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yq-clear-context

Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.

EthanYoQ/Skill-hub

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含まれるファイル（2）

SKILL.md2.0 KB
LICENSE1.0 KB

SKILL.md（原文）

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Start with minimal context: expose purpose, available actions, and constraints first; load detailed instructions when needed.
Make discovery explicit: every deferred resource needs a clear description and a reliable way to retrieve it.
Keep critical constraints visible: permissions, destructive effects, and required validation should appear before the relevant action.
Prefer bounded outputs: return concise results with identifiers or paths for retrieving details; avoid dumping entire logs or documents.
Use locality: include a bounded amount of likely needed adjacent context with an operation’s result. For example, deliver a thread reply with a few preceding messages. Mark omissions and truncation explicitly, and provide a way to retrieve more.
Evaluate total work: saving context is useful only if it does not cause more searches, repeated reads, or mistakes.

Tool definitions

Delete obvious constraints. Omit rules the model learns from the call result, such as a missing file failing to read or a failed send not being delivered.
Describe behavior, not implementation. State what the tool does and returns; omit internal mechanisms, output markers the model sees in results, and enforcement details.
Put parameter rules on the parameter. Defaults, ranges, pairing, and when-to-set rules belong in that parameter's description or schema; a description that varies with configuration usually means the varying part belongs to a parameter.
Say each fact once. Do not repeat the tool definition in its system-prompt section, a parameter description in the tool description, or one tool's rules in another tool.
Measure the change. Compare first-turn prompt tokens before and after a tool-definition change.

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概要と使いどころ

aact-bulk-trials

無料

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

日本語の概要は準備中です。原文の説明を表示しています。