last_7_days_news

# Last 7 Days News

This skill organizes the latest 7 days of AI news and X discussion signals.

The X workflow is intentionally opinionated:

- use a validated X cookie stored only at `/tmp/last_7_days_news_x_cookie.txt`
- sample high-signal profile timelines first
- use Home feed only as a supplement
- use the search page only as a final fallback
- collect page content through `agent-browser`
- filter by time window, topic relevance, and engagement in the conversation
- optionally generate an HTML report and a one-page summary

Do not treat `collect_x_feed.py` or Home feed as the default primary entry point.

## When To Use

Use this skill when the user wants:

- AI, technology, or industry news from the last 7 days
- recent or high-signal X discussions from the last 7 days
- a merged brief across public news and social discussion
- a trend summary organized as `Hot Topics / Viewpoints / Opportunities`
- coverage of OpenAI, Anthropic, Claude, Gemini, MCP, agents, coding tools, or adjacent AI themes

## Default Workflow

### 1. Lock the topic, time window, and source scope

- The default time window is the last `7` days.
- If the user does not specify sources, cover all of the following in the same run:
  - official blogs, news sites, and documentation
  - developer communities such as Hacker News, GitHub, and Reddit
  - X discussion signals
- Treat these as parallel evidence tracks rather than a single linear path.
- If the user does not specify an output structure, use:
  - `Hot Topics`
  - `Viewpoints`
  - `Opportunities`
- If the user does not specify an output language, follow the current conversation language.

### 2. Expand public-source exploration

In one evidence track, collect stable sources that do not require extra accounts:

- Hacker News
- GitHub
- RSS feeds and official blogs
- web search and official documentation

Prioritize high-signal information:

- official announcements
- product updates with clear dates
- highly discussed community posts
- implementation notes and developer writeups

### 3. Expand X exploration in parallel

In a second evidence track, run the X portion in this order:

1. reuse and validate the `/tmp` cookie
2. sample high-signal account timelines
3. add Home feed only when timeline samples are insufficient
4. try a narrow search query only when both timeline and Home feed are insufficient
5. normalize, deduplicate, and cross-check the X findings against the public-source track

Do not start from the search page, and do not rely on Home feed alone for broad AI conclusions.

### 4. Merge, cross-check, and rank the evidence

After the public-source track and the X track have both explored the topic:

- merge the normalized results into one candidate set
- deduplicate overlapping items
- cross-check social claims against stable public links whenever possible
- rank the merged set by:
  - original source quality
  - freshness
  - engagement or discussion intensity
  - relevance to the user's ask

The default mental model is:

- one track explores public sources
- one track explores X signals
- the result layer merges both before summarization

### 5. X cookie rules

Only use this cookie path:

- `/tmp/last_7_days_news_x_cookie.txt`

The minimum required cookie fields are usually:

- `auth_token`
- `ct0`

If a cookie file exists, validate it first with `scripts/validate_x_cookies.py`.
Do not trust file existence alone.

Preferred login refresh flow:

1. run `scripts/ensure_x_cookies.sh`
2. if login is needed, complete login in the opened browser
3. export cookies with `scripts/export_x_cookies.py`
4. validate the refreshed cookie again with `scripts/validate_x_cookies.py`

`export_x_cookies.py` writes only to `/tmp/last_7_days_news_x_cookie.txt`.

### 6. Keyword handling

Keywords still matter, but they are filters rather than the default collection entry point.

Preferred keyword input:

- ask the user for a keyword file when one already exists
- read one keyword per line
- ignore empty lines and `#` comment lines
- normalize to lowercase for matching

If the user does not have a file, build a temporary in-memory list inside the current run.

Example keywords:

```text
chatgpt
claude
gemini
openai
anthropic
mcp
agent
ai coding
```

Extra rules:

- avoid using the bare keyword `ai` as a hard Home feed filter because the false-positive rate is high
- prefer brand names and compound phrases such as `openai`, `anthropic`, `claude`, `gemini`, `mcp`, `ai agent`, and `coding agent`
- when sampling a high-signal account timeline, let the account signal and time window dominate keyword filtering

### 7. Browser collection mode

Prefer direct `agent-browser` actions over ad hoc scraping scripts.

Default browser flow:

1. open `https://x.com`
2. inject cookies from `/tmp/last_7_days_news_x_cookie.txt`
3. refresh the page
4. open `https://x.com/home` to confirm the authenticated state
5. open high-signal profile timelines one by one
6. use `agent-browser snapshot` to read visible content
7. continue with `scroll down`, `click`, `tab`, and `back`

The high-signal account list lives here:

- `references/x-high-signal-accounts.md`

Rules for account groups:

- start with the default stable group
- use the extended observation group only when the topic clearly needs it or when the stable group is not enough
- the extended observation priority is `model company -> AI coding -> Chinese community`
- ask the user for a short confirmation before expanding beyond the stable group

Recommended sampling order:

1. model companies and official accounts
2. core people and researchers
3. AI coding and agent-tool ecosystem

For tasks about recent AI trends or opportunity areas, prioritize account timeline sampling over Home feed.

At minimum, the collection pass should:

- read visible tweet and article text
- capture source link, text, author, time, and interaction signals
- filter by account signal and time window first, then use keywords as a secondary filter
- keep only co