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

Turn a scary relative-risk headline into the actual absolute risk, before and after.

In

{ outcome, claim?, baseline_count, baseline_per, relative_multiplier, cluster?, model? }

Out

{ baseline, adjusted, absolute_change_pp, change_per_baseline, number_needed, framing, worth_worrying, grounded_in, provenance }

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Describe an offering — get ad angles + ready-to-test variations for your channel.

In

{ offering, channel?, cluster? }

Out

{ offering_summary, angles[]{angle, why}, variations[]{angle, headline, primary_text, cta}, channel_notes, test_plan[], riskiest_assumptions[], grounded_in, provenance }

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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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Describe a business — get growth options across the Ansoff matrix.

In

{ business, cluster? }

Out

{ business_summary, quadrants[]{quadrant, risk, moves[]}, recommended_focus, riskiest_assumptions[], grounded_in, provenance }

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Get the technical blueprint — architecture, stack, components, phases — for any product idea.

In

{ text, sourceType?: deep_research|book|research, sourceId?, title?, author? }

Out

ApplicationDesignCanonical[] (architecture · stack · data model · features · flows · phases)

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Describe a decision — get a clean Architecture Decision Record (Nygard form).

In

{ decision, cluster? }

Out

{ decision_summary, title, status, context, decision, options_considered[]{option, pros[], cons[]}, consequences{positive[], negative[]}, riskiest_assumptions[], grounded_in, provenance }

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A strategy map with causal logic and few measures — not a wall of KPIs.

In

{ organization, strategy, current_metrics? }

Out

{ strategy_map[] (perspective · objective · drives), scorecard[] (measures ≤2 · target_guidance · initiatives), metric_dispositions[], exec_pay_tie_in, pitfalls[], cascade_note, grounded_in, provenance }

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Describe a crowded market — get a Blue Ocean ERRC grid + value innovation.

In

{ market, cluster? }

Out

{ market_summary, errc{eliminate[], reduce[], raise[], create[]}, value_innovation, new_demand, tagline, riskiest_assumptions[], grounded_in, provenance }

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Describe a brand — get a voice & tone guide with do/don't and sample rewrites.

In

{ brand, cluster? }

Out

{ brand_summary, voice_attributes[]{attribute, description, do[], dont[]}, tone_by_context[]{context, tone}, vocabulary{use[], avoid[]}, sample_rewrites[]{before, after}, riskiest_assumptions[], grounded_in, provenance }

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Surface and structure the venture concepts buried in research, books, or briefs.

In

{ text, sourceType?: deep_research|book|research, sourceId?, title?, author? }

Out

BusinessIdeaCanonical[] (value prop · target market · business model · revenue streams · key features)

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Describe a venture — get a drafted, pressure-tested Business Model Canvas (or Lean Canvas).

In

{ venture, mode?: business_model_canvas|lean_canvas, cluster? }

Out

{ mode, venture_summary, blocks[] (content · assumptions · strength · grounded_in), riskiest_assumptions[], grounded_in, provenance }

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Describe a market — get 2–4 buyer personas with triggers, objections, and channels.

In

{ market, cluster? }

Out

{ market_summary, personas[]{name, archetype, demographics, goals[], pains[], buying_triggers[], objections[], channels[], messaging_angle}, primary_persona, riskiest_assumptions[], grounded_in, provenance }

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Describe a promotion — get a single-minded creative brief.

In

{ campaign, audience?, cluster? }

Out

{ campaign_summary, objective, target_audience, insight, single_minded_proposition, reasons_to_believe[], key_message, tone_and_voice, channels[], success_metrics[], mandatories[], riskiest_assumptions[], grounded_in, provenance }

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Describe a customer win — get a Challenge–Solution–Results case study draft.

In

{ customer_story, cluster? }

Out

{ story_summary, headline, customer_snapshot, challenge, solution, results[], quote, cta, riskiest_assumptions[], grounded_in, provenance }

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Check a child's total sleep against the age-recommended range.

In

{ age_months, night_sleep_hours, nap_hours, cluster?, model? }

Out

{ age_band, total_sleep_hours, recommended, deficit_hours, status, flags[], guidance, grounded_in, provenance }

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Describe a project — get a context-fit code-review rubric.

In

{ context, cluster? }

Out

{ context_summary, categories[]{category, checks[]}, blocking[], nits[], automation_suggestions[], riskiest_assumptions[], grounded_in, provenance }

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Describe a market — get competitor profiles, the whitespace, and differentiation moves.

In

{ market, competitors?: string[], cluster? }

Out

{ market_summary, competitors[]{name, positioning, strengths[], weaknesses[], target}, positioning_axes[], whitespace[], differentiation_opportunities[], threats_to_watch[], riskiest_assumptions[], grounded_in, provenance }

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Describe a brand + goal — get content pillars, funnel mapping, cadence, and topics.

In

{ brand, goal?, cluster? }

Out

{ brand_summary, content_pillars[]{pillar, why, formats[]}, funnel_mapping[]{stage, content_types[]}, cadence, topic_ideas[], distribution[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

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Turn a recurring problem into a root cause, and a chaotic workflow into managed flow.

In

{ mode: five_whys|kanban|fishbone, problem?, workflow? }

Out

five_whys → { problem_summary, why_chain[], root_causes[] (evidence · countermeasure), shallow_traps_flagged[] } · kanban → { workflow_summary, columns[] (wip_limit · rationale), flow_metrics[], notes[] }

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Know which side of the chasm you're on — and run the playbook that gets you across.

In

{ mode: diagnose|playbook, product, traction? }

Out

diagnose → { stage, stage_evidence[], gear_scores[], weakest_gear, recommended_motion } · playbook → { stage_placement, chasm_diagnosis, beachhead, whole_product_gaps[], bowling_alley[] } — cited to Moore's constructs

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Turn a venture's riskiest assumptions into a get-out-of-the-building validation plan.

In

{ venture, assumptions?: string[], cluster? }

Out

{ venture_summary, stage, validation_plan[] (hypothesis · experiment · interview_questions · grounded_in), interview_guide[], pivot_or_persevere, grounded_in, provenance }

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Describe a customer + offering — get a journey map with moments of truth and fixes.

In

{ scenario, cluster? }

Out

{ scenario_summary, stages[]{stage, customer_goal, touchpoints[], emotions, opportunities[]}, moments_of_truth[], gaps_and_frictions[], priority_fixes[], riskiest_assumptions[], grounded_in, provenance }

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Describe a decision — get a tailored 5-day design-sprint agenda and every facilitation artifact.

In

{ decision, team?, daysAvailable? }

Out

{ sprint_summary, days_planned, agenda[] (day · theme · goal · activities), how_might_we[], note_and_vote[], four_step_sketch, rumble_script, 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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Describe a goal — get a lifecycle email sequence, email by email.

In

{ goal, audience?, cluster? }

Out

{ sequence_summary, sequence_goal, emails[]{step, timing, subject, preview, purpose, body_outline[], cta}, success_metrics[], riskiest_assumptions[], 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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Install an operating system on a company — V/TO, Accountability Chart, Rocks, IDS, weekly L10.

In

{ company, context?, cluster? }

Out

{ company_summary, vto, accountability_chart[], quarterly_rocks[], issues_list[], ids_guidance, weekly_l10_agenda[], grounded_in, provenance }

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Stop listing perks — design the deal that wins the people you actually need.

In

{ organization, segments?, current_rewards?, talent_competitors? }

Out

{ pillars[] (differentiator|table_stakes|aspiration · promise · rationale), segment_views[], honesty_gaps[], evp_statement, proof_points[], validation_measures[], tie_ins[], 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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Describe an industry — get a Porter's Five Forces read with strategic implications.

In

{ industry, cluster? }

Out

{ industry_summary, forces[]{force, intensity, drivers[], assessment}, overall_attractiveness, strategic_implications[], riskiest_assumptions[], grounded_in, provenance }

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Describe your funnel — get an ICE-scored backlog of growth experiments.

In

{ context, cluster? }

Out

{ context_summary, experiments[]{hypothesis, lever, metric, test, effort, confidence, impact, ice_priority}, sequencing[], guardrail_metrics[], riskiest_assumptions[], grounded_in, provenance }

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Describe your venture — get a first sales funnel, promotion plan, channel roadmap, and a path to the first paying customer.

In

{ venture, product? }

Out

{ venture_summary, funnel{stages[]}, promotion_plan, channel_roadmap{channels[]}, activation_plan[], first_customer_process{steps[]}, channel_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Turn a CanonicAI capability corpus into a published, cited how-to guide keyed to a job profile.

In

{ cluster?: 'start-a-company', profile?, mode?: aspire|resolve, audience? }

Out

CapabilityGuide (title · overview · sequence · sections[how_to/watch_out_for/grounded_in] · tensions)

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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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Design pay that actually changes behavior — grounded in how motivation works.

In

{ role_or_team, target_behaviors, budget? }

Out

{ scheme_summary, plan_structure, reward_triggers[] (measure · line_of_sight · grounded_in), expectancy_check[], gain_sharing_scaffold|null, risks{gaming,crowding_out_intrinsic}, grounded_in, provenance }

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Describe an incident — get a blameless postmortem with typed action items.

In

{ incident, cluster? }

Out

{ incident_summary, impact, timeline[]{when, what}, root_cause, contributing_factors[], what_went_well[], action_items[]{action, type, owner_hint}, riskiest_assumptions[], grounded_in, provenance }

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Turn 'who should be paid more' from an argument into a defensible structure.

In

{ organization, jobs[]{title, summary}, factors?, totalPoints?, gradeCount? }

Out

{ method, factor_scale[], evaluations[] (per-factor degree · rationale · points · rank · grade), grades[] (point bands · range spreads), hierarchy_summary, red_flags[], governance[], market_pricing_note, grounded_in, provenance }

run it on peopleanalyst.com →

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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Describe an offering — get a conversion landing page structure + copy outlines.

In

{ offering, audience?, cluster? }

Out

{ offering_summary, hero{headline, subhead, cta}, sections[]{section, purpose, content_outline[]}, objections_addressed[], primary_cta, riskiest_assumptions[], grounded_in, provenance }

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Describe what you're launching — get objectives, a channel plan, and a phased sequence.

In

{ offering, audience?, cluster? }

Out

{ offering_summary, objectives[], channel_plan[]{channel, role, funnel_stage, first_action}, launch_sequence[]{phase, timeframe, activities[]}, content_ideas[], key_metrics[], riskiest_assumptions[], grounded_in, provenance }

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Cascading objectives that stay participative — designed against MBO's own pathologies.

In

{ organization, objectives, example_unit? }

Out

{ cascade[] (level · objectives: serves · evidence_of_attainment), setting_process[], review_rhythm[], pay_link_note, failure_modes[], grounded_in, provenance }

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Describe an offering (+ budget) — get a paid-media plan with channel budget shares.

In

{ offering, budget?, goal?, cluster? }

Out

{ offering_summary, recommended_mix[]{channel, objective, budget_share, rationale, kpi}, flighting, testing_budget, measurement[], riskiest_assumptions[], grounded_in, provenance }

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Describe a brand — get a message house: umbrella message, pillars, proof, boilerplate.

In

{ brand, audience?, cluster? }

Out

{ brand_summary, umbrella_message, pillars[]{pillar, proof_points[]}, boilerplate, elevator_pitch, riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Describe your offer — get a StoryBrand BrandScript, a one-liner, and taglines.

In

{ brand, audience?, cluster? }

Out

{ brand_summary, one_liner, brandscript{character, problem{external,internal,philosophical}, guide{empathy,authority}, plan[], call_to_action{direct,transitional}, success[], failure[]}, taglines[], grounded_in, provenance }

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Describe your business — get a north-star, funnel metrics, and a KPI tree.

In

{ context, cluster? }

Out

{ context_summary, north_star, funnel_metrics[]{stage, metrics[]}, kpi_tree[]{kpi, definition, drivers[]}, guardrail_metrics[], reporting_cadence, riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

Describe an offering — get a co-marketing/partnerships plan.

In

{ offering, cluster? }

Out

{ offering_summary, partner_types[]{type, why, example_partners[]}, co_marketing_ideas[], outreach_approach, deal_structures[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Turn any research, transcript, or market notes into structured personas.

In

{ text, sourceId?, title? } — raw text is first-class

Out

PersonaCanonical[] (demographics · psychographics · behaviors · goals · pain points · use cases)

run it on peopleanalyst.com →

Describe an offering — get a deliberate market position, statement, and beachhead.

In

{ offering, competitors?: string[], cluster? }

Out

{ offering_summary, positioning_statement, components{target_customer, market_category, competitive_alternatives[], unique_attributes[], value_and_proof[]}, beachhead, riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Describe an announcement — get a PR plan: narrative, angles, outlets, timeline.

In

{ announcement, cluster? }

Out

{ announcement_summary, narrative, story_angles[]{angle, outlet_type}, target_outlets[], spokesperson_points[], assets_needed[], timeline[]{phase, activities[]}, riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Describe an offering — get a pricing model, tiers, and the assumptions to test.

In

{ offering, market?, cluster? }

Out

{ offering_summary, recommended_model, rationale, price_metric, tiers[]{name, who, price_guidance, included[]}, psychological_pricing[], monetization_risks[], riskiest_assumptions[], grounded_in, provenance }

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Drop in a task list (and roles) — get a validated RACI plus impact/effort priority grids and a sequenced plan.

In

{ tasks?: string[], roles?: string[], decision?, mode?: raci|prioritization|rapid|both }

Out

{ mode, raci?{roles, rows[], validation[], valid}, rapid?{decision, assignments[], validation, valid, failure_modes[]}, prioritization?{action_priority[], complexity_impact[], sequenced_actions[]}, grounded_in, provenance }

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Go from a research dump to a titled, positioned, chapter-by-chapter content plan.

In

{ source: deep_research|notes, text, sourceId? } per stage, or the full pipeline

Out

BookIdeaCanonical[] → EnhancedBookConcept → BookOutline (parts/chapters/learning objectives)

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Describe an offering — get a referral/word-of-mouth program design.

In

{ offering, cluster? }

Out

{ offering_summary, program_type, incentive_structure, mechanics[], triggers[], messaging[], success_metrics[], risks[], riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Paste an idea (or book, study, report) — get a PRD-grade, MVP-scoped backlog.

In

{ input, sourceType: business_idea|deep_research|book|research, sourceId? }

Out

RequirementCanonical[] + gap report (missing/weak fields)

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Describe your churn situation — get a lifecycle retention plan.

In

{ context, cluster? }

Out

{ context_summary, lifecycle_stages[]{stage, goal, tactics[]}, churn_drivers[], retention_levers[], loyalty_mechanics[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

Describe a decision — get distinct future scenarios + no-regret strategies.

In

{ focus, cluster? }

Out

{ focus_summary, key_uncertainties[], critical_axes[]{axis, low_end, high_end}, scenarios[]{name, narrative, implications[]}, robust_strategies[], signposts[], riskiest_assumptions[], 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 }

run it on peopleanalyst.com →

Describe a market — get an STP read: segments by need, the beachhead, and a position per segment.

In

{ market, cluster? }

Out

{ market_summary, segments[]{name, description, size_signal, needs[], attractiveness}, recommended_target, targeting_rationale, positioning_angle_per_segment[], riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Describe a topic — get intent-mapped keyword clusters + content briefs.

In

{ topic, cluster? }

Out

{ topic_summary, keyword_clusters[]{cluster, intent, keywords[], content_idea}, pillar_and_clusters, quick_wins[], content_briefs[]{title, target_keyword, angle}, riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

Build today's baby nap/bedtime schedule from age + this morning's wake time.

In

{ age_months, wake_time ('HH:MM' 24h), cluster?, model? }

Out

{ age_band, wake_window, recommended_naps, schedule[], overtired_warnings[], guidance, grounded_in, provenance }

run it on peopleanalyst.com →

Describe a service — get user-centric SLIs, SLOs, and an error-budget policy.

In

{ service, cluster? }

Out

{ service_summary, slis[]{sli, definition, measurement}, slos[]{objective, target, window}, error_budget_policy, alerting_notes[], riskiest_assumptions[], grounded_in, provenance }

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Turn 'do better' into a goal you can measure — SMART objective + metric + OKR.

In

{ aim, context?, cluster? }

Out

{ original_aim, smart_objective (specific/measurable/achievable/relevant/time_bound + metric/target/cadence), measurability_flags[], okr?, grounded_in, provenance }

run it on peopleanalyst.com →

Describe a brand — get a posting cadence, content mix, and a one-week calendar.

In

{ brand, platforms?, cluster? }

Out

{ brand_summary, cadence, content_mix[]{type, share, purpose}, weekly_plan[]{day, platform, format, idea}, hashtag_strategy, success_metrics[], riskiest_assumptions[], grounded_in, provenance }

run it on peopleanalyst.com →

Enter the few numbers a founder has — get CAC, LTV, payback, your target number, and TAM computed and interpreted.

In

{ venture, salesMarketingSpend?, customersAcquired?, pricePerPeriod?, grossMarginPct?, periodsPerYear?, monthlyChurnPct?|avgCustomerLifetimeMonths?, desiredAnnualIncome?, market?{beachheadCustomers?, annualContractValue?, followOnCustomers?, totalMarketCustomers?}, buyerBenefit? }

Out

{ venture_summary, metrics[] (value · display · formula · benchmark · health), interpretations[] (meaning · lever · grounded_in), quantified_value_proposition, kpis[], fundability_verdict, grounded_in, provenance }

run it on peopleanalyst.com →

Run a SWOT, PEST(LE), or stakeholder analysis with substance — not a template full of the obvious.

In

{ subject, frame?: swot|pest|stakeholder|pestel, cluster? }

Out

{ subject_summary, frame, swot?|pest?|stakeholders?|pestel?, implications[], thin_cells[], grounded_in, provenance }

run it on peopleanalyst.com →

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 }

run it on peopleanalyst.com →

Describe a design — get a senior design review with risks + recommendations.

In

{ design, cluster? }

Out

{ design_summary, strengths[], risks[]{risk, severity, mitigation}, scalability_notes[], failure_modes[], open_questions[], recommendations[], riskiest_assumptions[], grounded_in, provenance }

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Name a task you personally do — get a documented SOP and a score for whether the business can run without you.

In

{ task, context? }

Out

{ task_summary, sop{steps[] (action · owner · standard · metric · grounded_in)}, franchise_prototype{founder_dependency_hotspots[], missing_standards[], delegability_score, score_rationale}, grounded_in, provenance }

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Describe a system — get a rated, prioritized tech-debt register.

In

{ context, cluster? }

Out

{ context_summary, items[]{item, impact, effort, risk_if_ignored, priority}, quick_wins[], sequencing[], riskiest_assumptions[], grounded_in, provenance }

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Rewrite a tense toddler moment through the How-to-Talk approaches.

In

{ situation, child_age_years?, cluster?, model? }

Out

{ situation, rewrites[] (approach·say·why), praise_swap, principle, 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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Enter your founders, shares, and each round's terms — get a cap table, dilution waterfall, and a priced, defensible valuation.

In

{ venture, founders:[{name, shares}], optionPoolShares?, rounds:[{name, raiseAmount, preMoneyValuation?|postMoneyValuation?}], targetExitValuation? }

Out

{ venture_summary, initial_cap_table[], rounds[] (pre/post · investor% · founders% · post-round cap table), dilution_waterfall[], founder_ownership_at_exit_pct, founder_value_at_exit, valuation_methods[] (step_up + risk_mitigation), dilution_read, pushback[], grounded_in, provenance }

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Describe a business — get a Porter value-chain map + sources of advantage.

In

{ business, cluster? }

Out

{ business_summary, primary_activities[]{activity, value_drivers[], improvement_opportunities[]}, support_activities[]{...}, margin_levers[], sources_of_advantage[], riskiest_assumptions[], grounded_in, provenance }

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Describe an offering — get a Value Proposition Canvas with a fit read and a value-prop statement.

In

{ offering, segment?, cluster? }

Out

{ offering_summary, customer_profile{jobs[],pains[],gains[]}, value_map{products_services[],pain_relievers[],gain_creators[]}, fit_assessment, value_proposition_statement, riskiest_assumptions[], grounded_in, provenance }

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Describe a business — score its resources on VRIO for sustained advantage.

In

{ business, cluster? }

Out

{ business_summary, resources[]{resource, valuable, rare, inimitable, organized, implication}, sustained_advantages[], gaps_to_address[], riskiest_assumptions[], 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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