Cascade Analysis Language
Every case study ships with its own source code.
CAL is a domain-specific language for detecting, measuring, scoring, and validating organizational cascades across six dimensions. 12 keywords. 3 formulas. One decision gate. The math is auditable. The output is reproducible. The evidence is public.
Every case in the library passes through the same scoring pipeline. The formula is auditable, reproducible, and verifiable with a calculator.
FETCH ≥ 1,000 → EXECUTE. Below that → QUEUE or SKIP. The threshold is the publication gate. Every case in the library cleared it.
CAL maps to the Sense → Analyze → Measure → Decide → Act → Validate pipeline. Each keyword corresponds to an observable behavior from the Cormorant Foraging methodology.
CAL connects what you observe to what you do about it. The same pipeline runs whether you're analyzing a bank collapse or a workforce restructuring.
cal scaffold entitycal run script.cal --data entities.json — deterministic, reproducible, auditable.RECALL case ON date — check WATCH triggers against reality. Measure calibration. Close the loop.Three cases. Three sectors. The same language detecting patterns invisible to conventional analysis.
-- Silicon Valley Bank: 6D Cascade Analysis
-- Sense → Analyze → Measure → Decide → Act
FORAGE banks
WHERE asset_liability_mismatch > 50
AND uninsured_deposits > 85
AND cro_vacancy IS "18 months"
ACROSS D5, D1, D3, D4, D6, D2
DEPTH 3
SURFACE svb_cascade
DIVE INTO deposits
WHEN withdrawal_rate > 1000000 -- $1M per second
TRACE cascade
EMIT bank_run_signal
DRIFT svb_cascade
METHODOLOGY 90 -- expected risk detection capability
PERFORMANCE 15 -- actual: audits passed, cascade invisible
FETCH svb_cascade
THRESHOLD 1000
ON EXECUTE CHIRP critical "6/6 dimensions compromised in 48 hours"
SURFACE analysis AS json-- Amazon AI Workforce Cascade: 6D Analysis
-- Sense → Analyze → Measure → Decide → Act
FORAGE tech_companies
WHERE corporate_layoffs > 25000
AND revenue_growth > 10
AND ai_capex > 100000000000
AND engineering_cut_ratio > 35
ACROSS D2, D6, D5, D3, D1, D4
DEPTH 3
SURFACE amazon_cascade
DIVE INTO workforce
WHEN sde_layoff_pct > 30 -- software engineers >30% of cuts
TRACE cascade
EMIT controlled_demolition_signal
DRIFT amazon_cascade
METHODOLOGY 85 -- expected: world-class workforce planning
PERFORMANCE 35 -- actual: mass cuts while record profits
FETCH amazon_cascade
THRESHOLD 1000
ON EXECUTE CHIRP critical "6/6 dimensions hit — workforce is the legacy system"
SURFACE analysis AS json-- EV Writedown Cascade: 6D Analysis
-- Sense → Analyze → Measure → Decide → Act
FORAGE automotive_sector
WHERE ev_writedowns > 50000000000
AND mandate_reversals > 2
AND hybrid_entrant_count > 3
AND market_share_decline > 50
ACROSS D4, D3, D6, D2, D1, D5
DEPTH 3
SURFACE ev_correction_cascade
DIVE INTO regulatory_shock
WHEN mandate_revocations > 2 -- EV mandates scrapped, tax credits expiring
TRACE cascade
EMIT hybrid_pragmatist_signal
DRIFT ev_correction_cascade
METHODOLOGY 85 -- expected: hybrid viability known (locomotive tech for 100+ years)
PERFORMANCE 35 -- actual: regulatory gaps, capital asymmetry, scale barriers
FETCH ev_correction_cascade
THRESHOLD 1000
ON EXECUTE CHIRP critical "6/6 dimensions hit — the hybrid pragmatists are filling the gap"
SURFACE analysis AS jsonCAL isn't a whitepaper. It's a published language with an executable runtime, a formal specification, and 160+ cases of empirical evidence.
The cases above are three of 160+. Banking, tech, automotive, sports, aerospace, entertainment, fashion, agriculture, beauty-healthcare, SMB, trades — the same six dimensions surface patterns that conventional analysis misses.