Ai Capsule

Ranks your daily AI news feed by personal relevance — scores every article and surfaces the most useful ones first.

AI Capsule — Personal AI News Value Evaluator

Scores and ranks your daily AI news feed based on what you actually care about. Tell it your role and familiar areas — it scores every article against your profile and ranks them so the most relevant ones appear first. Nothing is filtered out: every article makes it into the digest, just in the right order. Sources include HuggingFace Papers, OpenAI, Anthropic, DeepMind, Simon Willison, GitHub Trending, HN, Reddit, and more (16 total).

Trigger words: say daily, daily mode, or 每日模式 to run the full digest. Paste a URL or article text to score a single article.

First time: run bash $SKILL_DIR/scripts/setup-env.sh once, then say daily — Claude will walk you through a short setup (role, familiar areas, output language).

Initialization

First time only: run bash $SKILL_DIR/scripts/setup-env.sh to set up the Python venv. Config is created on first run via guided setup.

On every run, resolve the skill directory, Python interpreter, and config path:

SKILL_DIR=$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]:-$0}" 2>/dev/null || echo "$0")")" && pwd)
CONFIG_FILE="${AI_CAPSULE_CONFIG:-$HOME/.ai-capsule/config.yaml}"

# Prefer venv; fall back to system python3 for spawned/headless agents (OpenClaw, CI)
if [ -x "$SKILL_DIR/.venv/bin/python" ]; then
  PYTHON="$SKILL_DIR/.venv/bin/python"
else
  PYTHON="$(command -v python3 || command -v python)"
  echo "WARN: venv not found, using system Python ($PYTHON). Run: bash $SKILL_DIR/scripts/setup-env.sh"
fi

echo "SKILL_DIR: $SKILL_DIR"
echo "PYTHON: $PYTHON"
echo "CONFIG_FILE: $CONFIG_FILE"
cat "$CONFIG_FILE" 2>/dev/null || echo "initialized: false"

If initialized: false (or config file missing) → enter the guided setup flow (Step -1), otherwise jump to Step 0.

---

User Configuration

Config file: ~/.ai-capsule/config.yaml — lives outside the skill directory, survives updates.

Override: set env var AI_CAPSULE_CONFIG=/path/to/config.yaml to use a different file.

Built-in identity profiles:

• engineer: Is it actionable in a real engineering context?
• pm: Does it affect product direction, UX, or business decisions?
• researcher: Does it advance understanding of a technical principle?
• learner: Is this direction worth investing time to learn systematically?
• founder: Does it affect tech stack choices, product direction, or competitive positioning?

Built-in purpose profiles (control scoring weights):

• learn: D×0.35, N×0.25, R×0.2, U×0.1, WPS×0.1
• solve: U×0.45, R×0.3, D×0.1, N×0.1, WPS×0.05
• scout: W×0.25, P×0.2, N×0.2, S×0.15, R×0.15, U×0.05

---

Usage

• Single article: paste title + body directly
• Batch: paste a JSON array [{"id":"1","title":"...","content":"..."},...]
• URL: paste a link starting with http or https
• Daily: say "daily" or "daily mode"
• Reconfigure: say "reconfigure" → Claude resets initialized: false and walks through setup again

---

Execution Steps

Step -1: Guided Setup (first run only)

Runs when initialized: false or config file is missing.

Question 1 — Your role

What role do you primarily take when consuming AI news?

• A) Application engineer / full-stack engineer → engineer
• B) AI researcher / ML engineer → researcher
• C) Product manager / founder → pm or founder
• D) Student / self-learner → learner
• E) Other (describe freely)

Question 2 — Your familiar areas

e.g. LLM application development, RAG, Agent frameworks, model fine-tuning…

Question 3 — Your primary purpose

• A) Follow the technical frontier (learn)
• B) Solve concrete problems at work (solve)
• C) Scan market trends (scout)
• D) Context-dependent (default: learn)

Question 4 — What you don't want to see

e.g. pure marketing fluff, trend analysis with no code…

Question 5 — Output language

What language should card text, value analysis, and scoring reasons be written in?

• A) Chinese / 中文 (default)
• B) English
• C) Other (describe freely, e.g. "日本語")

Proper nouns (model names, framework names, GitHub repo names) always stay in their original form.

Question 6 — Data directory

Where should daily reports, history, and dedup files be stored?

• A) ~/.ai-capsule/data (default — survives skill updates)
• B) Custom path (describe freely)

After collecting answers, write the config:

mkdir -p ~/.ai-capsule
cat > "${AI_CAPSULE_CONFIG:-$HOME/.ai-capsule/config.yaml}" << 'EOF'
initialized: true
identity: [user description]
role_desc: [user description]
familiar_areas:
  - [area 1]
default_identity: [engineer/pm/researcher/learner/founder]
default_purpose: [learn/solve/scout]
output_language: zh  # zh = Chinese / en = English / other language name
data_dir: ~/.ai-capsule/data
EOF

---

Step 0: Mode Detection

• Input starts with http(s):// → URL mode
• Input starts with [ and looks like a JSON array → Batch mode
• Input contains "daily", "daily mode", "每日模式", or "日报" → Daily mode
• Ambiguous → ask the user

Step 1: Content Retrieval

Fetch tool mapping — use whichever tool your agent runtime provides:

Runtime	Tool
Claude Code	WebFetch
Codex / shell agents	Bash: curl <url> + python parse
Tavily agents	tavily_extract
Fallback	Ask user to paste content

URL mode: Fetch and extract main text. For WeChat links, ask user to paste manually.

Batch mode: Parse JSON, extract title and content/text fields, score each article.

Daily mode: Read and execute $SKILL_DIR/sections/daily-mode.md.

Single article mode: Go directly to Step 2.

Step 2: Dedup Check

Dedup file: {data_dir}/dedup-titles.txt (one title per line).

Daily mode: handled centrally in sections/daily-mode.md Step 3 — never judge manually.

Single / URL / Batch mode:

grep -Fx "article title" "$(
  "$PYTHON" -c "
import os, pathlib, yaml
cfg = yaml.safe_load(open(os.environ.get('AI_CAPSULE_CONFIG', str(pathlib.Path.home()/'.ai-capsule/config.yaml'))))
print(cfg.get('data_dir','~/.ai-capsule/data').replace('~', str(pathlib.Path.home())))
")/dedup-titles.txt"

If output is non-empty, skip and say: "This article has already been scored (title: XXX)."

Step 3: Scoring

Read default_identity, default_purpose, and output_language (default: zh) from $CONFIG_FILE.

Scoring framework: see $SKILL_DIR/sections/scoring.md (Read the file before every run).

Runtime mode switches:

• "learn" / "学习" → PURPOSE = learn
• "solve" / "解决问题" → PURPOSE = solve
• "scout" / "找灵感" → PURPOSE = scout
• "pm perspective" / "engineer perspective" / etc. → switch IDENTITY

Always output the score_json block first:

<score_json>
{
  "title": "article title",
  "source": "hf | openai | anthropic | hn | reddit | github | producthunt | blog | unknown",
  "url": "taken directly from pending.json or fetch result — never construct from title",
  "identity": "engineer",
  "purpose": "learn",
  "R": 8, "U": 7, "N": 6, "D": 8,
  "W": 5, "P": 6, "S": 4,
  "total": 7.2,
  "F": 9,
  "final_score": 7.2,
  "action": "READ",
  "summary": "article entity + core value",
  "reasons": {
    "R": "specific relevance reason",
    "U": "specific utility reason",
    "N": "specific novelty reason",
    "D": "specific depth reason",
    "W": "specific wow reason",
    "P": "specific perspective reason",
    "S": "specific cross-domain reason"
  }
}
</score_json>

F: Personal Fit (0–10), assessed against familiar_areas and dislikes in config. final_score = total × (0.7 + 0.3 × F/10) — sorting only, never displayed. action: TRY (runnable code/tool) / READ (deep content) / SCAN (brief/low score).

Step 4: Display Output

Output format: see $SKILL_DIR/sections/output-format.md (Read the file before every run).

Language rule: all card text follows output_language. Always keep in original form: article titles, technical terms, model/framework names, GitHub repo names, URLs, Action tags (TRY/READ/SCAN).

Batch / Daily mode: output detailed cards only, sorted high-to-low by score — no summary table.

Step 5: Append Records

Append to {data_dir}/history.jsonl (with timestamp) and {data_dir}/dedup-titles.txt.

---

Data Source Management

Source configs: $SKILL_DIR/sources/{industry}.yaml

View sources:

"$PYTHON" "$SKILL_DIR/sources_extract.py" --markdown --industry ai

Add a source:

bash $SKILL_DIR/scripts/add-source.sh --industry ai --type rss     --name "Name" --url https://example.com/feed
bash $SKILL_DIR/scripts/add-source.sh --industry ai --type webfetch --name "Name" --url https://example.com --note "hint"
bash $SKILL_DIR/scripts/add-source.sh --industry ai --type tavily  --name "Name" --query "search terms" --domains "reddit.com"

Source schema:

# RSS — fetched automatically by fetch.py
- name: Source Name
  type: rss
  url: https://...
  limit: 5
  handler: huggingface    # optional: huggingface | hacker_news

# URL fetch — agent fetches in daily Step 2
- name: Source Name
  type: webfetch
  url: https://...        # use YYYY/M/D placeholder for date-based URLs
  limit: 5
  note: "hint for the agent"

# Tavily search — agent searches in daily Step 2
- name: Source Name
  type: tavily
  query: "search terms"
  include_domains:
    - reddit.com
  time_range: day         # day | week | month
  limit: 5

New industry: create $SKILL_DIR/sources/{industry}.yaml using ai.yaml as template.

---

Source: github.com/WebPudge/ai-capsule · Feedback / Bug report → Issues