Easel is an open-source content workspace and AI agent for managing social media from trend discovery through planning, content creation, publishing, and performance analysis. It is intended for social media creators and maintains separate account profiles, content projects, platform adaptations, and publishing records. The catalogue skills support these content and publishing workflows.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ZJU-REAL/Easelnpx agentmods add skills/zju-real/easel/skill-topic-evaluatorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zju-real/easel/skill-topic-evaluator)<a href="https://agentmods.dev/skills/zju-real/easel/skill-topic-evaluator"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-topic-evaluator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zju-real/easel/skill-topic-evaluator"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-topic-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00173 | $0.01828 |
| Opus 5.5 | $0.00069 | $0.00731 |
| Sonnet 5.5 | $0.00035 | $0.00366 |
| Haiku 4.5 | $0.00017 | $0.00183 |
Grade A, and why
skill-topic-evaluator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
选题可行性评估
用户给一个选题，多维度打分评估值不值得做，输出"做/不做/改方向"建议。
评分口径：本 SKILL 与
skill-content-matrix共用../../shared/scoring-dimensions.md的统一七维 + 标尺 + 权重。matrix 做批量池快评，本 SKILL 做单条深评（逐维展开详细分析）。两者维度、标尺、综合分阈值完全一致。
输入
| 参数 | 必填 | 说明 |
|---|---|---|
| 选题 | 是 | 用户想做的选题标题或描述 |
| 目标平台 | 否 | 小红书 / 抖音 / 微博 / 知乎 / B站 / 公众号（有 Profile 时自动提取） |
| 补充背景 | 否 | 选题来源、灵感、竞品参考等 |
输出
# 选题评估报告
## 选题: {用户给出的选题}
目标平台: {platform}
评估时间: {date}
## 七维评分
按 `../../shared/scoring-dimensions.md` 的统一维度和标尺打分，逐维带详细依据：
| 维度 | 得分 | 说明 |
|------|------|------|
| 流量潜力 | X/10 | {痛感强度 + 平台话题热度 + 搜索需求} |
| 账号匹配 | X/10 | {与定位、受众、内容体系的契合度} |
| 竞争差异化 | X/10 | {同类饱和度 + 能否切差异化角度，高分=竞争低} |
| 时效价值 | X/10 | {常青 vs 一次性热点，高分=常青} |
| 变现空间 | X/10 | {能否自然接广告/带货/引流} |
| 制作成本 | X/10 | {资源与技能门槛，反向，高分=易做} |
| 合规风险 | X/10 | {敏感度风险，反向，高分=低风险} |
综合得分: XX/100（按 scoring-dimensions.md 的推荐权重加权换算）
## 结论: {做 / 不做 / 改方向}
{一句话总结判断理由}
## 详细分析
### 流量潜力分析
{该选题在目标平台的搜索热度、话题讨论量、同类爆款情况}
### 竞争差异化
{头部玩家是否占位、中腰部突围空间、差异化切入点}
### 时效价值
{常青还是短期热点、最佳发布窗口}
### 变现路径
{可接的商业合作类型、引流转化链路}
### 制作可行性
{需要的素材/设备/专业知识/时间投入}
### 账号契合度
{与创作者内容体系和粉丝画像的匹配程度}
### 合规风险
{敏感赛道标注 ⚠️ 及合规建议}
## 优化建议（改方向时提供）
1. {角度调整建议}
2. {形式调整建议}
3. {时机调整建议}
## 替代选题推荐（不做时提供）
1. {替代选题 A} — {推荐理由}
2. {替代选题 B} — {推荐理由}
评分标尺
统一的七维标尺（1-3 / 4-6 / 7-8 / 9-10 分档）见 ../../shared/scoring-dimensions.md，本 SKILL 直接套用，不另立口径。
执行步骤
-
解析选题意图
- 提取用户给出的选题核心关键词和主题方向
- 识别选题类型：知识干货、情绪共鸣、热点追踪、人设展示、带货种草、争议讨论
- 如未指定平台，根据选题类型推断最适合的平台，或询问用户
-
逐维深度评估
按
../../shared/scoring-dimensions.md的七维逐一展开，每维给分并写明具体依据：- 流量潜力 — 平台话题热度、搜索需求、同类历史表现、传播性、算法偏好
- 账号匹配 — 与定位/人设/赛道、已有内容承接、粉丝画像兴趣、长期成长影响
- 竞争差异化 — 同类饱和度、头部是否占位、中腰部突围空间、可切的新角度
- 时效价值 — 常青 vs 时效；常青评估长期搜索价值，时效评估衰减速度和最佳窗口
- 变现空间 — 商业价值（品牌/带货/知识付费/引流）、受众付费意愿、变现路径是否自然
- 制作成本（反向）— 所需素材/设备/专业知识/周期，创作者现有能力能否覆盖
- 合规风险（反向）— 敏感赛道标注 ⚠️ 及合规建议
-
综合判断与输出
- 按 scoring-dimensions.md 的推荐权重计算综合得分（满分 100）
- 按统一阈值给结论：
- ≥ 70：做，立刻排期
- 50-69：改方向，调整后再评估（附至少 2 条优化建议）
- < 50：不做，附至少 2 个替代选题
- 输出完整评估报告
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 144 lines · 173 tokens per session scan A 0e1d24d42650
skill-topic-evaluator is a skill published in the GitHub repository ZJU-REAL/Easel (4,052 stars, last pushed 2d ago), licensed Apache-2.0. It adds 173 tokens to every session and 1,828 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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