据 AlphaMoat 统计,截至 2026-10,Last30days 累计下载 23,003 次、获 30 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 1,234 位,在「数据分析」分类(915 个)中排名第 114 位。
列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。
| 平台 | 累计下载 | 星标 | 评论 |
|---|---|---|---|
| ClawHub ↗ | 4,301 | - | - |
| SkillHub ↗ | 2.3万 | 30 | — |
各平台的计数口径不同,统计时间不一致。
从 Reddit、X、YouTube、Hacker News、GitHub 等来源合并近 30 天的公开内容,去重聚类后给出排序简报与引用。适合看发布反响、竞品对比与趋势,但只想要单一官方来源、不需要社区信号的任务不适用。 来源包括社区平台,结论反映可见的社区信号;若只接受单一官方来源,应改用该来源而非本技能。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-10 | 2.3万 | — | 30 |
| 2026-09 | 1.4万 | — | 30 |
| 2026-08 | 1.2万 | +5.5% | 30 |
| 2026-07 | 1.1万 | — | 30 |
| 2026-05 | 9,548 | — | 29 |
| 2026-03 | 8,102 | — | 27 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: last30days description: Research the last 30 days across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and grounded web search. Returns a ranked, clustered brief with citations. Use when the task needs recent social evidence, competitor comparisons, launch reactions, trend scans, or person/company profiles. author: AIsa version: 1.0.4 license: MIT homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/last30days user-invocable: true primaryEnv: AISA_API_KEY
requires:
bins:
- python3
- bash
env:
- AISA_API_KEY
metadata:
aisa:
emoji: 🔎
requires:
bins:
- python3
- bash
env:
- AISA_API_KEY
optionalEnv:
- LAST30DAYS_PLANNER_MODEL
- LAST30DAYS_RERANK_MODEL
- LAST30DAYS_FUN_MODEL
- AISA_MODEL
- AISA_BASE_URL
- XIAOHONGSHU_API_BASE
- LAST30DAYS_REASONING_PROVIDER
- INCLUDE_SOURCES
- LAST30DAYS_X_BACKEND
- LAST30DAYS_YOUTUBE_TRANSCRIPTS
- LAST30DAYS_REDDIT_COMMENTS
- GH_TOKEN
- GITHUB_TOKEN
- LAST30DAYS_CONFIG_DIR
- LAST30DAYS_DEBUG
primaryEnv: AISA_API_KEY
compatibility:
- openclaw
- claude-code
- hermes
openclaw:
emoji: 🔎
requires:
bins:
- python3
- bash
env:
- AISA_API_KEY
optionalEnv:
- LAST30DAYS_PLANNER_MODEL
- LAST30DAYS_RERANK_MODEL
- LAST30DAYS_FUN_MODEL
- AISA_MODEL
- AISA_BASE_URL
- XIAOHONGSHU_API_BASE
- LAST30DAYS_REASONING_PROVIDER
- INCLUDE_SOURCES
- LAST30DAYS_X_BACKEND
- LAST30DAYS_YOUTUBE_TRANSCRIPTS
- LAST30DAYS_REDDIT_COMMENTS
- GH_TOKEN
- GITHUB_TOKEN
- LAST30DAYS_CONFIG_DIR
- LAST30DAYS_DEBUG
primaryEnv: AISA_API_KEY
---
# last30days 📰
**30-day multi-source research brief for autonomous agents. Powered by AIsa.**
One API key. Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and grounded web search — merged into a single ranked brief.
## Compatibility
Works with any [agentskills.io](https://agentskills.io)-compatible harness, including:
- **Claude Code** and **Claude**
- **OpenAI Codex**
- **Cursor**
- **Gemini CLI**
- **OpenCode**, **Goose**, **OpenClaw**, **Hermes**
- and other harnesses that implement the [Agent Skills specification](https://agentskills.io/specification)
Requires Python 3, a POSIX shell, and `AISA_API_KEY` (get one at [aisa.one](https://aisa.one)).
## Example requests
### Trend scan
```text
"last30days OpenAI Agents SDK"
```
### Competitor comparison
```text
"last30days Claude Code vs Codex"
```
### Person or company profile
```text
"last30days Peter Steinberger"
```
### Launch reaction
```text
"last30days GPT-5 launch --deep"
```
### Prediction-market angle
```text
"last30days bitcoin price"
```
## Quick start
```bash
# 1. Export your AIsa key
export AISA_API_KEY=sk-...
# 2. First-run setup (interactive — picks planner / rerank / fun-scorer models)
bash scripts/run-last30days.sh setup
# 3. Research a topic
bash scripts/run-last30days.sh "OpenAI Agents SDK"
```
## Common flags
```bash
# Low-latency profile (fewer candidates per source)
bash scripts/run-last30days.sh "$ARGUMENTS" --quick
# Higher-recall profile
bash scripts/run-last30days.sh "$ARGUMENTS" --deep
# Machine-readable output (full plan + candidates + clusters)
bash scripts/run-last30days.sh "$ARGUMENTS" --emit=json
# Restrict to specific sources
bash scripts/run-last30days.sh "$ARGUMENTS" --search=reddit,x,grounding
# Check provider / source availability
bash scripts/run-last30days.sh --diagnose
```
## Inputs and outputs
**Input.** A topic, person, company, product, or comparison — for example: `OpenAI Agents SDK`, `OpenClaw vs Codex`, or `Peter Steinberger`.
**Output.** A markdown brief by default, or JSON with:
- `query_plan` — planner-generated subqueries and source weights
- `ranked_candidates` — reranked candidate pool with scores
- `clusters` — semantically grouped findings
- `items_by_source` — per-source item lists with dates, engagement, and URLs
- `provider_runtime` — which models and retrieval backends ran
- `errors_by_source` — any source-level failures (fail-soft)
## When to use
- You need recent evidence from the last 30 days about a person, company, product, market, tool, or trend.
- You want a ranked competitor comparison, launch-reaction summary, creator or community sentiment scan, or shipping update.
- You want a structured JSON brief to feed into another agent.
## When not to use
- Timeless reference questions with no recent-evidence requirement.
- Tasks where you only want one official source and do not want social or community signals.
## Capabilities
- **AISA-powered**: planner (structured JSON query plan), reranker (relevance ordering), fun-scorer (meme or quirk signal), and hosted retrieval for X, YouTube, TikTok, Instagram, Polymarket, and grounded Tavily web search.
- **Public paths (no extra credentials)**: Reddit and Hacker News.
- **GitHub** via the official API when `GH_TOKEN` or `GITHUB_TOKEN` is set — optional.
- **Fail-soft**: if one source errors or times out, the brief still renders with the others and notes the gap.
## Model configuration
The skill makes three LLM calls per run. Each role is independently pinnable via `~/.config/last30days/.env`:
```bash
LAST30DAYS_PLANNER_MODEL=qwen-flash # fast + reliable JSON
LAST30DAYS_RERANK_MODEL=qwen-plus-2025-12-01 # quality ranking
LAST30DAYS_FUN_MODEL=qwen-flash # cheap vibes
```
Or set `AISA_MODEL=...` for a single model across all three roles. Run `last30days setup` to pick interactively — the picker fetches the live catalog from [aisa.one/docs/guides/models](https://aisa.one/docs/guides/models).
## API reference
last30days calls the following AIsa endpoints directly. See the [full API Reference](https://aisa.one/docs/api-reference) for the complete catalog.
- [OpenAI Chat / `createChatCompletion`](https://aisa.one/docs/api-reference/chat/createchatcompletion) — planner, reranker, fun-scorer
- [Twitter Advanced Search](https://aisa.one/docs/api-reference/twitter/get_twitter-tweet-advanced-search) — X retrieval
- [YouTube Search](https://aisa.one/docs/api-reference/search/get_youtube-search) — YouTube retrieval
- [Tavily Search](https://aisa.one/docs/api-reference/search/post_tavily-search) — grounded web
- [Polymarket Markets](https://aisa.one/docs/api-reference/prediction-market/get_polymarket-markets) — prediction-market retrieval
Reddit and Hacker News use their respective public APIs directly (no AISA proxy required).
## Requirements
- **Python 3.12+**
- **`AISA_API_KEY`** — required. Get one at [aisa.one](https://aisa.one).
- **`GH_TOKEN` / `GITHUB_TOKEN`** — optional, enables the GitHub source.
```bash
# Pin an interpreter ≥ 3.12
for py in /usr/local/python3.12/bin/python3.12 python3.14 python3.13 python3.12 python3; do
command -v "$py" >/dev/null 2>&1 || continue
"$py" -c 'import sys; raise SystemExit(0 if sys.version_info >= (3, 12) else 1)' || continue
LAST30DAYS_PYTHON="$py"
break
done
```
中国金融机构数据爬取与分析综合 Skill v12.0.0。2334家/43大类机构名单、60+财经媒体源舆情、 A股报告/公告/研报、60+种查询多源金融数据(自动降级+缓存)、爬取调度与网页操作增强、 实时数据中枢、24 技术指标引擎、多源证据融合、110 MCP 工具 + 10 Resource + 14 Prompt。 🆕 v12.0.0 四条线(信息获取 / 打包下载 / 数据分析 / 登录): 编排:gather_info() 一次跑完"路由→抓取→去占位→质量分→能力边界→回执→打包", 8 类意图各带问法、输入输出、典型耗时与 not_for(说清哪件事做不了)。 诚实:反爬占位条目一律剔除并记账,空结果报 E_SOURCE_EMPTY,不把假数据判成 B 级成功。 交付:包内自带 manifest.json(逐文件 sha256),verify 查得出改动/缺失/多塞, 批量下载单条失败不中断整批,失败清单可直接只重下那几条。 登录:Cookie 由用户导出或扫码接管,档案化 + 健康度四档,对外输出全脱敏(值永不回显)。 何时触发:爬取机构、舆情爬取、东方财富、公告下载、实时行情、技术指标、统一查询、 交付包、批量重下、我已经登录了、登录态还有效吗(完整触发词见下方 auto_trigger)。
大宗商品价格数据查询技能。用于查询大宗商品期现货价格、宏观经济指标、产业链供需数据、进出口库存数据、金融市场数据等。当用户询问:(1) 期货/现货价格查询,如"螺纹钢价格"、"铜期货收盘价"; (2) 历史行情或趋势分析;(3) 区域或企业价格对比;(4) 宏观经济指标,如GDP、CPI;(5) 产业链上下游数据;(6) 多市场金融数据(期权、股票、债券、外汇);(7) 结算确认单生成时触发此技能。
32种图表,3 个主题,2步生成: 1.上传数据 — 将 CSV / Excel / JSON 等格式的数据文件拖入对话框; 2.查看结果 — 交互式图表(HTML),可保存图表为图片.
AdMapix 的取数层:把广告与应用相关的查询转成接口调用,只回原始 JSON。
论文配图一键生成:一条命令从 JSON/CSV/Excel 生成顶刊级学术图表,科研绘图 与期刊配图全场景覆盖。40 种图表(森林图/KM 生存曲线(自动风险表)/竞争风险累计发生率/ROC/校准 曲线/决策曲线 DCA/列线图/RCS 非线性剂量反应/亚组分析森林图/RMST 限制平均生存时间/Galbraith 径向图/火山图/雨云图/山脊图/哑铃图/游泳图(逐例时长+里程碑)/ 肿瘤缓解瀑布图/UpSet 交集/PRISMA 流程图/漏斗/BA 一致性/PCA/热图/箱线/小提琴/ 组合图/Cox 多因素回归森林图等),9 套配色(含色盲安全 Okabe-Ito 与期刊色板),9 种期刊投稿预设, YAML 流水线一条命令出全套论文图,一次渲染多格式导出(TIFF/PNG/PDF), PDF 重叠门禁,600dpi。纯本地运行,数据不出机,中文零配置。适用:写论文/ 投稿要配图、把数据变成可投稿图表、批量重出全套图。不适用:文本润色/翻译/ 改写(请用写作类工具);商业报表/仪表盘/海报装饰;无数据支撑的纯示意图。
Intelligent chart generation and data analysis skill. Reads user-supplied data files (CSV/Excel/JSON), analyzes data characteristics with LLM assistance, auto-recommends and generates interactive ECharts visualizations. Use when the user asks to analyze data, generate charts, create visualizations, or work with tabular data files.
全球智能投研工具(stock-researcher)v13.0:多源数据 + 概率预测 + 可导出报告。 核心能力: - 多源数据路由:按市场/类别选源,逐级降级;每个结果标注来源与时效等级;源不可用时明确标 unavailable - 全资产量化:股票/ETF/场外基金/指数/期货(期限结构、基差、持仓、折溢价)多因子分析 - 概率预测:1d/5d/1M/3M/6M 经验分布 + 分位数 + 方向概率 + 区间,kNN 邻域收缩融合全局分布; 样本不足自动弃权,可按历史命中样本(同桶)做概率校准 - 证据链简报:每条结论绑定来源、时间与数据质量,冲突信号保留而不抹平 - 报告导出:Word/PPT/PDF/Markdown/JSON/纯文本,纯标准库渲染 + 可选第三方引擎 - MCP Server 24 工具,Claude Code 可直接调用;知识库 12 适配器双向同步 三条铁律: - 数据不装:拿不到就报不可用,不拿 0 价、不拿空表冒充 - 预测不装:概率来自历史相似条件的经验分布,样本不足就说不足,不硬给方向 - 评分≠盈利保证:任何结论都带免责声明
走 Tavily 检索公开网页,返回带链接与摘要的结构化结果。
数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。