Execute Consulting Analysis

Execute Consulting Analysis

Create the transparent transformation between approved evidence and a derived result. Preserve the central lead's bounded test; do not decide the overall answer.

Use this skill only when the result materially depends on a calculation, sizing, score, benchmark transformation, experiment, diagnostic, business case, model, scenario, multi-source reconciliation, or sensitivity—not to repeat a reported number.

Run the analysis loop

1. Restate the test. Preserve the hypothesis, decision use, method boundary, and support/refute/inconclusive conditions. State what the analysis can and cannot establish.
2. Lock material inputs. Record decisive source/extract, version, fields, population, period, units, denominators, and transformations. Reuse the evidence skill's canonical minimum_access_request when a decision-changing input is inaccessible; do not reproduce a competing field specification.
3. Choose a readable method. Explain the formula, model, comparison, score, experiment, or solver; expose assumptions and decision-changing rules.
4. Run relevant checks. Reconcile totals, units, denominators, missingness, duplicates, joins, ranges, signs, formulas, and benchmark comparability. Test calibration, leakage, bias, confounding, or model validity when applicable.
5. Test sensitivity and rivals. Vary inputs/assumptions capable of changing the decision and test whether a simpler rival fits.
6. Rerun only affected logic when a qualified input changes; show the exact before/after input and result delta.
7. Return, do not adjudicate. State result, method, checks, sensitivity, limits, suggested hypothesis status, and conclusion boundary.

Apply decision rules

• A weighted score is not self-validating. Check construct fit, weighting rationale, missing-data policy, comparable scales, threshold logic, and weight sensitivity.
• A benchmark needs aligned population, scope, period, geography, currency, denominator, maturity, and method. Show a common-basis bridge or transparent bounded normalization when exact conversion is impossible; not comparable alone is not a result.
• For utilization, capacity, throughput, or constraint claims, compare the mechanism-matched views that may bind: nominal availability, staffed/startable window, cycle/start-slot capacity, and complementary-resource load. Reconcile observed throughput with the maximum implied starts/cycles; quantify any inconsistency.
• A model failing a required check cannot support its conclusion. A reversing sensitivity remains visible.
• A proxy is labeled and states which direct claim it cannot replace.
• For each derived benefit, cost, or exact decision threshold, label provenance as evidenced or judgment and state additive versus overlapping treatment. Do not turn a category share into causal addressability by multiplying it by a total unless the mechanism is evidenced; if used as a scenario, state the assumption and range. Untested option efficacy remains a hypothesis.

Return one compact note

Include test/decision use; input locators and definitions; method/formula; assumptions; checks and failures; result with units/scope; sensitivity and rival; limitation/blocked item; suggested support, refute, or inconclusive; any inherited restrictions; and, for reruns, the prior-result locator and before/after delta. Mark preliminary, directional, or non-comparable results honestly.

Load detail only when needed

• Read analysis-workflow.md for multi-step, rerun, or consequential models—not for a short transparent calculation.
• Read method-gates.md only for a specialized method needing an explicit feasibility/validity gate.
• Read quality-bar.md for consequential handoff or failed review.
• Reusable or consequential models may need organization-specific frozen-input, permission, lineage, and handover controls; do not manufacture that overhead for ordinary analysis.