feat(knowledge-production): Playbook Draft Generator — prepare the corpus deterministically
The bottleneck is not content, it is knowledge PRODUCTION. Instead of writing 200 playbooks by hand, generate drafts deterministically from data the software already owns, then have an expert review them. Mirrors the legal pipeline (Gesetz -> Parser -> Obligation -> Review) for BreakPilot's own knowledge: new Capability -> Registry -> Transition Pattern -> Playbook Draft Generator -> Expert Review -> versioned Playbook. - compliance/knowledge_production/: generate_playbook_draft(capability, requirement, control_links) + drafts_from_pattern(pattern) -> one PlaybookDraft per delta capability. Owned fields (why / closes_regulations / expected_evidence / typical_controls) are assembled with per-field provenance; the practitioner know-how (tools / process_steps / how_others) is left as an explicit TODO. - DraftStatus lifecycle (Freigabestatus): draft_generated -> in_review -> reviewed -> validated -> proven. Deterministic, NO LLM in the core (any model enrichment stays offline/advisory/propose-only). - ADR-005: extends "the engine does not change, the corpus grows" with "and the corpus is not written by hand — it is deterministically prepared, then curated". - reference suite: "Knowledge Production" section turns the convergence pattern into 12 auto-assembled drafts (why/closes/evidence filled, tools/steps TODO) -> review 12 drafts, don't write 12 playbooks. 10 tests (50 with playbook/optimization/transition/company), mypy --strict clean, check-loc 0. Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
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"""Knowledge Production — deterministically prepare the corpus, then curate it.
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The corpus is not written by hand: the Playbook Draft Generator structures drafts from data the
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software already owns (Transition Pattern + leverage + injected Execution controls), leaving the
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practitioner know-how as TODO for expert review. Mirrors the legal pipeline (Parser -> Review).
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Deterministic, no LLM in core, no new corpus, no new meta-model class (freeze v1.0).
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"""
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from __future__ import annotations
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from .engine import drafts_from_pattern, generate_playbook_draft
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from .schemas import DraftStatus, PlaybookDraft
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__all__ = [
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"generate_playbook_draft",
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"drafts_from_pattern",
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"PlaybookDraft",
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"DraftStatus",
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]
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"""Knowledge Production — the Playbook Draft Generator (deterministic assembly + expert review).
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Mirrors the legal pipeline (Gesetz -> Parser -> Obligation -> Review) for BreakPilot's OWN knowledge:
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new Capability -> Registry -> Transition Pattern -> **Playbook Draft Generator** -> Expert Review ->
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versioned Playbook. The generator does not WRITE playbooks — it STRUCTURES drafts from data the
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software already owns (a transition/convergence pattern's delta requirement: why_asked, covers_targets,
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expected_evidence) plus injected Execution controls. The practitioner know-how (tools / process steps /
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how others do it) is left as an explicit TODO for the expert (or a separate offline-propose step).
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Fully deterministic, NO LLM in the core (deterministic-first: any model enrichment is offline,
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advisory, never in this assembly). No new corpus, no new meta-model class (freeze v1.0). Python 3.9.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from .schemas import DraftStatus, PlaybookDraft
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_SOFT_FIELDS = ["tools", "process_steps", "how_others_do_it"] # practitioner know-how — expert/offline-propose
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_DISCLAIMER = (
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"Maschinell assemblierter ENTWURF aus vorhandenen Daten (Transition Pattern + Leverage + "
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"injizierte Controls). KEINE normative Anforderung; erfordert fachliche Kuratierung (TODO-Felder) "
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"und Statuswechsel draft_generated -> reviewed -> validated."
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)
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def generate_playbook_draft(
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capability_id: str,
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requirement: Optional[Dict[str, Any]] = None,
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control_links: Optional[List[str]] = None,
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) -> PlaybookDraft:
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"""Assemble a playbook draft for ONE capability from a pattern delta requirement (deterministic).
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`requirement`: a delta_requirement dict (why_asked / covers_targets / expected_evidence). Owned
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fields are filled with provenance; soft fields are listed in `todo`. `control_links`: injected
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Execution controls (default empty — no Execution data in the draft generator).
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"""
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req = requirement or {}
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why = str(req.get("why_asked") or req.get("missing_because") or "")
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closes = sorted({str(t) for t in req.get("covers_targets", [])})
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evidence = [str(e) for e in req.get("expected_evidence", [])]
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controls = list(control_links or [])
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provenance: Dict[str, str] = {}
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todo: List[str] = []
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if why:
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provenance["why"] = "transition_pattern:why_asked"
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else:
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todo.append("why")
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if closes:
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provenance["closes_regulations"] = "leverage:covers_targets"
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if evidence:
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provenance["expected_evidence"] = "transition_pattern:expected_evidence"
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else:
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todo.append("expected_evidence")
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if controls:
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provenance["typical_controls"] = "execution:control_links"
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todo.extend(_SOFT_FIELDS) # always expert-owned
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return PlaybookDraft(
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capability_id=capability_id,
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status=DraftStatus.DRAFT_GENERATED,
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title=capability_id.replace("_", " "),
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why=why,
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closes_regulations=closes,
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expected_evidence=evidence,
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typical_controls=controls,
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provenance=provenance,
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todo=todo,
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disclaimer=_DISCLAIMER,
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)
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def drafts_from_pattern(
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pattern: Dict[str, Any],
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control_links_by_cap: Optional[Dict[str, List[str]]] = None,
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) -> List[PlaybookDraft]:
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"""Assemble one playbook draft per delta capability of a transition/convergence pattern.
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This is the "produce drafts, don't write them" tool: feed a pattern -> get a draft per missing
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capability, ready for expert review. Deterministic + order-preserving (pattern order).
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"""
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links = control_links_by_cap or {}
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drafts: List[PlaybookDraft] = []
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for d in pattern.get("delta_requirements", []):
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cap = d.get("capability")
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if not cap:
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continue
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drafts.append(generate_playbook_draft(str(cap), d, links.get(str(cap))))
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return drafts
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"""Schemas for Knowledge Production — deterministic draft assembly + lifecycle.
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The corpus is no longer written by hand: it is deterministically PREPARED from data the software
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already owns (Capability, Transition Pattern, Controls, Evidence, leverage), then curated by an
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expert. A `PlaybookDraft` is a machine-assembled skeleton with per-field provenance and an explicit
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TODO list of what still needs human (or offline-propose) input. No LLM in the deterministic core.
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Python 3.9 compatible (no `|` unions).
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"""
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from __future__ import annotations
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from enum import Enum
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from typing import Dict, List
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from pydantic import BaseModel, Field
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class DraftStatus(str, Enum):
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"""Freigabestatus — the knowledge lifecycle from machine draft to proven (mirrors the
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transition-pattern / playbook maturity, with a machine-assembled pre-stage)."""
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DRAFT_GENERATED = "draft_generated" # machine-assembled, NOT yet expert-touched
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IN_REVIEW = "in_review" # an expert is curating it
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REVIEWED = "reviewed" # internally reviewed
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VALIDATED = "validated" # domain expert confirmed
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PROVEN = "proven" # confirmed in the field
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class PlaybookDraft(BaseModel):
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"""A deterministically assembled playbook draft for one capability.
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Owned fields (why / closes_regulations / expected_evidence / typical_controls) are filled from
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existing data with provenance; the practitioner know-how (tools / process_steps / how_others)
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is left as TODO. The expert reviews a draft instead of writing from a blank page.
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"""
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capability_id: str
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status: DraftStatus = DraftStatus.DRAFT_GENERATED
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title: str = ""
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why: str = "" # from the transition pattern (why_asked/missing_because)
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closes_regulations: List[str] = Field(default_factory=list) # from leverage (covers_targets)
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expected_evidence: List[str] = Field(default_factory=list) # from the transition pattern
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typical_controls: List[str] = Field(default_factory=list) # injected from Execution (may be empty)
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provenance: Dict[str, str] = Field(default_factory=dict) # field -> source it was assembled from
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todo: List[str] = Field(default_factory=list) # fields the expert/offline-propose must still add
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disclaimer: str = "" # machine draft, requires expert curation
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@@ -40,6 +40,7 @@ from compliance.transition_reasoning import (
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)
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from compliance.optimization import roadmap_from_delta, select_within_budget
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from compliance.playbook import playbooks_for_plan
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from compliance.knowledge_production import drafts_from_pattern
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import os
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import yaml
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@@ -438,6 +439,30 @@ coverage_table([
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("Playbook-Inhalt (Knowledge)", "TODO" if _miss else "PASS", "%d Capabilities brauchen noch Inhalt" % len(_miss)),
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])
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# ── Knowledge Production — Playbook Draft Generator (vorbereiten, dann kuratieren) ───
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w("## Knowledge Production — Playbook-Entwürfe automatisch assemblieren")
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w("")
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w("_Der Engpass ist nicht Content, sondern Wissensproduktion. Der Corpus wird nicht von Hand geschrieben, sondern deterministisch aus vorhandenen Daten (Transition Pattern + Leverage + injizierte Controls) vorbereitet — dann fachlich kuratiert (wie Gesetz→Parser→Obligation→Review)._")
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w("")
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_kp = drafts_from_pattern(CP) if CP else [] # CP = convergence pattern (already loaded)
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w("**Aus 1 Pattern → %d Playbook-Entwürfe** (`status: draft_generated`): eigene Felder (Warum/schließt/Nachweise) aus den Daten gefüllt, der Experte ergänzt nur Tools/Prozess/How-others." % len(_kp))
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w("")
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_kd = next((d for d in _kp if d.capability_id == "sbom_creation"), _kp[0] if _kp else None)
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if _kd:
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w("**Beispiel-Entwurf — `%s`** _(%s)_" % (_kd.capability_id, _kd.status.value))
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w("- **Warum** (aus Pattern): %s" % _kd.why.strip())
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w("- **schließt** %s · **Nachweise** %s" % ("+".join(_kd.closes_regulations) or "—", ", ".join(_kd.expected_evidence) or "—"))
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w("- **Provenance:** %s" % ", ".join("%s←%s" % (k, v) for k, v in _kd.provenance.items()))
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w("- **TODO (Experte/Offline-Propose):** %s" % ", ".join(_kd.todo))
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w("")
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w("_So reviewt der Experte %d Entwürfe statt %d Playbooks zu schreiben. Derselbe Generator bereitet später ISO14001-/IATF-Entwürfe vor, sobald der Corpus da ist._" % (len(_kp), len(_kp)))
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w("")
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coverage_table([
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("Playbook Draft Generator (deterministisch)", "PASS", "%d Entwürfe aus 1 Pattern, kein LLM im Kern" % len(_kp)),
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("Provenance + TODO + Freigabestatus", "PASS", "draft_generated→reviewed→validated→proven"),
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("Draft-Generatoren neue Domänen (Phase A)", "TODO", "Transition-/Reference-Scenario-Drafts"),
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])
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# ── Epics + roll-up ───────────────────────────────────────────────────────
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w("## Gaps → Epics (Backlog — nur erfasst, NICHT implementiert)")
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w("")
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@@ -296,6 +296,28 @@ _Derselbe Capability-Strang, neuer Renderer: aus Diagnose wird Beratung. Die `fe
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| Roadmap → Playbook (Verkettung) | **PASS** | 2/12 Maßnahmen mit Playbook |
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| Playbook-Inhalt (Knowledge) | **TODO** | 10 Capabilities brauchen noch Inhalt |
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## Knowledge Production — Playbook-Entwürfe automatisch assemblieren
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_Der Engpass ist nicht Content, sondern Wissensproduktion. Der Corpus wird nicht von Hand geschrieben, sondern deterministisch aus vorhandenen Daten (Transition Pattern + Leverage + injizierte Controls) vorbereitet — dann fachlich kuratiert (wie Gesetz→Parser→Obligation→Review)._
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**Aus 1 Pattern → 12 Playbook-Entwürfe** (`status: draft_generated`): eigene Felder (Warum/schließt/Nachweise) aus den Daten gefüllt, der Experte ergänzt nur Tools/Prozess/How-others.
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**Beispiel-Entwurf — `sbom_creation`** _(draft_generated)_
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- **Warum** (aus Pattern): CRA requires an SBOM; MaschinenVO does not.
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- **schließt** CRA · **Nachweise** sbom
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- **Provenance:** why←transition_pattern:why_asked, closes_regulations←leverage:covers_targets, expected_evidence←transition_pattern:expected_evidence
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- **TODO (Experte/Offline-Propose):** tools, process_steps, how_others_do_it
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_So reviewt der Experte 12 Entwürfe statt 12 Playbooks zu schreiben. Derselbe Generator bereitet später ISO14001-/IATF-Entwürfe vor, sobald der Corpus da ist._
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**Architecture Coverage**
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| Layer | Status | Hinweis |
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|---|---|---|
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| Playbook Draft Generator (deterministisch) | **PASS** | 12 Entwürfe aus 1 Pattern, kein LLM im Kern |
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| Provenance + TODO + Freigabestatus | **PASS** | draft_generated→reviewed→validated→proven |
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| Draft-Generatoren neue Domänen (Phase A) | **TODO** | Transition-/Reference-Scenario-Drafts |
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## Gaps → Epics (Backlog — nur erfasst, NICHT implementiert)
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| Epic | Titel | schliesst Coverage-Luecke |
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@@ -307,6 +329,6 @@ _Derselbe Capability-Strang, neuer Renderer: aus Diagnose wird Beratung. Die `fe
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## Suite-Status (Roll-up)
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- Coverage-Zellen gesamt: **35**
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- PASS: **26** · PARTIAL: 3 · UNSUPPORTED: 1 · TODO: 4 · N/A: 1 · NEEDS_FACTS: 0
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- Coverage-Zellen gesamt: **38**
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- PASS: **28** · PARTIAL: 3 · UNSUPPORTED: 1 · TODO: 5 · N/A: 1 · NEEDS_FACTS: 0
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- Fortschritt = PASS-Anteil steigt, wenn Epics RS-001…004 landen (objektiver Maßstab, kein LOC).
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"""Tests for Knowledge Production — the Playbook Draft Generator.
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Acceptance: deterministically assemble a playbook DRAFT for a capability from a transition-pattern
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delta requirement (why / closes / evidence with provenance), leaving practitioner know-how as an
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explicit TODO; turn a whole pattern into one draft per delta capability. No LLM, fully deterministic.
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The expert reviews drafts instead of writing from a blank page.
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"""
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from __future__ import annotations
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from compliance.knowledge_production import (
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DraftStatus, PlaybookDraft, drafts_from_pattern, generate_playbook_draft,
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)
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REQ = {
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"capability": "sbom_creation",
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"why_asked": "CRA requires an SBOM; MaschinenVO does not.",
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"covers_targets": ["CRA"],
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"expected_evidence": ["sbom"],
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}
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CONV_REQ = {
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"capability": "product_cyber_risk_assessment",
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"why_asked": "Both require assessing cyber threats.",
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"covers_targets": ["CRA", "MaschinenVO"],
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"expected_evidence": ["product_risk_assessment"],
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}
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def test_assembles_owned_fields_with_provenance():
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d = generate_playbook_draft("sbom_creation", REQ, control_links=["component_inventory"])
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assert d.status == DraftStatus.DRAFT_GENERATED
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assert d.why.startswith("CRA requires an SBOM")
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assert d.closes_regulations == ["CRA"] and d.expected_evidence == ["sbom"]
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assert d.typical_controls == ["component_inventory"]
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assert d.provenance["why"] == "transition_pattern:why_asked"
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assert d.provenance["closes_regulations"] == "leverage:covers_targets"
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assert d.provenance["typical_controls"] == "execution:control_links"
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def test_soft_fields_are_todo():
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d = generate_playbook_draft("sbom_creation", REQ)
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assert d.todo == ["tools", "process_steps", "how_others_do_it"] # practitioner know-how owed
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def test_missing_owned_fields_go_to_todo():
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d = generate_playbook_draft("x", {})
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assert "why" in d.todo and "expected_evidence" in d.todo
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assert d.closes_regulations == [] and d.typical_controls == []
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assert d.status == DraftStatus.DRAFT_GENERATED
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def test_missing_because_fallback_for_why():
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d = generate_playbook_draft("x", {"missing_because": "no analogue in ISO 27001"})
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assert d.why == "no analogue in ISO 27001" and "why" not in d.todo
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def test_closes_deduped_sorted_and_title_humanised():
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d = generate_playbook_draft("secure_signed_update_distribution", {"covers_targets": ["MaschinenVO", "CRA", "CRA"]})
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assert d.closes_regulations == ["CRA", "MaschinenVO"]
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assert d.title == "secure signed update distribution"
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def test_controls_default_empty_no_execution_data():
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d = generate_playbook_draft("x", REQ)
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assert d.typical_controls == [] # nothing injected -> empty
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def test_drafts_from_pattern_one_per_delta_in_order():
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pattern = {"delta_requirements": [REQ, CONV_REQ]}
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drafts = drafts_from_pattern(pattern)
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assert [d.capability_id for d in drafts] == ["sbom_creation", "product_cyber_risk_assessment"]
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assert drafts[1].closes_regulations == ["CRA", "MaschinenVO"] # leverage 2 carried through
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def test_drafts_from_pattern_injects_controls_and_skips_unnamed():
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pattern = {"delta_requirements": [REQ, {"why_asked": "no capability key"}]}
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drafts = drafts_from_pattern(pattern, control_links_by_cap={"sbom_creation": ["c1"]})
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assert len(drafts) == 1 and drafts[0].typical_controls == ["c1"] # entry without capability skipped
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def test_deterministic():
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pattern = {"delta_requirements": [REQ, CONV_REQ]}
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a = [(d.capability_id, d.why, tuple(d.todo)) for d in drafts_from_pattern(pattern)]
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b = [(d.capability_id, d.why, tuple(d.todo)) for d in drafts_from_pattern(pattern)]
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assert a == b
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def test_returns_playbookdraft_type():
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assert isinstance(generate_playbook_draft("x", REQ), PlaybookDraft)
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@@ -0,0 +1,55 @@
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# ADR-005: Knowledge Production — prepare deterministically, then curate
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- **Status:** Accepted
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- **Datum:** 2026-06-27
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- **Typ:** Architektur-Entscheidung
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- **Bezug:** [ADR-004](ADR-004-implementation-playbooks.md), [ADR-002](ADR-002-transition-is-data-not-architecture.md), Architektur-Freeze v1.0, [[transition-reasoning]], [[iace-quality-architecture]]
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## Kontext
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Mit Capability Delta, Optimization und Playbooks ist die Diagnose weitgehend fertig. Der nächste
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Engpass ist NICHT „Content" (mehr Playbooks schreiben), sondern **Wissensproduktion**: würde man
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200 Playbooks (und je Domäne neue Patterns/Reference-Szenarien) von Hand schreiben, verlagerte sich
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der Engpass dauerhaft vom Engineering auf manuelle Wissenspflege.
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Der entscheidende Grundsatz war: **„Die Engine ändert sich nicht. Der Corpus wächst."** Diese ADR
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ergänzt ihn:
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> **„Und der Corpus wird nicht manuell geschrieben. Er wird deterministisch vorbereitet und
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> anschließend fachlich kuratiert."**
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## Entscheidung
|
||||
|
||||
1. **BreakPilot produziert künftig keine fertigen Wissensartefakte, sondern ENTWÜRFE.** Ein Draft
|
||||
Generator strukturiert deterministisch aus Daten, die die Software bereits besitzt
|
||||
(Capability, Transition Pattern, Controls, Evidence, Regulatory Map / Leverage), einen Entwurf —
|
||||
und überlässt das Fachwissen der menschlichen Kuratierung.
|
||||
|
||||
2. **Spiegelung der Legal-Pipeline.** Wie `Gesetz → Parser → Obligation → Review` gilt jetzt
|
||||
`neue Capability → Registry → Transition Pattern → Playbook Draft Generator → Expert Review →
|
||||
versioniertes Playbook`. Dieselbe Logik für jedes Wissensartefakt (Playbooks, später Transition
|
||||
Patterns, Reference-Szenarien).
|
||||
|
||||
3. **Deterministisch-first (kein LLM im Kern).** Der Generator assembliert nur, was die Software
|
||||
besitzt; weiche Felder (Tools / Prozessschritte / „wie machen das andere") werden als **TODO**
|
||||
ausgewiesen. Optionale Modell-Anreicherung bleibt **offline, advisory, propose-only** — nie im
|
||||
deterministischen Kern (vgl. [[iace-quality-architecture]]).
|
||||
|
||||
4. **Freigabestatus.** Jedes Artefakt trägt einen Lifecycle
|
||||
`draft_generated → in_review → reviewed → validated → proven` plus **Provenance je Feld**
|
||||
(woraus es assembliert wurde) — Voraussetzung für Review-Workflow und Versionierung.
|
||||
|
||||
## Konsequenzen
|
||||
|
||||
- **Review statt Schreiben:** der Experte reviewt N Entwürfe statt N Artefakte zu schreiben — der
|
||||
manuelle Aufwand sinkt massiv, ohne fachliche Kontrolle aufzugeben.
|
||||
- **Neue Domänen werden billig:** sobald ein Domänen-Corpus (z. B. Umwelt) existiert, erzeugt
|
||||
derselbe Generator erste Entwürfe — ISO 14001 wird ein Draft-+-Review-Problem, kein Schreibprojekt.
|
||||
- **Internes Werkzeug:** die wertvollste Maschine ist nicht nur das Kunden-OS, sondern die
|
||||
**Produktionsmaschine für das eigene regulatorische Wissen** — sie wird mit jeder Domäne wertvoller.
|
||||
- **Freeze-konform:** kein neues Metamodell, kein Graph, kein neuer Corpus. `compliance/knowledge_production`
|
||||
ist eine reine, deterministische Vorbereitung (computed-not-stored); Execution-Controls werden injiziert.
|
||||
- **Phase A (Wissensproduktion) VOR Phase B (neue Domänen):** Draft-Generatoren (Playbook ✓, dann
|
||||
Transition-Pattern, Reference-Szenario) + Review-Workflow + Versionierung + Freigabestatus, dann
|
||||
ISO 14001 / IATF 16949 / IEC 62443.
|
||||
- Diese ADR ist non-runtime → kein Deploy (siehe [ADR-001](ADR-001-runtime-deploy-policy.md)).
|
||||
Reference in New Issue
Block a user