Content Performance Analyst

Runs a monthly analysis of project-level content output and produces a structured report with optimization suggestions.

When to use

• Need to tally last calendar month's content output, product-line coverage, and type distribution.
• Need to compare the planned schedule against actual output.
• Need observations based on available effect data.
• Need topic and knowledge-base adjustment suggestions for next month.

Do not use

• Single-article pre-review — handled by content-compliance-reviewer.
• Auto-modifying the content calendar, published content, or product priority.
• Fabricating numbers when effect data is missing.

Input

calendar_path:
published_dir:
channel_data: path or null
product_line_map:
performance_profile:
  - product_priority:
  - content_type_weights:
  - channel_distribution_targets:
analysis_month: YYYY-MM

When effect data is missing, analyze only observable dimensions.

Workflow

Step 1: [Deterministic] Confirm scope

Analyze the previous calendar month by default. Confirm the calendar, published directory, and effect data.

Step 2: [Deterministic] Collect data

1. Scan the published-content directory; extract title, date, product, type, channel.
2. Read the schedule; compare plan vs actual output.
3. If channel-effect data exists, extract reads, saves, forwards, and read-completion metrics.
4. Check the knowledge base for recent updates and backlog.

Step 3: [LLM] Analyze

• Output completion: plan vs actual.
• Product-line coverage: article count and type per product.
• Product-priority alignment.
• Content-type distribution.
• Effect-data boundaries (mark DATA_MISSING when missing).
• Knowledge-base health.

Step 4: [LLM] Produce monthly report

# Content Monthly Report: YYYY-MM

## Output overview

| Date | Title | Product | Type | Channel | Status |
|---|---|---|---|---|---|

## Plan vs actual

- Planned / Produced / Deferred / Missing.

## Product-line coverage

| Product | Count | Type mix | Priority alignment |
|---|---:|---|---|

## Performance data (if available)

| Article | Reads | Saves | Forwards | Read-completion |
|---|---|---|---|---|

Mark missing data as DATA_MISSING.

## Key findings

- Positive.
- Gaps.
- Action items.

## Recommendations for next month

- Topic suggestions.
- Product and format adjustments.
- Knowledge-base maintenance.

## Knowledge-base health

- Recently updated:
- Thin sections:
- Stale entries:

Step 5: [Deterministic] Save

Save to: reports/YYYY-MM-monthly-report.md

Hard Rules

1. Never fabricate performance data. Missing data is marked DATA_MISSING.
2. Never modify the content calendar or trigger downstream writing.
3. Product-line analysis uses the private product map, not inference from titles.
4. Dataset gaps must be reported with the observed scope and data boundary.
5. Human confirmation required before the plan team acts on recommendations.
6. Every finding must reference a date, file or data point.

Failure Handling

| Scenario | Action | |---|---| | Calendar missing | Stop; report the missing file | | Published directory empty | Output zero-production report | | Channel data missing | Analyze only output dimensions | | Knowledge-base not found | Skip the health section | | Product map missing | Use title-based inference with a boundary note |

Output Format

reports/YYYY-MM-monthly-report.md

Verification

• [ ] Output count matches actual directory scan.
• [ ] Plan-vs-actual uses the calendar as reference.
• [ ] Every performance figure has an observable source or is marked DATA_MISSING.
• [ ] Recommendations reference concrete gaps.
• [ ] Report does not trigger downstream tasks.

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中文摘要

Content Performance Analyst 做项目级内容的月度分析，输出结构化报告与下月优化建议。统计产出完成度、产品线覆盖、类型分布与效果数据（缺失标 DATA_MISSING），对比排期与实际，检查知识库健康度。硬性规则：不编造数据、不改排期、产品线分析用私有映射而非标题推断、每条发现须有日期/文件/数据点支撑。