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
- Scan the published-content directory; extract title, date, product, type, channel.
- Read the schedule; compare plan vs actual output.
- If channel-effect data exists, extract reads, saves, forwards, and read-completion metrics.
- 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_MISSINGwhen 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
- Never fabricate performance data. Missing data is marked
DATA_MISSING. - Never modify the content calendar or trigger downstream writing.
- Product-line analysis uses the private product map, not inference from titles.
- Vendor- or platform-reported metrics must carry a "reported by {party}, not independently verified" caveat; a bare number is a fail.
- Dataset gaps must be reported with the observed scope and data boundary.
- Human confirmation required before the plan team acts on recommendations.
- 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.
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。
