playbook/antigravity-awesome-skills/skills/xvary-stock-research/references/methodology.md

XVARY Methodology (Public Framework)

This document is the public framework for XVARY Research.

It is intentionally the menu, not the recipe: stage names, logic flow, and decision philosophy are published; internal prompts, thresholds, and convergence algorithms are not.

Full narrative: xvary.com/methodology

Research Philosophy

XVARY is built around five principles:

1. Variant perception first: value comes from being directionally right where consensus is wrong.
2. Evidence before narrative: facts constrain the story, not the other way around.
3. Conviction is earned: scores reflect cross-validated support, not tone or confidence theater.
4. Adversarial challenge is mandatory: every thesis gets attacked before publication.
5. Kill-file discipline: each call includes explicit thesis-invalidating conditions.

22-Stage Operational DAG (21-Stage Research Spine + Finalize)

flowchart TD
    s1[directive_selection] --> s2[phase_a]
    s2 --> s3[data_quality_gate]
    s3 --> s4[evidence_gap_analysis]
    s4 --> s5[kvd_hypothesis]
    s4 --> s6[pane_selection]
    s6 --> s7[quant_foundation]
    s7 --> s8[model_quality_gate]
    s6 --> s9[phase_b]
    s5 --> s9
    s9 --> s10[triangulation]
    s10 --> s11[pillar_discovery]
    s11 --> s12[phase_c]
    s11 --> s13[why_tree]
    s12 --> s14[quality_gate]
    s13 --> s14
    s14 --> s15[challenge]
    s15 --> s16[synthesis]
    s16 --> s17[audit]
    s17 --> s18[report_json]
    s18 --> s19[audience_calibration]
    s18 --> s20[compliance_audit]
    s19 --> s21[completion_loop]
    s20 --> s21
    s21 --> s22[finalize]

The operational DAG has 22 nodes in code (finalize included). Publicly we refer to the core research spine as the 21-stage methodology and treat finalization as release control.

Stage Intent (One-Line)

1. directive_selection: choose sector/style evidence directives.
2. phase_a: collect baseline facts, filings, market context, and broad evidence.
3. data_quality_gate: block low-integrity factual inputs.
4. evidence_gap_analysis: detect missing evidence and open targeted searches.
5. kvd_hypothesis: identify candidate key value drivers.
6. pane_selection: choose report panes for company profile.
7. quant_foundation: build model scaffolding (valuation/risk context).
8. model_quality_gate: sanity-check model outputs before synthesis.
9. phase_b: run enrichment search and deeper context collection.
10. triangulation: compare evidence across independent reasoning vectors.
11. pillar_discovery: derive weighted thesis pillars.
12. phase_c: execute module-level synthesis in parallel.
13. why_tree: decompose causal claims and dependency chains.
14. quality_gate: run structured quality tests and consistency checks.
15. challenge: adversarially test each pillar and assumptions.
16. synthesis: assemble conviction, variant view, and scenario posture.
17. audit: multi-role verification with follow-up rounds.
18. report_json: build structured report payload.
19. audience_calibration: ensure readability + decision-usefulness.
20. compliance_audit: verify methodology and policy compliance.
21. completion_loop: repair sparse or inconsistent sections.
22. finalize: release gating and artifact finalization.

Quality Gates (Public Names + What They Check)

• Data Quality Gate: missingness, stale fields, broken units, filing coherence.
• Model Quality Gate: sanity bounds, impossible outputs, assumption integrity.
• Quality Gate: cross-module consistency, contradiction flags, evidence sufficiency.
• Audience Calibration: clarity, thesis readability, decision speed under time pressure.
• Compliance Audit: methodology adherence, sourcing hygiene, output policy checks.
• Finalize Gate: final validation + publication readiness.

23 Research Modules

1. kvd: key value-driver identification and trajectory framing.
2. core_facts: baseline thesis framing and variant setup.
3. operations: revenue engine, segment economics, moat mechanics.
4. financials: profitability, balance-sheet quality, cash conversion.
5. valuation: intrinsic range, scenario math, and expectation gap.
6. management: leadership quality, incentives, and execution credibility.
7. competition: market structure, rival dynamics, strategic pressure.
8. risk: kill criteria, thesis breakers, and downside maps.
9. capital_allocation: buybacks/dividends/M&A capital discipline.
10. governance: board structure, oversight quality, shareholder alignment.
11. catalysts: event map and timing-sensitive thesis triggers.
12. product_tech: product moat, roadmap durability, and innovation path.
13. supply_chain: supplier dependency, resilience, and bottleneck exposure.
14. tam: market size realism, penetration runway, and saturation risk.
15. street: consensus expectations vs. internal thesis.
16. macro_sensitivity: rates/FX/cycle sensitivity mapping.
17. value_framework: investment framework fit + decision rubric.
18. quant_profile: factor, drawdown, and liquidity behavior profile.
19. signals: alternative/leading indicators and signal dashboard.
20. derivs: options/short-interest positioning context.
21. earnings_track: beat/miss quality and guidance reliability.
22. history: strategic timeline and historical analog framing.
23. executive_summary: cross-module synthesis for fast decisioning.

Conviction Scoring (Concept)

Conviction is built from weighted pillars rather than a single-model output:

• Pillar strength (how well each core claim is supported)
• Pillar dependency risk (how fragile each claim is)
• Cross-module consistency (do independent modules agree?)
• Adversarial challenge survival (did core claims hold up?)
• Downside asymmetry under identified kill criteria

Weights are dynamic by business model and evidence reliability. Exact calibration is proprietary.

Kill-File Risks (Concept)

Every thesis is paired with explicit conditions that invalidate it. A kill file is not a downside list; it is the shortest set of assumptions that, if broken, forces re-underwriting.

Typical kill-file categories:

• Structural demand break
• Unit-economics deterioration
• Balance-sheet fragility
• Regulatory/regime shock
• Management credibility failure

Five-Vector Triangulation (Concept)

Each ticker is evaluated through five independent vectors before synthesis:

1. Accounting reality
2. Market-implied expectations
3. Operational execution
4. Strategic position / industry structure
5. Macro-regime sensitivity

The goal is convergence testing: where vectors agree, conviction rises; where they diverge, uncertainty is made explicit.

Intentionally Not Published

• Module prompt templates
• Prompt routing logic and fallback trees
• Threshold matrices and gating cutoffs
• Internal convergence scoring mechanics
• Sector-specific directive libraries