Conciseness

Lean and efficient throughout — it never explains concepts Claude already knows (no Monte Carlo, percentile, or agile primers), and the scenario-file comments earn their tokens by encoding non-obvious model semantics ('history already contains today's tax, so execute durations scale by dependency_tax / dependency_tax_current'). Not 4: no section is trimmable padding; stylistic phrasing ('Read it like a portfolio manager, not a lab') carries directive weight.

Actionability

Mostly executable: a full command in step 1 ('python3 <this-repo>/skills/portfolio-pdlc/scripts/portfolio_sim.py <workspace>') and a complete, copy-paste-ready scenario file with exact knob names. Not 5: step 3's command abbreviates the path ('python3 .../portfolio_sim.py <workspace> --scenario …') so it is not literally copy-paste ready, and no sample output is shown.

Workflow Clarity

Five clearly sequenced steps with explicit validation checkpoints and feedback loops: the conditional priors dialogue when 'most stages run on priors', the run-to-run noise comparison in step 4 ('A delta smaller than the run-to-run noise… is a legitimate, decision-relevant finding'), the fixed-seed/--seed noise protocol, and a Quality Gates checklist for closure. Matches the anchor-5 pattern of sequence + validation + checklist.

Progressive Disclosure

Well-organized overview structure (Outcome → When → Workflow → format → Rules → Quality Gates → References) with detail deferred one level deep: flag details live in the script's '--help' and the References section clearly signals the script and the sibling skill. No content is inlined that belongs in a separate file — the scenario format is needed for the core workflow.

Specificity

Lists multiple concrete actions with comprehensive coverage: 'Run a deterministic Monte Carlo simulation of flow through the portfolio PDLC — baseline from the workspace's own flow history, plus what-if scenarios (WIP limit changes, arrival-rate shaping, dependency-tax reduction from platform extraction or team reorganization)', plus 'to set Service Level Expectations' and 'produces confidence-labeled forecasts, never point predictions'. Not 4: coverage of the skill's capabilities has no gaps.

Completeness

Explicitly answers both: what ('Run a deterministic Monte Carlo simulation… produces confidence-labeled forecasts, never point predictions') and when ('Use as the Discovery move for improvement bets before anyone changes policies or reorganizes teams, or to set Service Level Expectations') — a 'Use as…' clause with concrete trigger phrases. The missing-'Use when' cap does not apply; not 4 because the when-clause is already explicit and specific.

Trigger Term Quality

Good natural-term coverage — 'Monte Carlo simulation', 'WIP limit', 'what-if scenarios', 'Service Level Expectations', 'platform extraction', 'team reorganization', 'improvement bets' — that the target user would plausibly say. Not 5: common variations a user might naturally say ('cycle time', 'throughput', 'forecast lead time') are absent; not 3: coverage goes well beyond 'some relevant keywords'.

Distinctiveness Conflict Risk

Clear niche (deterministic flow simulation / SLE forecasting for a portfolio PDLC) with distinct triggers; 'Advanced member of the portfolio-pdlc family' explicitly positions it against siblings rather than blurring them. Not 4: overlap risk with related skills is minimal given the simulation/discovery-specific triggers.