bggg-data-x

bggg-data-x is a skill for Codex from binggandata/bggg-skills. It costs 128 tokens per session (1,470 once invoked), scanned A, original, MIT.

A tool for collecting public posts from X, formerly known as Twitter, through the visible search results in an already logged-in Chrome browser. It saves the posts and their metadata as structured JSONL data, where each line is one record.

In plain words
What is it for?
Use it for customer-opinion research, social listening, competitor research, and multilingual keyword discovery on X. It supports query files with language, round, result-limit, and sorting settings.
Why use it?
It creates a traceable copy of research evidence instead of relying on manually copied posts or unstructured notes. It also organizes searches, raw results, and normalized data for later analysis.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it for customer-opinion research, social listening, competitor research, and multilingual keyword discovery on X. It supports query files with language, round, result-limit, and sorting settings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/binggandata/bggg-skills/bggg-data-x
Install

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.

Any agent
npx skills add binggandata/bggg-skills --skill bggg-data-x
Clone the repo
git clone --depth 1 https://github.com/binggandata/bggg-skills

Made for: Codex.

Wrote 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.

agentmods badge for bggg-data-x

README.md
[![agentmods](https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-x/github.svg)](https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-x)
Your own site
<a href="https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-x"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-x/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.

agentmods 80×15 button for bggg-data-x

Your own site · 80×15
<a href="https://agentmods.dev/skills/binggandata/bggg-skills/bggg-data-x"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-data-x.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 128 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,470 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00128 $0.01470
Opus 5 $0.00064 $0.00735
Sonnet 5 $0.00026 $0.00294
Haiku 4.5 $0.00013 $0.00147

Measured 13d ago against content hash 3dd607ba4f16, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bggg-data-x 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/build_query_plan.py, scripts/normalize_x_dom.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

bggg-data-x/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BGGG X Data

Collect public X posts from the visible, rendered search timeline in the user's logged-in Chrome. Preserve one source package per query, then normalize locally.

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/x_queries.tsvwork/x/(request plan、逐查询 source package)→ data/raw/x_multi_<date>.jsonl

Workflow

  1. Prepare a tab-separated query file:
query	lang	round	max_rows	sort
sample-ingredient lang:en	EN	1	250	latest
ボリュフィリン	JP	1	200	latest

Build a deterministic plan:

python3 scripts/build_query_plan.py \
  --queries config/x_queries.tsv \
  --output work/x/request_plan.json
  1. Use the Chrome plugin and follow its control skill. Select the user's Chrome explicitly, read its complete browser documentation, and reuse the browser binding. Never inspect or export cookies, local storage, profiles, passwords, or session stores.

  2. Open the first planned search URL. Confirm from visible page state that X is signed in and the search timeline is available. If sign-in blocks the page, ask the user to sign in in Chrome; do not switch browser or bypass authentication.

  3. For each query, collect only rendered cards from the visible DOM. Follow references/chrome_collection.md for the exact selectors, extraction function, scroll loop, checkpointing, and failure handling.

Read the full file on GitHub · 119 lines

Files

What ships with it

5 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.

Changes

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.

  1. 13d ago First seen · 119 lines · 128 tokens per session scan A 3dd607ba4f16

Subscribe to this mod's changes

bggg-data-x is a skill published in the GitHub repository binggandata/bggg-skills (594 stars, last pushed 1mo ago), licensed MIT. It adds 128 tokens to every session and 1,470 once invoked, about $0.0006 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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