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/bundles/bundle_ml_ai_engineer.md

@@ -71,14 +71,16 @@
 ## S4: Skills Guide
 
 **Bold tools (resume Technical Skills section):**
-Python, Kubernetes, Docker, AWS (S3 · Glue · Athena · Airflow), Kafka, PyTorch · Scikit-learn
+Python, Kubernetes, Docker, AWS (S3 · Glue · Athena · Airflow), Kafka, Scikit-learn
+*(PyTorch and TensorFlow/Keras are **certification-context-only** in `claims.json` — list them only beside the IBM AI Engineering certificate, never as production tooling.)*
 
 **Must-include skills (ATS match):**
 - Python, MLOps, ML inference deployment
 - Docker, Kubernetes, Ansible
 - AWS (S3, Glue, Athena, Redshift, Airflow), Apache Iceberg
 - Apache Kafka
-- PyTorch, Scikit-learn, Pandas, NumPy
+- Scikit-learn, Pandas, NumPy
+- PyTorch / TensorFlow **only with certification context** — see the note above; a bare ATS listing is the exact way the context gets lost
 - CI/CD (GitLab, Jenkins)
 
 **Nice-to-have (include if JD mentions):**