Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sutchan/Agent-Skills-Hub --skill self-media-content-strategygit clone --depth 1 https://github.com/sutchan/Agent-Skills-HubWrote 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/sutchan/agent-skills-hub/self-media-content-strategy)<a href="https://agentmods.dev/skills/sutchan/agent-skills-hub/self-media-content-strategy"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/self-media-content-strategy/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/sutchan/agent-skills-hub/self-media-content-strategy"><img src="https://agentmods.dev/badge/skills/sutchan/agent-skills-hub/self-media-content-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.00722 |
| Opus 5 | $0.00039 | $0.00361 |
| Sonnet 5 | $0.00016 | $0.00144 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
self-media-content-strategy 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 2d 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.
This is a copy
100% identical to self-media-content-strategy — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
内容策略
输入
优先读取已有账号说明、历史内容和数据。只有缺失且会改变策略时，确认：
- 领域、人设和目标受众。
- 业务目标和本阶段优先级。
- 已有平台、账号阶段和历史表现。
- 更新频率、团队规模和可投入时间。
- 可持续的一手证据和内容来源。
- 商业目标、品牌边界和不能做的内容。
可从 account-profile-template.md 建立账号档案。
方法
1. 写清定位承诺
用一句话说明“谁，为谁，持续提供什么价值，凭什么相信”。定位必须能约束选题，不写空泛口号。
2. 将目标映射为内容类型
| 类型 | 主要作用 | 常见形式 |
|---|---|---|
| 涨粉 | 被新用户发现 | 教程、避坑、反常识、趋势解释 |
| 互动 | 激活现有受众 | 问题、选择、争议、共同经历 |
| 信任 | 建立专业度和人格 | 案例、幕后、复盘、深度方法 |
| 转化 | 承接产品或服务 | 测评、答疑、案例、合作内容 |
不要套用固定比例。根据账号阶段、历史数据和产能提出初始比例，并明确这是待验证假设。
3. 建立选题池
每条选题至少记录：
- 母题和一句话钩子。
- 内容目标和目标受众。
- 主平台和可扩展平台。
- 一手证据和仍缺证据。
- 时效窗口、优先级和预计成本。
- 是否属于系列。
使用 topic-pool-template.md。优先留下可持续生产、能强化定位和有真实证据的选题。
4. 设计系列栏目
将零散选题收敛为 2 到 4 个栏目。每个栏目写清内容承诺、证据来源、适合平台、更新频率和停止条件。
5. 生成内容日历
先分配产能，再排日期。保留热点插槽，避免把未来内容全部写死。每个周期只设置一个可归因的实验变量。使用 content-calendar-template.md。只有用户明确要求提醒且当前环境提供调度能力时，才创建排期提醒。
输出
交付：
- 一句话定位。
- 阶段目标和内容配比假设。
- 2 到 4 个栏目。
- 选题池。
- 周度或月度日历。
- 成功指标、停止条件和本周期唯一实验。
如果历史数据不足，明确写“建立基线”，不伪造精确目标。
What ships with it
4 files 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.
- 2d ago First seen · 71 lines · 79 tokens per session scan A 0a159e7f2b6b
self-media-content-strategy is a skill published in the GitHub repository sutchan/Agent-Skills-Hub (2 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 722 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to self-media-content-strategy, differing in 0 lines, and is treated as a copy.
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