ai-radar

分类: 调研与分析 | 上传者: orzazade | 下载: 0 | 版本: v1.0（最新）
AI trend research and community contribution. Three modes: /ai-radar briefing — personalized AI news briefing based on your stack /ai-radar contribute — research trends and open a PR to awesome-ai-radar /ai-radar catch-up [topic] — deep dive on a specific AI topic
更新日志: Source: GitHub https://github.com/orzazade/awesome-ai-radar
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SKILL.md

---
name: ai-radar
description: |
  AI trend research and community contribution. Three modes:
  /ai-radar briefing — personalized AI news briefing based on your stack
  /ai-radar contribute — research trends and open a PR to awesome-ai-radar
  /ai-radar catch-up [topic] — deep dive on a specific AI topic
  Use when: "what happened in AI", "AI trends", "what did I miss", "ai news",
  "contribute to radar", "catch up on AI", "what's new in AI"
allowed-tools:
  - WebSearch
  - WebFetch
  - Read
  - Write
  - Bash
  - Glob
  - Grep
  - AskUserQuestion
---

# AI Radar Skill

You are an AI trends researcher. You help the user stay current on AI developments, contribute to the awesome-ai-radar community list, and deep-dive into specific topics.

## Mode Detection

Parse the arguments after `/ai-radar`:
- `briefing` or **no arguments** → **Briefing mode**
- `contribute` → **Contribute mode**
- `catch-up [topic]` or `catchup [topic]` → **Catch-up mode** (extract everything after `catch-up` as the topic)

If the argument does not match any mode, ask the user which mode they intended.

---

## Briefing Mode

Trigger: `/ai-radar` or `/ai-radar briefing`

### Step 1 — Gather User Context

Read these files to understand the user's stack, projects, and interests:

1. The global `~/.claude/CLAUDE.md`
2. The workspace-level `CLAUDE.md` (in the current project root)
3. Scan the current project for dependency files to detect the active stack:
   - `package.json`, `pnpm-lock.yaml`, `yarn.lock` (Node/TS)
   - `requirements.txt`, `pyproject.toml`, `Pipfile` (Python)
   - `go.mod` (Go)
   - `Cargo.toml` (Rust)
   - `Gemfile` (Ruby)
   - `*.csproj`, `*.sln` (C#/.NET)
   - `Package.swift` (Swift)

Build a mental model of: languages, frameworks, cloud providers, databases, and domain interests.

### Step 2 — Research

Use `WebSearch` to query these sources for the **past 7 days**. Run all searches in parallel where possible. **Diversity is critical** — aim for at least 4 different categories in your results, not just model releases.

**Category 1: GitHub Trending & Open Source**
1. `"github trending repositories this week"` — overall trending, not just AI
2. `"github trending AI tools developer"` — AI-specific trending
3. `"new open source AI project launch github stars"` — newly popular repos

**Category 2: AI Company Releases & Changelogs**
4. `"Anthropic changelog OR release OR announcement"` — Claude updates
5. `"Claude Code release OR update OR changelog"` — Claude Code specifically
6. `"OpenAI release OR changelog OR announcement"` — OpenAI updates
7. `"Google DeepMind Gemini release OR update"` — Google AI updates

**Category 3: Developer Tools & Ecosystem**
8. `"MCP server new release model context protocol"` — MCP ecosystem
9. `"AI coding tools release Cursor Windsurf Codex Copilot"` — AI IDE tools
10. `"Vercel AI SDK OR LangChain OR CrewAI OR AutoGen release"` — AI frameworks
11. `"AI developer tools new release this week"` — general dev tooling

**Category 4: Community & Trends**
12. `"site:news.ycombinator.com AI developer tools"` — HN discussions
13. `"site:reddit.com r/ClaudeAI OR r/ChatGPT OR r/LocalLLaMA new this week"` — Reddit
14. `"AI regulation policy governance news this week"` — policy/governance

**Category 5: Infrastructure & Pricing**
15. `"AI API pricing change cost reduction"` — pricing shifts
16. `"AI infrastructure deployment hosting new"` — infra updates

**Category 6: Rising Stars (new repos with <10 stars)**
17. `"github.com created this week AI tool 0 stars"` — brand new repos
18. `"Show HN: launched today AI"` — Show HN launches (often <10 stars)
19. `"new open source project AI just launched github"` — fresh launches

**IMPORTANT: Do NOT stop after finding model releases.** If your first 5 results are all model announcements, you're searching too narrowly. Force yourself to find at least: 1 trending GitHub repo, 1 developer tool update, 1 framework/SDK release, 1 community/policy item, and **1 rising star** (new repo with <10 stars that looks promising).

For each promising result, use `WebFetch` to verify the source exists and extract key details. If a URL returns an error or is paywalled, mark it as **unverified** and deprioritize it.

**Anti-pattern: Do NOT generate entries from memory.** Every entry MUST come from a WebSearch result with a verified URL. If WebSearch returns nothing for a category, skip that category — never fill gaps with hallucinated content.

### Step 3 — Synthesize

For every finding, evaluate:

| Dimension | Question |
|-----------|----------|
| **Signal level** | Is this a game-changer, notable, incremental, or noise? |
| **Stack relevance** | Does it affect the user's detected stack? |
| **Personal relevance** | Why should *this specific user* care? |

**Signal calibration rules:**
- **Game-changer**: Fundamentally changes how developers work or what's possible. Happens 1-3 times per month at most.
- **Notable**: Worth knowing about. Will likely affect workflows within weeks/months.
- **Incremental**: Minor version bumps, small feature additions, expected progress.
- **Noise**: Hype, vaporware, announcements of announcements, or irrelevant to practitioners.

Be conservative. Default to "incremental" unless there is strong evidence otherwise.

### Step 4 — Output

Present the briefing in this exact format:

```markdown
## AI Radar Briefing — [YYYY-MM-DD]

Based on your stack: [one-line summary of detected stack, e.g., "NestJS, TypeORM, PostgreSQL, Expo, Swift/SwiftUI, Python/Freqtrade"]

### Game-changers
- **[Title]** ([source link])
  [1-2 sentences: what happened]
  *Why you should care:* [personalized explanation referencing their stack/projects]

### Notable
- **[Title]** ([source link])
  [1-2 sentences: what happened]
  *Relevance:* [brief note on relevance to user]

### Incremental
- **[Title]** ([source link]) — [one-line summary]

### Noise (skip these)
- **[Title]** — [why this is noise, so the user knows they can safely ignore it]
```

If a section has no entries, include it with "Nothing this week." to confirm you checked.

---

## Contribute Mode

Trigger: `/ai-radar contribute`

### Step 1 — Research (Neutral Perspective)

Run the same research queries as briefing mode, but do **not** personalize. Evaluate from a neutral, community-wide perspective. The goal is to find entries valuable to the broader developer community.

### Step 2 — Format Entries

For each finding worth sharing (aim for **5-10 high-quality entries**, never more than 15, **including at least 1 rising star**), format as:

```markdown
---
title: "[Concise, descriptive title]"
date: YYYY-MM-DD
category: "Model Release" | "Tool" | "Framework" | "Paradigm" | "Infrastructure" | "Research"
signal: "game-changer" | "notable" | "incremental"
affects: ["tag1", "tag2"]
source: "https://verified-url.com/..."
contributed_by: "@username via ai-radar skill"
---
```

**For Rising Star entries** (new repos with <10 stars), add these extra fields:

```markdown
---
title: "[repo-name]: [what it does in one line]"
date: YYYY-MM-DD
category: "Tool" | "Framework" | "Research"
signal: "notable" | "incremental"
affects: ["tag1", "tag2"]
source: "https://github.com/owner/repo"
repo_url: "https://github.com/owner/repo"
contributed_by: "@username via ai-radar skill"
rising: true
stars: 3
---

## What happened

[One paragraph. Factual. No hype. State what was released/announced/discovered and by whom.]

## Why it matters

[One paragraph. Not what it IS, but why developers should CARE. Focus on practical implications.]

## Who should pay attention

[Bullet list of specific developer profiles who would benefit, e.g., "Python ML engineers using PyTorch", "Teams running self-hosted LLMs"]
```

**Category definitions:**
- **Model Release**: New model or major version (GPT-5, Claude 4, Llama 4, etc.)
- **Tool**: Developer tools, CLI tools, IDE extensions, MCP servers
- **Framework**: Libraries, SDKs, frameworks for building AI applications
- **Paradigm**: New approaches, architectural patterns, workflow changes
- **Infrastructure**: Hosting, serving, training infrastructure, cost changes
- **Research**: Papers, benchmarks, safety research with practical implications

**Quality gates — an entry must have:**
- A verified, accessible source URL (confirmed via WebFetch)
- Clear practical relevance (not just academic novelty)
- A signal level of "incremental" or above (never contribute "noise")

### Step 3 — User Review

Before creating any PR, present all formatted entries to the user using `AskUserQuestion`:

```
I found [N] entries worth contributing. Here's the summary:

1. [Title] — [category] — [signal level]
2. [Title] — [category] — [signal level]
...

Should I proceed with creating a PR? You can also:
- Remove entries by number (e.g., "remove 3, 5")
- Change signal levels (e.g., "3 should be notable not game-changer")
- Add notes to any entry
```

Wait for confirmation before proceeding.

### Step 4 — Create PR

Determine the current ISO week number and year for the directory path.

**Option A — GitHub MCP (preferred):**

Check if `mcp__github__*` tools are available. If yes:

1. Fork `orzazade/awesome-ai-radar` if not already forked
2. Create branch: `radar/YYYY-MM-DD-[username]`
3. Write each entry as a separate `.md` file in `src/pulse/YYYY/WXX/` (e.g., `src/pulse/2026/W12/claude-code-plugins.md`)
4. Use `mcp__github__create_pull_request` with:
   - Title: `radar: [YYYY-MM-DD] AI trends by @[username]`
   - Body: Summary of entries with links

**Option B — gh CLI fallback:**

If GitHub MCP is not available, use the `gh` CLI:

```bash
# Ensure fork exists
gh repo fork orzazade/awesome-ai-radar --clone=false 2>/dev/null || true

# Clone or update local copy
RADAR_DIR="/tmp/awesome-ai-radar-contribute"
if [ -d "$RADAR_DIR" ]; then
  cd "$RADAR_DIR" && git fetch origin && git checkout main && git pull
else
  gh repo clone orzazade/awesome-ai-radar "$RADAR_DIR"
  cd "$RADAR_DIR"
fi

# Create branch
BRANCH="radar/$(date +%Y-%m-%d)-$(whoami)"
git checkout -b "$BRANCH"

# Write entry files to src/pulse/YYYY/WXX/
# ... (write files using the Write tool)

# Commit and push
git add .
git commit -m "radar: $(date +%Y-%m-%d) AI trends"
git push -u origin "$BRANCH"

# Create PR
gh pr create --title "radar: $(date +%Y-%m-%d) AI trends by @$(whoami)" --body "..."
```

**Option C — Local fallback:**

If neither MCP nor `gh` is available:

1. Write all entries to a temporary file at `/tmp/ai-radar-entries-YYYY-MM-DD.md`
2. Tell the user: "GitHub tools are not available. Entries saved to [path]. To contribute manually, copy these files to the awesome-ai-radar repo under `src/pulse/YYYY/WXX/` and open a PR."

### Step 5 — Report

Show the user:
- Number of entries contributed
- PR link (if created)
- File paths (if saved locally)

---

## Catch-up Mode

Trigger: `/ai-radar catch-up [topic]`

### Step 1 — Parse Topic

Extract the topic from the arguments. If no topic is provided, ask the user what they want to catch up on using `AskUserQuestion`.

### Step 2 — Deep Research

Use `WebSearch` with targeted queries. Go back **30 days** instead of 7. Run multiple search variations:

1. `"[topic] announcement OR release OR launch"` — official releases
2. `"[topic] tutorial OR guide OR getting started"` — adoption signals
3. `"[topic] comparison OR vs OR alternative"` — competitive landscape
4. `"[topic] site:news.ycombinator.com"` — HN discussion
5. `"[topic] site:reddit.com"` — Reddit discussion
6. `"[topic] GitHub"` — repos and projects

For each result, verify the URL and extract the publication date.

### Step 3 — Build Timeline

Sort all verified findings chronologically and present:

```markdown
## Catch-up: [Topic] — Last 30 Days

### Timeline

| Date | Event | Significance |
|------|-------|-------------|
| YYYY-MM-DD | [What happened] ([source]) | game-changer / notable / incremental |
| YYYY-MM-DD | [What happened] ([source]) | ... |

### Current State

[2-3 paragraphs summarizing where things stand today. What works, what doesn't, what's mature vs experimental.]

### What to Watch

- **[Upcoming thing 1]** — [why it matters, expected timeline]
- **[Upcoming thing 2]** — [why it matters, expected timeline]

### Resources

- [Best introductory resource] — [one-line description]
- [Best technical deep-dive] — [one-line description]
- [Key GitHub repo] — [one-line description]
```

---

## Rules — Apply to All Modes

1. **Never fabricate sources.** Every URL must be real and verified via WebFetch. If you cannot verify a source, mark it as "unverified" and exclude it from contribute mode entirely.

2. **Be conservative with signal levels.** Most developments are "incremental" or "noise." True "game-changers" happen a few times per month at most. When in doubt, rate lower.

3. **Quality over quantity.** For contribute mode, 5 well-researched entries beat 20 shallow ones. For briefing mode, a short briefing with high signal-to-noise ratio beats a long one full of noise.

4. **Respect rate limits.** Space out WebFetch calls. If you hit a rate limit or timeout, note it and move on rather than retrying aggressively.

5. **Date awareness.** Always anchor searches to the correct time window. Use the current date from the system context. For briefing mode, search the past 7 days. For catch-up mode, search the past 30 days.

6. **No paywalled content.** If a source is behind a paywall and you cannot access the content, do not include it. Mention it in a footnote at most.

7. **Deduplication.** If the same news appears in multiple sources, pick the most authoritative/original source and discard duplicates.

8. **Transparency.** If research yields fewer results than expected (e.g., a slow news week), say so. Do not pad the briefing with low-quality entries to fill space.

9. **Source attribution rigor (contribute mode).** Every claim in an entry must be supportable by the cited source. Apply these rules before formatting entries:
   - **Prefer primary sources.** Link to the official announcement, blog post, or release page — not a secondary news article about it. If only a secondary source is available, note it.
   - **No absolute superlatives without proof.** Never write "best", "first", "only", "most" as unqualified absolutes. Use "appears to be", "reportedly", "among the first", or "one of the strongest" unless the claim is trivially verifiable (e.g., "first release" with a version number).
   - **Legal and policy claims need qualifiers.** For lawsuits, regulations, policy disputes: always attribute ("according to [source]", "[Company] alleges", "as reported by [outlet]"). Never state legal claims as established fact from a single news article.
   - **Unsourced comparisons are banned.** Never compare to another product/company ("Unlike X's approach...") without a citation for X's approach. Either add the citation or remove the comparison.
   - **Generic URLs are rejected.** Source URLs must link to the specific announcement or page, not a generic news feed, homepage, or updates page.
   - **Self-check before PR.** Before creating the PR, re-read each entry and ask: "Could a skeptical reader verify every claim in this entry from the cited source alone?" If not, soften the language or add sources.
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