# Scientific Threshold And Context Inference

> Skill: scientific threshold and context inference

- Skill: `dingxingdi/scientific-threshold-and-context-inference-2` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/scientific-threshold-and-context-inference-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/scientific-threshold-and-context-inference-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dingxingdi (https://skillmd.com/u/dingxingdi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dingxingdi/scientific-threshold-and-context-inference-2

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# Skill: scientific threshold and context inference

## 1. Capability Definition & Real Case
* **Professional Definition**: The ability to operationalize implicit domain conventions that are not explicitly encoded in the schema, such as significance thresholds, effect directionality, trial-phase interpretation, or approval logic that depends on external scientific context.
* **Dimension Hierarchy**: Query Reasoning->Domain-Constrained Semantics->scientific threshold and context inference

### Real Case
**[Case 1]**
* **Initial Environment**: A biomedical BigQuery database stores GWAS summary statistics including p-values, beta coefficients, gene mappings, and disease labels. The schema does not itself encode the phrase “increase the risk” as a ready-made field.
* **Real Question**: Are there any C9orf72 gene mutations that increase the risk of Parkinson’s disease?
* **Real Trajectory**: Filter the Parkinson’s GWAS table to the target gene, apply the genome-wide significance threshold, constrain the beta direction to positive effect, and then inspect the remaining variants.
* **Real Answer**: A correct SQL answer requires applying both p-value and directionality conventions rather than merely filtering on the gene name.
* **Why this demonstrates the capability**: Nothing in the natural-language question explicitly says “p < 5e-08” or “beta > 0”. The agent must know that “increase the risk” implies positive effect direction and that significant association implies a strict threshold. This is therefore a canonical threshold-and-context inference task.

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**[Case 2]**
* **Initial Environment**: The same biomedical environment links genes, diseases, drugs, and trial or approval metadata. The user asks about intervention relevance rather than a raw join result.
* **Real Question**: What drugs target genes up-regulated in Parkinson’s disease?
* **Real Answer**: The correct answer requires identifying the relevant genes first, then using contextual drug-target and status information to decide which candidates qualify.
* **Why this demonstrates the capability**: The challenge is not just joining drug and gene tables. The phrase “target genes up-regulated in Parkinson’s disease” presupposes a domain-aware interpretation of what biological evidence and status filters are relevant. The agent must therefore inject context that is partly outside bare schema matching.

## Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:

1. **Phase 1: Environment Exploration**
   Read the exploration guidelines to discover raw knowledge seeds:
   `references/EXPLORATION.md`

2. **Phase 2: Trajectory Selection**
   Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
   `references/SELECTION.md`

3. **Phase 3: Data Synthesis**
   Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
   `references/SYNTHESIS.md`