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reliable-tool-context可靠的工具上下文

Agent Skill

reliable-tool-context 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

10,209

周安装

409

GitHub Stars

1

下载量

3,305
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reliable-tool-context(可靠的工具上下文)
来源仓库:https://github.com/zmaciel/reliable-tool-context
安装命令:
openclaw skills install reliable-tool-context
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install reliable-tool-context

简介

reliable-tool-context 用于从命令输出构建可靠的工具上下文,适合在 OpenClaw 中需要重现代码查询结果时使用。

  • 它结合工件和紧凑代码片段,提升工具调用的可复现性。
  • 通过 openclaw skills install reliable-tool-context 安装,具体用法请查阅原始文档。
  • 使用前应验证上下文生成的准确性,防止因输出截断导致信息丢失。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
reliable-tool-context
description
Build reliable tool context from command output using artifacts and compact reproducible code queries.
homepage
https://github.com/lourencomaciel/sift-gateway/tree/main/docs/openclaw
metadata
{"openclaw":{"skillKey":"sift-gateway-reliable-tool-context","homepage":"https://github.com/lourencomaciel/sift-gateway/tree/main/docs/openclaw","requires":{"bins":["sift-gateway"]},"install":[{"id":"uv","kind":"uv","package":"sift-gateway","bins":["sift-gateway"],"label":"Install Sift Gateway (uv)"}]}}

Reliable Tool Context

Use this skill to build reliable tool context whenever command output will be analyzed by the model and correctness matters. Capture once, query from artifacts, and return compact answers without copying raw payloads into model context.

CLI Decision Rule

Use Sift to establish reliable tool context when any of these are true:

  • Output will be consumed by the model (analysis, transformation, or follow-up

querying).

  • Pagination exists or may exist (pagination.next.kind=="command").
  • JSON schema/root confidence is low, or rows may be heterogeneous (even for

small payloads).

  • You need reproducibility, redaction discipline, or auditability.

Use direct CLI only when all of these are true:

  • Output is clearly small.
  • Schema/root path is obvious.
  • It is a one-off human inspection with no follow-up model reasoning.

Required workflow

  1. Capture output as an artifact:
sift-gateway run --json -- <command>
  1. Keep only artifact_id plus a short summary in prompt context.
  2. Handle pagination explicitly only when present. If

pagination.next.kind=="command", continue with:

sift-gateway run --json --continue-from <artifact_id> -- <next-command-with-next_params-applied>
  1. Query artifacts with explicit root path and compact output:
sift-gateway code --json <artifact_id> '$' --code "def run(data, schema, params): return [{'id': row.get('id'), 'status': row.get('status')} for row in data[:20]]"
  1. Resolve root path from response hints, not guesswork:
  • Current run behavior uses canonical root path $; use $ for follow-up

code queries.

  • If response_mode=="schema_ref" and schemas are present, use schema

root_path as the source of truth.

  • Treat sample_item as a preview row only.

Schema discovery protocol

  • Do not use jq '.[0]' (or equivalent "first-item" shortcuts) to infer schema

or root path. Many payloads are object-wrapped, have multiple candidate roots, or include heterogeneous rows where first-item heuristics are misleading.

  • In Sift responses, sample_item is emitted only when Sift can verify

consistent item shape across the resolved list. If sample_item is absent, inspect schemas; for current run captures, schema root_path should be $.

  • If sample_item_text_truncated is true, treat long text fields as truncated

previews and confirm details with a focused code query.

Guardrails

  • sift-gateway code defaults to --scope all_related; start with

--scope single for anchor-only analysis and widen scope only when needed.

  • Start with run(data, schema, params); move to `run(artifacts, schemas,

params)` only when cross-artifact joins are required.

  • Use pure Python first; do not assume optional packages are available.
  • Return aggregates or top <= 20 rows; avoid full-record dumps.
  • Never paste raw captured payloads back into context.
  • Do not claim completeness until pagination.retrieval_status == COMPLETE.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

95.72%
按下载量换算3,164

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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