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

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name: competitive_analysis description: 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. compatibility: Requires Python 3.10+, PostgreSQL access, OSS access, and a DingTalk webhook. metadata: {"openclaw":{"requires":{"bins":["python3"],"env":["COMPETITIVE_ANALYSIS_DSN","DINGTALK_WEBHOOK","OSS_ENDPOINT","OSS_BUCKET","OSS_ACCESS_KEY_ID","OSS_ACCESS_KEY_SECRET"]}},"owner":"bbt","skillType":"enterprise","reportTemplate":".docs/竞品分析/BUBBLETREE儿童枕市场机会分析报告.pdf"}

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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

1. Find unconsumed trigger rows where status='success'. 2. Load the last 6 months of product, review, and QA data. 3. Aggregate results by category. 4. Produce analysis_context.json for the host to continue narrative generation. 5. Generate a Markdown/HTML skeleton that follows the reference PDF structure. 6. Send a DingTalk summary. 7. 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

1. Read references/data-contract.md. 2. Confirm that the trigger table already includes the consumption fields. 3. Configure skills.entries.competitive_analysis.env as shown in references/openclaw-setup.md. 4. Start a new OpenClaw session so the skill reloads. 5. Run python3 {baseDir}/scripts/run_report.py or invoke it from an external scheduler. 6. Read the generated analysis_context.json. 7. Let the host runtime generate the final narrative based on references/report-outline.md. 8. 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.

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.