Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw.

Name: Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw.
Author: wandervine

作者 Wan Wei · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ✓ 安全检测通过

总下载

当前安装

版本数

在 OpenClaw 中安装

/install bbt-competitive-analysis

功能描述

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report sk...

使用说明 (SKILL.md)

Competitive Analysis

Use When

The competitor analysis tables already exist.
You need to poll competitive_crawl_trigger on a schedule.
You need standardized reports grouped by category.
You need to send summaries to a DingTalk robot.

Do not use this skill for:

one-off ad hoc analysis
open-ended research without database inputs
flexible report generation without a fixed template

Required Inputs

Database connection: COMPETITIVE_ANALYSIS_DSN
OSS endpoint: OSS_ENDPOINT
OSS bucket: OSS_BUCKET
OSS access key id: OSS_ACCESS_KEY_ID
OSS access key secret: OSS_ACCESS_KEY_SECRET
DingTalk webhook: DINGTALK_WEBHOOK
Optional DingTalk signing secret: DINGTALK_SECRET
In OpenClaw, prefer environment injection through skills.entries.competitive_analysis.env

Goal

Find unconsumed trigger rows where status='success'.
Load the last 6 months of product, review, and QA data.
Aggregate results by category.
Produce analysis_context.json for the host to continue narrative generation.
Generate a Markdown/HTML skeleton that follows the reference PDF structure.
Send a DingTalk summary.
Mark trigger rows as consumed after success.

Entry Points

Primary command:

python3 {baseDir}/scripts/run_report.py

Common arguments:

--category CATEGORY
--since-months 6
--limit 20

Files

SKILL.md: skill entry instructions
references/report-outline.md: report structure contract
references/data-contract.md: data contract and field expectations
references/openclaw-setup.md: OpenClaw setup example
scripts/run_report.py: main CLI
scripts/render_report.py: Markdown/HTML rendering
scripts/send_dingtalk.py: DingTalk delivery
analysis_context.json: structured analysis context for the host runtime

Rules

Follow the reference PDF for section order.
If fields are missing, keep the section and mark values as 未采集 or 待补充.
Keep the CLI stateless and let an external scheduler trigger it.
Do not call any external LLM API from the script.
Let the host runtime generate deeper narrative content from analysis_context.json and references/report-outline.md.
In OpenClaw, prefer host-managed environment injection over .env.

Minimal Workflow

Read references/data-contract.md.
Confirm that the trigger table already includes the consumption fields.
Configure skills.entries.competitive_analysis.env as shown in references/openclaw-setup.md.
Start a new OpenClaw session so the skill reloads.
Run python3 {baseDir}/scripts/run_report.py or invoke it from an external scheduler.
Read the generated analysis_context.json.
Let the host runtime generate the final narrative based on references/report-outline.md.
Validate the final output against the report outline.

Success Criteria

New successful trigger rows are detected.
Reports are generated per category.
Section structure matches the reference report.
DingTalk receives the summary message.
Trigger rows are marked as consumed.

安全使用建议

This package appears to do what it claims, but check these operational matters before installing: 1) Limit the PostgreSQL DSN to the minimum required privileges (SELECT on product/review/qa tables and UPDATE on the competitive_crawl_trigger consumption fields); avoid providing a superuser DSN. 2) Understand that providing OSS_ACCESS_KEY_* typically grants write/list access to a bucket — prefer a scoped/short-lived key and a dedicated bucket/prefix so generated report URLs cannot leak unrelated data. 3) The generated report URLs are likely public by default (SKILL uses public URL construction); verify OSS_PUBLIC_BASE_URL and bucket ACLs if you want artifacts private. 4) DingTalk webhook messages include links to report files — ensure the webhook and message contents are acceptable to your privacy rules. 5) Review and test in a staging environment first to confirm schema expectations (the skill will fail fast if required trigger columns are missing) and to avoid mass consumption of triggers in production. 6) Keep secrets out of shared openclaw.json or rotate them regularly. If you want a lower blast radius, run the skill under an account with tightly scoped DB/OSS permissions.

功能分析

Type: OpenClaw Skill Name: bbt-competitive-analysis Version: 1.0.0 The skill bundle is a legitimate enterprise tool designed for automated competitive analysis. It polls a PostgreSQL database for crawl triggers, aggregates product/review data, generates structured reports (HTML/Markdown), uploads them to Alibaba Cloud OSS, and sends summaries via DingTalk webhooks. The implementation in scripts like `run_report.py` and `upload_oss.py` uses standard libraries and follows safe practices, such as parameterized SQL queries and filename sanitization, with no evidence of malicious intent, data exfiltration, or prompt injection.

能力标签

cryptocan-make-purchases

能力评估

✓ Purpose & Capability

The name and description match the required resources and behavior: a PostgreSQL DSN to read/mark triggers, OSS credentials to upload reports, and a DingTalk webhook to send summaries. Required binary (python3) is appropriate.

✓ Instruction Scope

SKILL.md and the code limit runtime actions to database queries/updates, local report rendering, optional OSS upload, and DingTalk webhook posts. The README explicitly forbids calling external LLM APIs. No instructions ask the agent to read unrelated system files or transmit data to unexpected endpoints.

✓ Install Mechanism

This is an instruction-only skill with bundled scripts; there is no automated download or install step. Runtime Python dependencies (psycopg, optional oss2) are declared via code and fail with clear messages if missing — no external arbitrary downloads or extracted archives are used.

✓ Credentials

Requested env vars (COMPETITIVE_ANALYSIS_DSN, OSS_*, DINGTALK_WEBHOOK/SECRET) directly support DB access, artifact upload, and notification. Note: the script requires DB write privileges on the trigger table (to mark consumed/failed) — that is expected for this workflow but is a higher-privilege DB role than read-only.

✓ Persistence & Privilege

The skill does not request permanent inclusion (always:false) and does not modify other skills or system-wide settings. It updates its own domain (database trigger rows and writes report files) as part of normal operation.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install bbt-competitive-analysis
安装完成后，直接呼叫该 Skill 的名称或使用 /bbt-competitive-analysis 触发
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

v1.0.0

**Initial release with automated, database-driven competitor analysis workflow for OpenClaw:** - Adds CLI and scripts to poll triggers, aggregate 6 months of product/review/QA data, and generate category-based analysis. - Outputs structured `analysis_context.json`, Markdown/HTML report skeleton, and sends a DingTalk summary. - Includes setup/config references and database/data contract documentation. - Designed for scheduled, standardized analysis with external host runtime handling final narrative generation. - Ensures all outputs and workflow steps are compatible with OpenClaw platform requirements.

元数据

Slug bbt-competitive-analysis

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw. 是什么？

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report sk... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 89 次。

如何安装 Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw.？

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

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw. 是免费的吗？

是的，Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw. 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw. 支持哪些平台？

Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw. 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 Poll competitive crawl triggers, aggregate the last 6 months of product, review, and QA data by category, produce structured analysis context and a report skeleton, upload outputs to OSS, then send a DingTalk summary. Use for database-driven scheduled competitor analysis in OpenClaw.？

由 Wan Wei（@wandervine）开发并维护，当前版本 v1.0.0。