本文へ移動
cccskills
無料GitHub で公開

bggg-data-reddit

Collect auditable Reddit search results and full comment trees at scale, preserve the source JSON, and normalize posts and comments into analysis-ready JSONL. Use for VOC, market research, community discovery, keyword snowballing, or any task that needs Reddit post and comment text without inventing unavailable fields. Reddit data layer of the bggg VOC suite (shared project folder; orchestrated by industry-orchestrator, reported by bggg-voc-report).

インストール方法を見る

含まれるファイル(6)

  • SKILL.md5.0 KB
  • agents/openai.yaml263 B
  • references/chrome_same_origin.md1.6 KB
  • references/schema.md827 B
  • scripts/build_request_plan.py2.8 KB
  • scripts/normalize_reddit.py9.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

BGGG Reddit Data

Use Reddit's public JSON listings through a logged-in or public Chrome tab when direct shell access is blocked. Save every response before transforming it, then normalize locally with the bundled script.

VOC Project Layout(bggg 系列共用)

bggg VOC 系列 skill(bggg-data-amazon / bggg-data-reddit / bggg-data-x / bggg-voc-report / industry-orchestrator)共用一个项目文件夹,让多平台数据规整到同一处、下游分析零改路径。开工先确定项目根目录 <project>(用户指定,或新建 voc-<产品或主题slug>/),并从 <project> 根目录执行本 skill 的全部命令(下文相对路径都基于它):

<project>/
  PROJECT.md            # 研究简报 + 决策日志(编排 skill 维护;单独使用可省)
  config/               # 采集目标:amazon_targets.tsv / reddit_queries.tsv / x_queries.tsv / keywords.txt
  work/<platform>/…     # 各平台原始证据、attempt 日志、request plan、manifest
  data/raw/             # 各平台规范化 JSONL(统一行契约,分析共用层)
  data/clean|coded/     # 下游清洗与编码(industry-orchestrator 维护)
  output/               # 报告与交付物(bggg-voc-report 写 output/report/)

本 skill 的落点:config/reddit_queries.tsv → work/reddit/(request plan 与 manifest)+ data/raw/source_json/(未修改源响应)→ data/raw/reddit_<lang>_<date>.jsonl。

Workflow

  1. Define a small query file and build a request plan:
python3 scripts/build_request_plan.py \
  --queries config/reddit_queries.tsv \
  --output work/reddit/request_plan.json

Use tab-separated columns query, lang, and round. lang and round are optional.

  1. Open https://www.reddit.com/ in Chrome. Fetch each planned URL from that Reddit page with same-origin fetch, including pagination through data.after. Do not read or export browser cookies, local storage, profile data, or credentials.

  2. Save each unmodified response under data/raw/source_json/ and append one manifest row per saved file. Follow references/chrome_same_origin.md for the browser loop and manifest format.

  3. Rank directly relevant parent posts by engagement and topical fit. Download complete comment trees for the highest-value parents with /comments/{post_id}.json?limit=500&depth=10&sort=top&raw_json=1. Treat replies as clustered under their parent thread; do not count thousands of replies from one viral thread as independent market prevalence.

  4. Normalize and validate:

python3 scripts/normalize_reddit.py \
  --manifest work/reddit/source_manifest.jsonl \
  --output data/raw/reddit_EN_2026-07-25.jsonl \
  --summary work/reddit/normalize_summary.json
  1. Report query count, parent-post count, comment count, unique rows, deleted/removed rows skipped, unresolved more nodes, HTTP failures, and parent-thread concentration.

Quality Rules

  • Keep raw source JSON immutable. Write retries to new files rather than overwriting evidence.
  • Use the Reddit fullname (t3_… or t1_…) as native_id; derive source_id with SHA-256.
  • Preserve exact text_raw. Put translations or labels in downstream clean/coded files.
  • Skip [deleted], [removed], empty bodies, ads, and promoted posts; count every exclusion.
  • Search listings are discovery samples, not complete census data. Record sort, time window, query, page cursor, and collection time.
  • Stop pagination only when after is null, the requested cap is reached, or repeated cursors/no-new-ID protection fires.
  • Respect login walls, rate limits, and challenge pages. Back off and record the failure; never bypass authentication or change the user's account.
  • Deduplicate exact native IDs first. Use similarity dedupe only in a later cleaning stage so raw evidence remains auditable.
  • For prevalence statistics, cap or weight rows per parent thread and disclose the rule.

Degradation

If Chrome same-origin fetching is unavailable, try a normal public Reddit JSON request with a conservative user agent. If it is blocked, deliver the request plan and collection manifest with the exact failure; do not substitute search-engine snippets for full Reddit comments.

Resources

  • scripts/build_request_plan.py: create deterministic search request and manifest templates.
  • scripts/normalize_reddit.py: parse saved search listings and nested comment trees into JSONL.
  • references/chrome_same_origin.md: Chrome collection loop, checkpointing, and manifest contract.
  • references/schema.md: normalized row and audit requirements.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

把图片、截图、海报、PPT 页面截图、HTML 或 SVG 设计稿转换成可编辑 PPTX 的 Codex skill。 当用户需要 image2ppt、image2pptx、图片转 PPT、截图转 PPT、PNG/JPEG 转可编辑幻灯片、 HTML/SVG 转 PPTX、把设计图拆成图片组件和文本框再拼成 PowerPoint、 或希望复用 Codex 内置生图能力把平面图重建为多元素可编辑 PPT 时,应该使用此 skill。 在 Codex 中处理二进制图片时,默认结合 imagegen skill:先用 Codex 视觉理解版式, 再用内置图片生成/编辑能力生成或清理多个组件图片,最后用本 skill 的脚本拼装 PPTX。

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

把一张或多张图片整理成 PSD 图层文件的创作与转换 skill。当用户需要 image2psd、图片转 PSD、 多张图片拼成 PSD、海报/设计稿拆成多个图层、白底转透明、颜色聚类拆层、把 Codex/AI 生图结果拆成元素图再合成 PSD、 或希望输出 layered PSD、可在 Photoshop/Photopea 中编辑的分层栅格文件时,应该使用此 skill。 在 Codex 中使用时,默认结合 imagegen skill:先用 Codex 视觉/生图能力理解、补齐或重建元素,再用本 skill 的脚本落地 PSD。

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

Collect Amazon.com written product reviews at scale through Woot's public review AJAX route, retain every attempt and error log, reconcile partial runs, and normalize exact review text into auditable JSONL. Use for VOC, competitor review mining, low-star complaint analysis, listing research, or batch ASIN review acquisition without Amazon credentials. Amazon data layer of the bggg VOC suite (shared project folder; orchestrated by industry-orchestrator, reported by bggg-voc-report).

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

Collect auditable public X/Twitter posts by controlling the user's already logged-in Chrome, searching X's rendered web interface, scrolling visible results, and extracting original post text and metadata from DOM into normalized JSONL. Use for VOC research, social listening, multilingual keyword discovery, competitor monitoring, or historical search when X requires the user's browser session and cookies, credentials, internal GraphQL, paid APIs, or search-engine snippets must not be exported or used. X data layer of the bggg VOC suite (shared project folder; orchestrated by industry-orchestrator, reported by bggg-voc-report).

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

Skill 进化器(饕餮)— 通过"吞噬"并分析其他 skill 的优势来强化目标 skill。 当用户想要:整合两个 skill、用一个 skill 优化另一个、对比分析两个 skill 的优劣、 把某个 skill 的优点提炼到另一个 skill 中、或者说"把 X 喂给 Y"、"用 X 来优化 Y"、 "整合这两个 skill"、"吃掉这个 skill"、"skill 进化"、"skill 升级"、"合并 skill" 等意图时,必须触发此 skill。即使用户没有明确说"饕餮",只要涉及到两个 skill 之间的 能力迁移、对比分析、或优势提取,都应该使用此 skill。

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

通用 CapCut 草稿生成与 AI 视频检查 skill。用于把本地 AI 视频套用现有 CapCut 草稿模板, 生成可在 CapCut 首页显示并可编辑的新草稿;也用于提取模板样式、验证草稿结构、抽帧检查 AI 痕迹、规划修复窗口和做本地 RIFE 补帧。

日本語の概要は準備中です。原文の説明を表示しています。

binggandata/bggg-skills6052026年8月13日 更新

binggandata のスキルをすべて見る

このスキルの問題を報告する