perf(pairing): trim always-on frontmatter and dedupe body prose

[liwenjie200543]

Summary

• Applies the Optimize skill token cost across all families (setup-family recipe) #1342 setup-family optimization recipe to the pairing skill family (Optimize the pairing skill family #1352). Both pairing skills were over the 200-token always-on budget — description + when_to_use are paid in every session, for every skill, invoked or not.
• pairing-multi-agent-review: always-on ~230 → ~172 tokens (chars÷4 estimate); measured body tokens 3,686 → 3,467.
• pairing-self-review: always-on ~209 → ~170; measured body 3,437 → 3,377.
• The body pass dedupes prose that repeated the frontmatter description, the per-pass scopes, and golden-rule bodies. Headings, golden-rule headlines, code blocks, and the pre-flight block are byte-identical; all quoted trigger phrases in when_to_use are preserved.

Test plan

• uv run --project tools/skill-token-count skill-token-count — stamps current, exit 0 (restamped via --write in the same PR).
• uv run --project tools/skill-and-tool-validator --group dev skill-and-tool-validate — exit 0. The only warnings (override-contract, no-telemetry-import) are pre-existing on files this PR does not touch.
• python tools/dev/skill-surface-hash.py — reports "surface_hash in sync across 78 skills", i.e. all structural anchors are unchanged.
• Eval-coupled strings verified present (e.g. the Pass A injection-guard finding text).
• The behavior eval suites for both skills (15 + 20 cases) require claude -p and were not run in my environment — noted here per the Optimize skill token cost across all families (setup-family recipe) #1342 recipe's "run evals before and after" step. Happy to run them locally on request, or maintainer run appreciated before merge.

Gen-AI disclosure

Prepared with AI coding assistance (ZCode); changes reviewed, tested, and committed by me.