Benjamin Admin
78f0ffa9de
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>
21 lines
766 B
Python
21 lines
766 B
Python
"""Implementation Playbook — the Berater renderer ("wie komme ich dort hin?").
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For one capability it assembles the full implementation journey (why / closes which regulations /
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tools / process / evidence / controls) from curated playbook knowledge + regulatory leverage +
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injected Execution links. `playbooks_for_plan` chains the Optimization Roadmap into per-measure
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playbooks. Pure, deterministic, computed-not-stored. No new corpus, no new meta-model class
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(freeze v1.0). Curated content = expert draft, never normative.
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"""
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from __future__ import annotations
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from .engine import build_playbook, playbooks_for_plan
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from .schemas import Playbook, PlaybookStep
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__all__ = [
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"build_playbook",
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"playbooks_for_plan",
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"Playbook",
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"PlaybookStep",
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]
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