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 binggandata/bggg-skills --skill sif-keyword-trackergit clone --depth 1 https://github.com/binggandata/bggg-skillsWrote 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/binggandata/bggg-skills/sif-keyword-tracker)<a href="https://agentmods.dev/skills/binggandata/bggg-skills/sif-keyword-tracker"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/sif-keyword-tracker/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/binggandata/bggg-skills/sif-keyword-tracker"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/sif-keyword-tracker.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 | $0.00084 | $0.00700 |
| Opus 5 | $0.00042 | $0.00350 |
| Sonnet 5 | $0.00017 | $0.00140 |
| Haiku 4.5 | $0.00008 | $0.00070 |
Grade A, and why
sif-keyword-tracker 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 13d 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.
What it actually says
Sif 关键词动态跟踪（Skill 2）
通常由 Skill 1 Step 10 触发。单独运行时同样遵循 暂停点 ③（见 scout 的
amazon-expert-guide.md）。
对比基准选择（1~7 天窗口）
python "{scripts.check_history}" --asin {ASIN} --output-dir "{output_dir}" --curr-date {DATE}
返回 compare_prev_result_dir、compare_note、compare_days_apart。
| 规则 | 说明 |
|---|---|
| 优先 | 按原数据日期（文件夹 YYYYMMDD / 原始表后缀）在 1~7 天窗口内选上一期，如 20260610 → 20260611 |
| 同日多次 | 仅当没有可比的异日数据时，才对比同日前一次运行（同日重跑） |
| 超出窗口 | 回退到紧邻上一次异日数据（报告会标注 warning） |
注意：0611 是数据日期，不是「今天跑脚本的日子」。对比的是两期 Sif 导出数据，不是同日重复执行。
两轮流程（与 Skill 1 相同）
第一轮：对比 + stats
python "{scripts.compare_versions}" --auto --output-root "{output_dir}" --output-dir "{result_dir}" --asin {ASIN} --curr-date {DATE} --skip-word
暂停点 ③（强制）
读取 {ASIN}_tracker_stats_{DATE}.json，向用户解读：
- 新增 / 消失词（含 S 级变化）
- SS 稳定词、搜索量与份额波动
- 试投 / 暂停 / 观察 建议
用户确认后才写 insights_tracker.md（必须针对本次数据，勿用模板凑数）。
第二轮：生成 Word
python "{scripts.compare_versions}" --auto --output-root "{output_dir}" --output-dir "{result_dir}" --asin {ASIN} --curr-date {DATE} --insights-file "{result_dir}/insights_tracker.md"
报告结构
- 数据段（脚本）：变化摘要、新增/消失清单（含 S 级）、SS 稳定词、搜索量/份额表
- AI 段（Agent）：策略解读、逐词建议、投放调整行动清单（须反映暂停点 ③ 与用户达成的共识）
产出
{result_dir}/{ASIN}_词库更新报告_{DATE}.docx
What ships with it
2 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.
- 13d ago First seen · 61 lines · 84 tokens per session scan A 4677321d1175
sif-keyword-tracker is a skill published in the GitHub repository binggandata/bggg-skills (594 stars, last pushed 1mo ago), licensed MIT. It adds 84 tokens to every session and 700 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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