fix(F07): propagate recorded corrections into active writing sources

The review's key point is that the error ran BOTH ways.

Overstatement, narrowed to canonical scope:
- BS-1 variants claimed "eliminated manual wafer inspection", "no manual
  intervention" and "unattended quality monitoring". Canonical BS-1 supports a
  qualitative reduction whose size is unverified. Reworded; the scope limit is
  now stated inline where the variants are written.
- bundle_data_platform called BS-1 a "zero-downtime constrained deployment".
  Integration into a fab that runs continuously is not a measured zero-downtime
  property.
- experience_swisscom carried "grounded retrieval" as a skill and ATS keyword
  while SW-8 records retrieval implementation as explicitly unknown, and its
  reframing note invited "evaluation discipline" although formal model
  evaluation is a globally forbidden pattern. Both removed.
- PyTorch sat in bundle_ml_ai_engineer as a bold tool and an ATS must-include
  with no certification context - the exact way the context gets lost.

Undervaluation, the half that quietly costs him roles:
- BS-6 (Linux administration, intensive Ansible incl. self-written extensions)
  has been canonical since 2026-08-27 but appeared in NO bundle, so the normal
  selection paths could not see it. Added to the data-platform and data-engineer
  bundles with its currency limit (employer-side ended Dec 2022; PP-3 is
  personal) and the standing GitOps prohibition.
- Linux, Ansible and Bash were missing from skills_taxonomy entirely.
- skills_taxonomy still said the IBM certification year was "not recorded" and
  that PyTorch needed verification; both were settled on 2026-08-27.

Not rewritten, deliberately: the NVIDIA and RUAG cover letters state the
production line could not be stopped. That exceeds the canonical record but is
plausible and both are already submitted. Recorded as an OPEN ITEM in
experience_bosch.md to be confirmed with the user before any reuse.

Validator PASS, 31/31 tests.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qej5SBvhcbGXHwLqNWiskM

resume_builder/experience/experience_swisscom.md

@@ -163,8 +163,9 @@
 
 **Metadata/catalogue platform (user-confirmed 2026-07-29):** Swisscom uses **Atlassian Compass** as the metadata platform for data products, including lineage. Dennis uses it as a practitioner to document data-product metadata and lineage. **Do NOT claim** he administers, rolled out or owns Compass. This is a genuine named catalogue/lineage tool and satisfies "or similar metadata/catalogue platform" JD phrasing — but it is **not** a substitute claim for Purview, Collibra or Alation, which remain unevidenced and forbidden.
 
-**Key skills:** Data Mesh, data products, metadata management, data lineage, Atlassian Compass, data catalog, data governance, AWS, Glue, Athena, CloudFormation, AWS CLI, CI/CD, agentic data foundation, grounded retrieval
-**ATS keywords:** Data Mesh, data products, metadata management, AWS, Glue, Athena, CloudFormation, CI/CD, data governance, grounded retrieval, agentic AI foundation
+**Key skills:** Data Mesh, data products, metadata management, data lineage, Atlassian Compass, data catalog, data governance, AWS, Glue, Athena, CloudFormation, AWS CLI, CI/CD, agentic data foundation
+**NOT a skill here:** *grounded retrieval* was listed until 2026-09-11 but no retrieval implementation is evidenced — SW-8 records it as explicitly unknown. Describe the data side (governed, documented, lineage-tracked data that agents can consume), never retrieval work.
+**ATS keywords:** Data Mesh, data products, metadata management, AWS, Glue, Athena, CloudFormation, CI/CD, data governance, agentic AI foundation
 **Reframing notes:**
 - ML/AI: MED — evidence for data readiness and grounded enterprise data, not agent architecture or retrieval ownership
 - Data Platform/Infra: HIGH — Data Mesh + metadata + AWS IaC is core platform signal
@@ -186,7 +187,7 @@
 **Key skills:** LLM application configuration, model selection, AI agents, domain knowledge bases, question answering, migration assistance, data mapping
 **ATS keywords:** LLM-powered applications, AI agents, model selection, knowledge grounding, question answering, workflow automation, data mapping
 **Reframing notes:**
-- ML/AI: HIGH for LLM-specific roles; lead with applied LLM delivery, then connect reliable data foundations and evaluation discipline.
+- ML/AI: HIGH for LLM-specific roles; lead with applied LLM *configuration*, then connect reliable data foundations. **Do not invoke evaluation discipline** — no LLM evaluation responsibility is evidenced and *formal model evaluation* is a globally forbidden pattern.
 - Accuracy: Never say “trained an LLM” unless model weights were fine-tuned. Prefer “grounded,” “configured,” or “provided with a domain-specific knowledge base.”
 - Production: Use “internal” or “prototype” only if true; otherwise omit deployment-status language until verified.