developer convention) -> own folder
docs-src/architecture/adr/. Non-runtime: this commit triggers no deploy, per its
own policy.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
9 docs + index in docs-src/architecture/ documenting the deterministic
retrieval engine: retrieval pipeline, authority rerank, source_class,
source_role, control-intent + diversity, assessment, confidence,
explainability + supersede, framework_* layer. Each doc carries the exact
constants, the rationale behind them, code refs, and the failure class
it addresses. Audit/onboarding reference.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Benjamin Admin