Conciseness

The body is dense and operational with no concept primers (nothing explains what attribution or a workflow is), and nearly every clause is an instruction. A few passages could still be trimmed — the once-per-conversation re-read rule ("when this skill is invoked again later in the same conversation… don't read them again; in a new session…"), the generic Treasure AI Studio routing paragraph, and Phase 3 step 4's seven-clause sentence — which fits "efficient; minor instances of over-explanation" rather than the every-token-earns-its-place anchor.

Actionability

Guidance is fully executable: copy-paste commands ("tdx tables \"<database>.*\"", the information_schema.columns query), exact paths and defaults ("workflow/config/input_params.yml", sink database "td_agents", project "mta_ais"), and precise section-level pointers into real reference files (e.g. "references/workflow-setup.md §1" for the discovery queries, §4 for validation). Every step says exactly what to do and where the exact command or detail lives, with no vague direction.

Workflow Clarity

The five phases are clearly sequenced with explicit checkpoints and feedback loops: source confirmation is mandatory ("Do not put the table inside the question. Do not skip this."), push requires one approval, and validation has a named fix-retry loop — "If check 1 shows a missing source or no conversions, or check 5 shows empty model output, fix the config and re-run before continuing" — plus re-validate on every Phase 4 iteration and a user gate before Phase 5. This matches the top anchor including error recovery for the batch workflow runs.

Progressive Disclosure

The 131-line body is an orchestration overview (phase table, per-phase context, How It Works, Quick Reference) with all detail split into the five real bundle files under references/, each cited with exact section numbers that verify against the actual files (workflow-setup §1–§4, ai-signals-guide §3, agent-setup, docs-setup, requirements_doc). References are one level deep and clearly signaled at the point of use, matching the well-signaled top anchor.

Specificity

The description lists multiple concrete actions covering the whole lifecycle: "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 "start, set up, configure, change, re-run or resume an MTA project" and "build or rebuild its agent". Nothing is generic or padded, matching the comprehensive-coverage anchor; a 4 would require minor coverage gaps, which are absent.

Completeness

Both questions are answered explicitly: the "what" in the first two sentences (journeys, standard + Markov/Shapley models, agent and dashboard) and the "when" in "Use it to start, set up, configure, change, re-run or resume an MTA project… Trigger on: …" with concrete trigger phrases. This matches the top anchor exactly; a 4 would require the "when" to be less explicit, which it is not.

Trigger Term Quality

The explicit trigger list covers natural phrasings and their synonyms: "set up MTA", "marketing attribution setup", "multi-touch attribution setup", "ai signals mta", "attribution workflow", "rebuild the MTA agent", "resume MTA setup", "MTA runbook", "MTA output tables", plus the goal-language variant "measure which channels drive conversions". Coverage of what users would actually say is comprehensive; a 4 would mean a few natural terms missing, and none are apparent.

Distinctiveness Conflict Risk

It occupies a clear niche — MTA on Treasure Data — with distinct, domain-specific triggers, and even adds a boundary rule that reduces mis-triggering: "Once the project's MTA agent is installed, questions about channel contribution and journeys go to it". Minimal conflict risk with other skills; the 4 anchor's "minor overlap risk" does not apply.