Conciseness

Telegraphic, assumes Claude's competence, and contains no explanations of concepts Claude already knows — but endpoint facts, name-check rules and agent-name conventions each appear in two or three places (Phase 3 step 4, the AI Signals note, and the Quick Reference), and the Phase Summary table overlaps the per-phase sections. Efficient with minor trimming opportunities — anchor 4, not 5, because not every token earns its place given that duplication.

Actionability

Provides copy-paste-ready commands ('tdx tables "<database>.*"', a full information_schema SQL query for classification), exact file paths, config keys ('ml_batch_api_endpoint', 'input_params.yml'), named output tables, and explicit placeholder conventions. Detail is delegated to real reference files with precise section pointers, covering the common cases fully.

Workflow Clarity

Multi-phase workflow with explicit sequencing, entry/resume routing, approval gates ('ask once to push, run and validate'), and explicit validation with feedback loops ('If check 1 shows a missing source or no conversions… fix the config and re-run before continuing'), plus re-push/re-run/re-validate and kit-refresh loops in Phase 4 and a DEV→PROD gate. Matches the validation-with-feedback-loops anchor; the batch-operation cap does not apply because validation is present and explicit.

Progressive Disclosure

The body is a clear overview with well-signaled one-level-deep references — every reference in the body (workflow-setup.md, ai-signals-guide.md, requirements_doc.md, agent-setup.md, docs-setup.md) exists in references/, and each mention states when to read it ('It is not needed for the standard setup steps (Phases 1–3)'). No bulk reference material is inlined in the body.

Specificity

Lists multiple concrete actions — 'builds customer journeys from marketing touchpoints and conversions, runs the standard attribution models and the AI Signals Markov and Shapley models, then builds the project's MTA agent and dashboard' — plus configure/change/re-run/resume operations. Comprehensive coverage with no gaps, matching the score-5 anchor rather than the minor-gaps anchor at 4.

Completeness

Explicitly answers both: what ('a workflow that builds customer journeys… runs the standard attribution models and the AI Signals Markov and Shapley models, then builds the project's MTA agent and dashboard') and when ('Use it to start, set up, configure, change, re-run or resume an MTA project… Trigger on: …'). Exactly the score-5 anchor with concrete trigger phrases; not 4 since 'when' is fully explicit.

Trigger Term Quality

Explicit trigger list covers natural synonyms and variations users would say: 'set up MTA', 'marketing attribution setup', 'multi-touch attribution setup', 'attribution workflow', 'measure which channels drive conversions', 'MTA runbook', 'MTA output tables'. Matches the comprehensive-synonyms anchor, not the few-missing-terms anchor at 4.

Distinctiveness Conflict Risk

Clear niche (MTA on Treasure Data with named Markov/Shapley models and MTA-prefixed triggers) plus explicit de-confliction guidance ('Once the project's MTA agent is installed, questions about channel contribution and journeys go to it'). Minimal conflict risk — the score-5 anchor.