Every tool in the toolbox, navigable by what you do.

Where your analytics practice actually stands — and the one constraint holding it there.

In

{ practice, cluster? }

Out

{ practice_summary, dimensions[5] (stage · evidence · gaps), overall (stage · binding_constraint), roadmap[], grounded_in, provenance }

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How big is the effect really — and was the study even powered to find it?

In

{ groups?|from_t?|from_f?, target_power?, context?, cluster? }

Out

{ computation (d · CI · r-equivalent · power · n-required · eta² · f), interpretation (narrative · caveats), grounded_in, provenance }

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Paste engagement results — get an action plan (the #1 thing surveys lack).

In

{ results, cluster? }

Out

{ results_summary, themes[]{theme, signal, likely_drivers[]}, priority_actions[]{action, level, owner_hint, timeframe}, comms_plan[], pitfalls[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

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Classify any free-text response against labeled exemplars — predicted class + calibrated confidence.

In

{ response, exemplars? | exemplarSetId?, semanticWeight?, topK? } — inline labeled exemplars OR a built-in set id ('interview-orientation')

Out

{ predictedClass, confidence, margin, classScores[], nearestExemplars[], features{weintraub,mehrabian}, embedding{mode,degraded} }

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Stop spreading talent investment like peanut butter — find the pools where it changes the game.

In

{ organization, strategic_goal, current_practices? }

Out

{ talent_pools[] (pivotal|important|foundational · rationale · grounded_in), bridge[] (impact/effectiveness/efficiency · linkage · breaks), weakest_link, recommendations[], measures_to_watch[], valuation_note, grounded_in, provenance }

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Find out what your HR data can and can't answer — before the analysis embarrasses you.

In

{ dataset, intended_analyses?, cluster? }

Out

{ dataset_summary, dimensions[] (findings · cannot_assess), fitness_for_purpose[] (verdict · blocking_issues), remediation_plan[], needs_profiling[], grounded_in, provenance }

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Put people risk on the same register as every other risk the board reads.

In

{ organization, context?, known_concerns? }

Out

{ risks[] (category · likelihood/impact · rationale · early_indicators · mitigations · suggested_owner), priority_read, blind_spot, operating_rhythm[], grounded_in, provenance }

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Prove training changed behavior and results — not just that people enjoyed it.

In

{ program, context?, include_roi?, financials? (program_cost · benefit_streams · isolation_adjustment), cluster? }

Out

{ program_summary, levels[1..4] (measures · instruments · timing · success_indicator), chain_of_evidence, level5? (benefit_streams · computed ROI%/BCR · honesty_notes), roi_level5?, caveats[], grounded_in, provenance }

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Find out whether your dashboard will change anything — before you ship it.

In

{ initiative, audience?, decision? }

Out

{ anchors[] (verdict: strong|partial|weak|absent · evidence · gaps · fixes · grounded_in), binding_constraint, overall_verdict, riskiest_assumption, grounded_in, provenance }

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A model card and evaluation audit for any people-data model — documented like it should have been on day one.

In

{ model_description, cluster? }

Out

{ model_summary, card[6 sections], audit[6 checks], priority_gaps[], grounded_in, provenance }

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Describe a team — get a 9-box talent grid with per-person actions.

In

{ team, cluster? }

Out

{ team_summary, placements[]{name, performance, potential, box, action}, box_summary[]{box, who[], strategy}, calibration_notes[], riskiest_assumptions[], grounded_in, provenance }

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Turn strategy context into a quarter-ready OKR set that measures outcomes, not activity.

In

{ context, level?: company|team|individual, objectives?, cluster? }

Out

{ level, context_summary, okr_sets[] (objective · rationale · key_results · grounded_in · strength), anti_patterns[], cadence, grounded_in, provenance }

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Describe a role — get a 30-60-90 onboarding plan.

In

{ role, cluster? }

Out

{ role_summary, phases[]{phase, goals[], activities[], success_signal}, stakeholders_to_meet[], resources_needed[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

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Is the org designed for its strategy? Star Model alignment plus spans-and-layers, with the math done in code.

In

{ design, headcount_by_layer?, cluster? }

Out

{ design_summary, star_points[5] (assessment · misalignments), realignment[], spans_and_layers (per-layer spans · flags) | null, data_flags[], grounded_in, provenance }

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Crediting rules that end the split disputes — every rule carries the dispute it prevents.

In

{ plan_context, roles?, cluster? }

Out

{ plan_summary, principles[], rules[7 scenarios] (rule · rationale · dispute_prevented · determinable), double_crediting_budget, data_flags[], grounded_in, provenance }

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A survey scale and self-assessment for any management concept — drafted to psychometric standards, honest that it's a draft.

In

{ concept, definition?, context?, cluster? }

Out

{ construct (refined_definition · not_to_be_confused_with), sub_dimensions[], items[6-12] (keying · dimension), response_scale, self_assessment, adjacencies[], caveats[], handoff, grounded_in, provenance }

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Upload a home-grown survey scale (items + a response matrix) — get a psychometric credibility report you can defend.

In

{ scaleName, items: [{ id?, text }], responses: number[][], groups?, referenceGroup?, cluster? } — responses is a respondents × items matrix; reverse-scored items already recoded

Out

{ scaleName, nItems, nRespondents, nDropped, reliability (cronbachAlpha · mcdonaldOmega · meanInterItemCorrelation · tier), dimensionality (eigenvalues · nFactorsKaiser · firstFactorShare · unidimensional), items[] (itemTotalCorrelation · alphaIfDeleted · loading · flags · recommendation), dif (Mantel-Haenszel · ETS A/B/C · flagged[]), verdict, narrative, grounded_in, provenance }

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Turn a psychometric score into a plain-language explanation tailored to who's reading it.

In

{ decomposition{ dimensions[], overall?, percentile?, reliability?, sem?, flags?, reference_cohort? }, audience, detail_level?, format?, instrument_context? }

Out

{ audience, explanation, key_points[], what_if[], caveats[], grounded_in (dimensions/flags cited), provenance }

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Describe a team + goal — get rated skill gaps with build/buy + a learning plan.

In

{ context, cluster? }

Out

{ context_summary, target_capabilities[], current_strengths[], gaps[]{skill, severity, build_or_buy, action}, learning_plan[], hiring_priorities[], riskiest_assumptions[], grounded_in, provenance }

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Stop guessing what moves the needle — see what actually does, with the math to back it.

In

{ outcome_key, records?|precomputed?, driver_keys?, context?, cluster? }

Out

{ computation (ranked drivers · CIs · verdicts · warnings), interpretation (narrative · do_not_conclude · next_steps), grounded_in, provenance }

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Enter a role and a few numbers — get a fully-loaded, cited cost of turnover and the retention business case.

In

{ segment, annualSalary?, headcount?, annualSeparations?|turnoverRatePct?, recruitingPctOfSalary?, onboardingPctOfSalary?, rampMonths?, rampProductivityLossPct?, vacancyDays?, knowledgeLossPctOfSalary?, absenceDaysPerYear? }

Out

{ segment_summary, components[] (formula · assumption · per_separation), per_separation_total, separations, segment_annual_total, absenteeism_annual, grand_total, sensitivity_drivers[], interpretations[], business_case, grounded_in, provenance }

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The dollar value of a better hire or a real program effect — computed, not vibed.

In

{ mode: selection|program, selection?|program?: {...}, context?, cluster? }

Out

{ computation (ΔU · ROI · break-even · sensitivity), interpretation (narrative · caveats · assumptions_to_challenge), grounded_in, provenance }

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Describe an org + goals — get a demand/supply/gap workforce plan.

In

{ context, cluster? }

Out

{ context_summary, demand[], supply[], gaps[]{gap, severity, approach}, build_buy_borrow{build[], buy[], borrow[]}, risks[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

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