How do I install the Zf Pipeline Contracts skill?

Run npx skillmds@latest add majiayu000/zf-pipeline-contracts in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Zf Pipeline Contracts free to use?

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Who published Zf Pipeline Contracts?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Zf Pipeline Contracts by majiayu000

Name: Zf Pipeline Contracts
Rating: 4.2 (5 reviews)
Author: majiayu000

Zf Pipeline Contracts

Pipeline contract enforcement for ZINC-Fusion-V15 soybean oil forecasting system. Use when working on any ZINC-Fusion-V15 task involving schema definitions, training pipelines, L0 specialists, data ingestion, MLflow/Dagster configuration, or debugging contract drift. Triggers on mentions of fusion.db, specialist models, horizon encoding, quantile outputs, OOF predictions, or any ZINC-Fusion development work.

majiayu000 Updated Sep 9, 2026 567 repo stars

File contents

ZF Pipeline Contracts

Enforce schema, drift prevention, and pipeline contracts across the L0→L1→L2→L3 architecture for ZINC-Fusion-V15.

Canonical Naming (Non-Negotiable)

Item	Canonical	Never Use
Project	ZINC-Fusion-V15	CBI-V15, CBI, zinc_fusion
Database	fusion.db	zinc_fusion_v15.db, cbi.db
Python module	`fusion.*`	`zinc_fusion.`, `cbi.`
Dagster package	`quickstart_etl`	(intentional scaffold name)
Model term	"Specialists"	"Big-10", "buckets", "Big-8"

If you drift to legacy names, stop and correct immediately.

L0 Architecture (11 Models)

ID	Name	Type	Domain
0	`core`	TimeSeriesPredictor	ZL price action
1	`crush`	TabularPredictor	Crush margin dynamics
2	`china`	TabularPredictor	Chinese demand/policy
3	`fx`	TabularPredictor	Currency impacts
4	`fed`	TabularPredictor	Fed policy
5	`tariff`	TabularPredictor	Trade policy
6	`energy`	TabularPredictor	Energy prices
7	`biofuel`	TabularPredictor	Biofuel demand
8	`palm`	TabularPredictor	Palm oil competition
9	`volatility`	TabularPredictor	Volatility regimes
10	`substitutes`	TabularPredictor	Veg oil substitution

Time Grains (LOCKED)

Grain	PK Column	Horizon Steps	Use Case
`_1h`	`ts_event`	N/A (features only)	Intraday volatility, sentiment
`_1d`	`as_of_date`	5, 21, 63, 126	Core forecasting, all OOF

Only _1h and _1d exist. Do not invent _4h, _8h, _1w grains.

Pipeline Layers

L3: Risk Layer      → Monte Carlo → VaR/CVaR → Procurement signals
        ↑
L2: Ensemble Layer  → Weighted fusion → P10/P50/P90 forecasts
        ↑
L1: Meta-Learner    → TabularPredictor stacking OOF from L0
        ↑
L0: Base Models     → 1 Core + 10 Specialists (11 total)

Neural Sentiment → ALL Specialists

Sentiment feeds ALL specialists, not just tariff/china/biofuel:

Specialist	Weight	Rationale
crush	0.10	WASDE/supply sentiment
china	0.15	Trade/demand sentiment
fx	0.08	Currency sentiment
fed	0.10	Monetary policy tone
tariff	0.15	Trade policy sentiment
energy	0.12	Energy/crude sentiment
biofuel	0.12	Biofuel mandate sentiment
palm	0.08	Palm/deforestation sentiment
volatility	0.05	Risk sentiment amplifier
substitutes	0.05	Cross-commodity sentiment

Total: 1.00

Top 3 Failure Modes

Priority	Failure	Cause	Detection
1	Contract drift	Column names diverge from code	Schema diff query
2	Join-key drift	L0 outputs don't uniquely key on (as_of_date, horizon_steps)	Duplicate check
3	Quantile crossing	p10 > p50 or p50 > p90	Monotonicity query

Reference Files

Load these based on task:

File	Load When
`references/naming_contracts.md`	Starting any ZF work
`references/schema_contracts.md`	Creating/modifying tables
`references/horizon_encoding.md`	Working with time horizons
`references/hourly_contracts.md`	Working with 1h grain data
`references/neural_sentiment_routing.md`	Sentiment feature engineering
`references/guardrail_queries.sql`	Before/after data mutations
`references/manifest.yaml`	Adding data sources
`references/new_specialist_checklist.md`	Adding L0 specialist

Quick Validation Workflow

Before any commit touching pipeline code:

Read references/guardrail_queries.sql
Run quantile crossing check
Run join-key uniqueness check (per specialist)
Run horizon encoding check
If adding tables, verify against references/schema_contracts.md

---name: zf-pipeline-contractsdescription: Pipeline contract enforcement for ZINC-Fusion-V15 soybean oil forecasting system. Use when working on any ZINC-Fusion-V15 task involving schema definitions, training pipelines, L0 specialists, data ingestion, MLflow/Dagster configuration, or debugging contract drift. Triggers on mentions of fusion.db, specialist models, horizon encoding, quantile outputs, OOF predictions, or any ZINC-Fusion development work.--- # ZF Pipeline Contracts Enforce schema, drift prevention, and pipeline contracts across the L0→L1→L2→L3 architecture for ZINC-Fusion-V15. ## Canonical Naming (Non-Negotiable) | Item | Canonical | Never Use | |------|-----------|-----------| | Project | ZINC-Fusion-V15 | CBI-V15, CBI, zinc_fusion | | Database | fusion.db | zinc_fusion_v15.db, cbi.db | | Python module | `fusion.*` | `zinc_fusion.*`, `cbi.*` | | Dagster package | `quickstart_etl` | (intentional scaffold name) | | Model term | "Specialists" | "Big-10", "buckets", "Big-8" | If you drift to legacy names, stop and correct immediately. ## L0 Architecture (11 Models) | ID | Name | Type | Domain | |----|------|------|--------| | 0 | `core` | TimeSeriesPredictor | ZL price action | | 1 | `crush` | TabularPredictor | Crush margin dynamics | | 2 | `china` | TabularPredictor | Chinese demand/policy | | 3 | `fx` | TabularPredictor | Currency impacts | | 4 | `fed` | TabularPredictor | Fed policy | | 5 | `tariff` | TabularPredictor | Trade policy | | 6 | `energy` | TabularPredictor | Energy prices | | 7 | `biofuel` | TabularPredictor | Biofuel demand | | 8 | `palm` | TabularPredictor | Palm oil competition | | 9 | `volatility` | TabularPredictor | Volatility regimes | | 10 | `substitutes` | TabularPredictor | Veg oil substitution | ## Time Grains (LOCKED) | Grain | PK Column | Horizon Steps | Use Case | |-------|-----------|---------------|----------| | `_1h` | `ts_event` | N/A (features only) | Intraday volatility, sentiment | | `_1d` | `as_of_date` | 5, 21, 63, 126 | Core forecasting, all OOF | **Only `_1h` and `_1d` exist.** Do not invent `_4h`, `_8h`, `_1w` grains. ## Pipeline Layers ``` L3: Risk Layer → Monte Carlo → VaR/CVaR → Procurement signals ↑ L2: Ensemble Layer → Weighted fusion → P10/P50/P90 forecasts ↑ L1: Meta-Learner → TabularPredictor stacking OOF from L0 ↑ L0: Base Models → 1 Core + 10 Specialists (11 total) ``` ## Neural Sentiment → ALL Specialists Sentiment feeds ALL specialists, not just tariff/china/biofuel: | Specialist | Weight | Rationale | |------------|--------|-----------| | crush | 0.10 | WASDE/supply sentiment | | china | 0.15 | Trade/demand sentiment | | fx | 0.08 | Currency sentiment | | fed | 0.10 | Monetary policy tone | | tariff | 0.15 | Trade policy sentiment | | energy | 0.12 | Energy/crude sentiment | | biofuel | 0.12 | Biofuel mandate sentiment | | palm | 0.08 | Palm/deforestation sentiment | | volatility | 0.05 | Risk sentiment amplifier | | substitutes | 0.05 | Cross-commodity sentiment | **Total: 1.00** ## Top 3 Failure Modes | Priority | Failure | Cause | Detection | |----------|---------|-------|-----------| | 1 | Contract drift | Column names diverge from code | Schema diff query | | 2 | Join-key drift | L0 outputs don't uniquely key on (as_of_date, horizon_steps) | Duplicate check | | 3 | Quantile crossing | p10 > p50 or p50 > p90 | Monotonicity query | ## Reference Files Load these based on task: | File | Load When | |------|-----------| | `references/naming_contracts.md` | Starting any ZF work | | `references/schema_contracts.md` | Creating/modifying tables | | `references/horizon_encoding.md` | Working with time horizons | | `references/hourly_contracts.md` | Working with 1h grain data | | `references/neural_sentiment_routing.md` | Sentiment feature engineering | | `references/guardrail_queries.sql` | Before/after data mutations | | `references/manifest.yaml` | Adding data sources | | `references/new_specialist_checklist.md` | Adding L0 specialist | ## Quick Validation Workflow Before any commit touching pipeline code: 1. Read `references/guardrail_queries.sql` 2. Run quantile crossing check 3. Run join-key uniqueness check (per specialist) 4. Run horizon encoding check 5. If adding tables, verify against `references/schema_contracts.md`

Frequently asked questions