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severinzhong

authoring-data-cli-source

作者 Severin Zhong · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ 安全检测通过
249
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当前安装
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在 OpenClaw 中安装
/install authoring-data-cli-source
功能描述
Use when the user needs to add or redesign an agent-data-cli source for RSS, news, social media, finance, APIs, scraping, browser automation, authentication,...
使用说明 (SKILL.md)

Authoring agent-data-cli source

Overview

Use this skill to design and implement an agent-data-cli source with stable behavior and clear project fit.

This skill is deliberately stricter than normal feature work because a weak source design causes protocol drift, command confusion, and unreliable sync behavior.

It is the source-authoring path for RSS feeds, HTTP APIs, HTML scraping, browser-driven sites, finance data, news content, and other remote content systems that must fit the source/channel/content model.

Current core contract to keep in mind:

  • source/channel is still the only core resource model
  • content update sources return ContentSyncBatch, not flat per-row persistence instructions
  • shared persistence is now content_nodes, content_channel_links, and content_relations
  • structural relations in core use abstract parent; source-specific meaning belongs in relation_semantic

Hard Gate

Do not start implementation immediately.

The required sequence is:

  1. research
  2. spec
  3. plan
  4. approval
  5. implement
  6. verify

If the user explicitly wants to skip a stage, say what risk that creates before proceeding.

When to Use

Use this skill when the user wants to:

  • add a new source
  • redesign an existing source
  • add source capabilities such as channel search, content search, content update, or content interact
  • add support for RSS, APIs, scraping, browser automation, authentication, cookies, or remote side effects

Do not use this skill for:

  • ordinary content operations against an existing source
  • unrelated CLI or store changes with no source work

Install From skills.sh

Install this skill directly from skills.sh:

npx skills add https://github.com/severinzhong/agent-data-cli --skill authoring-data-cli-source

Install

If agent-data-cli is not present locally, install it first:

git clone https://github.com/severinzhong/agent-data-cli
cd agent-data-cli
uv sync

Then load the bundled skills from this repository's skills/ directory and work from the repo root.

Important boundary:

  • source code belongs in the source workspace repo, typically agent-data-hub
  • keep agent-data-cli focused on core/cli/store/protocol work
  • do not install source runtime dependencies into the core project with uv add
  • use uv pip install or init.sh inside the source workspace instead

Workflow

1. Research

Identify the source type before making architecture decisions.

Classify it as one or more of:

  • RSS
  • API
  • HTML scraping
  • browser-driven
  • auth or session driven
  • interact capable

Research must confirm:

  • whether the source has a real channel concept
  • whether remote discovery and remote sync are distinct
  • how to identify unique content
  • whether the source has hierarchical or container-like content that should become content_relations
  • what time field is available
  • how pagination or incremental fetch works
  • what config is required
  • whether interact is actually possible

Use available web research, local fetch tools, and the repo's fetchers/ where appropriate.

2. Spec

Write a source-specific spec before implementation.

It must define:

  • source to resource mapping
  • supported capabilities
  • config fields and mode if needed
  • content normalization and dedup strategy
  • content_key strategy
  • whether update returns only direct content, or also context nodes and content_relations
  • whether the source needs relation_semantic values such as reply, contains, or list_item
  • storage requirements
  • error boundaries
  • CLI-visible semantics
  • testing scope

For native search/query views:

  • treat column names as a soft compatibility surface because multi-source and multi-channel aggregation merges by column header
  • prefer explicit names such as published_at, publisher, author, price, volume
  • avoid vague names such as time, source, value unless that meaning is genuinely exact
  • column order is mainly for readability; header naming is what determines merge behavior

3. Plan

Turn the approved spec into an implementation plan.

The plan must break work into:

  • failing tests to add first
  • source code units to implement
  • ContentSyncBatch construction path
  • CLI verification steps
  • persistence and audit verification

4. Approval

Wait for user approval after the spec and plan.

Do not jump from research straight to code.

5. Implement

Implement with TDD.

  • write failing tests first
  • verify the failure is correct
  • write minimal code
  • rerun focused tests

6. Verify

Before claiming completion, verify:

  • unit tests
  • CLI simulation tests
  • help output
  • capability and config behavior
  • persistence side effects
  • content_nodes / content_channel_links / content_relations side effects when update is involved
  • interact audit behavior when applicable

Read Next

  • references/source-contract.md for repository rules
  • references/source-type-rss.md for feed-like sources
  • references/source-type-api.md for JSON or HTTP API sources
  • references/source-type-browser.md for browser-driven sources
  • references/source-type-interact.md for remote side effects
  • references/source-testing.md for test matrix
  • references/source-review-checklist.md before final verification
安全使用建议
This skill is an authoring guideline and appears coherent with its purpose. It does not request secrets or special system privileges. Before following the install commands (git clone, npx skills add, uv sync, uv pip install, init.sh), manually inspect the referenced GitHub repository and any install scripts to confirm they are from a trusted source and contain no unexpected behavior. Be extra cautious when implementing sources that require browser automation or authentication: those flows may require credentials or cookies — store and grant those minimally and inspect any code that handles them. If you want higher assurance, ask the skill author for a link to the exact repo commit/tag or for vetted packaged releases rather than cloning a branch-less repo reference.
功能分析
Type: OpenClaw Skill Name: authoring-data-cli-source Version: 1.0.1 The skill bundle provides a structured framework and set of instructions for an AI agent to assist in developing data sources for the 'agent-data-cli' tool. The SKILL.md file enforces a strict workflow (Research, Spec, Plan, Approval, Implement, Verify) that requires explicit user approval before proceeding to implementation, which serves as a safety gate. The reference files (e.g., source-contract.md, source-type-api.md) provide architectural guidance for handling various data acquisition methods like APIs and RSS feeds. While the instructions involve development tasks such as cloning repositories and managing dependencies, they are well-aligned with the stated purpose and lack any indicators of malicious intent or unauthorized data exfiltration.
能力评估
Purpose & Capability
The name/description (authoring data-cli source) matches the SKILL.md and the included reference documents. The files describe source types, contracts, testing, and review checklists consistent with designing sources for agent-data-cli. There are no unrelated environment variables, binaries, or config paths requested.
Instruction Scope
The instructions are prescriptive about research, spec, plan, TDD, and verification and remain within the expected scope for authoring sources. They do reference cloning and working with the agent-data-cli repo and its fetchers, and recommend using local fetch tools and browser automation when needed. This is normal for the purpose, but the guidance does point the user to fetch and run third-party repo code (git clone, uv sync, uv pip install, init.sh), so users should inspect that upstream code before running it.
Install Mechanism
No formal install spec is included (instruction-only), which lowers risk. The README-style instructions show commands that clone or install code from a GitHub repository (https://github.com/severinzhong/agent-data-cli) and an npx-based skills add. GitHub and npx are common, but they will fetch and run remote code — this is expected for this skill but worth a quick manual review before executing those commands.
Credentials
The skill declares no required environment variables, credentials, or config paths. The references explicitly advise against ad-hoc environment reads and instruct sources to consume config via manifest/self.config. This is proportionate for an authoring guide.
Persistence & Privilege
The skill is not always-enabled and does not request persistent system presence. It is instruction-only and does not modify other skills or system-wide agent settings. Autonomous invocation is allowed by default (platform normal) but the skill does not request elevated or persistent privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install authoring-data-cli-source
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /authoring-data-cli-source 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
authoring-data-cli-source 1.0.1 - Expanded scope and clarified usage for broader source types, including finance, news, and social media. - Added guidance for installing the skill directly from skills.sh. - Updated documentation to clarify the separation of source and core/cli/store dependencies and workflows. - Detailed the current resource model and emphasized new contract points (e.g., use of content_nodes, content_relations, and relation_semantic). - Improved research and spec phases to cover hierarchical content and naming conventions for data columns. - Provided updated workflow and verification steps reflecting current project architecture.
v1.0.0
Initial release of authoring-data-cli-source. - Provides a strict step-by-step workflow for designing, implementing, and verifying new or redesigned agent-data-cli sources. - Emphasizes research, spec writing, and planning stages before implementation. - Details when and how to use the skill, including installation guidance. - Outlines approval gates, TDD requirements, and comprehensive verification steps. - Includes references to supporting documentation for deeper guidance.
元数据
Slug authoring-data-cli-source
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

authoring-data-cli-source 是什么?

Use when the user needs to add or redesign an agent-data-cli source for RSS, news, social media, finance, APIs, scraping, browser automation, authentication,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 249 次。

如何安装 authoring-data-cli-source?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install authoring-data-cli-source」即可一键安装,无需额外配置。

authoring-data-cli-source 是免费的吗?

是的,authoring-data-cli-source 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

authoring-data-cli-source 支持哪些平台?

authoring-data-cli-source 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 authoring-data-cli-source?

由 Severin Zhong(@severinzhong)开发并维护,当前版本 v1.0.1。

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