prismis 是 AI Skill Hub 本期精选AI工具之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。
A local-first content intelligence daemon that fetches RSS/Reddit/YouTube conten,提供内容智能分析服务
prismis 是一款基于 Python 开发的开源工具,专注于 ai、python 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
A local-first content intelligence daemon that fetches RSS/Reddit/YouTube conten,提供内容智能分析服务
prismis 是一款基于 Python 开发的开源工具,专注于 ai、python 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:pip 安装(推荐)
pip install prismis
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install prismis
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/nickpending/prismis
cd prismis
pip install -e .
# 验证安装
python -c "import prismis; print('安装成功')"
# 命令行使用
prismis --help
# 基本用法
prismis input_file -o output_file
# Python 代码中调用
import prismis
# 示例
result = prismis.process("input")
print(result)
# prismis 配置文件示例(config.yml) app: name: "prismis" debug: false log_level: "INFO" # 运行时指定配置文件 prismis --config config.yml # 或通过环境变量配置 export PRISMIS_API_KEY="your-key" export PRISMIS_OUTPUT_DIR="./output"
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Prismis transforms information overload into intelligence. It continuously monitors your RSS feeds, Reddit communities, and YouTube channels, uses AI to surface what matters to YOU personally, then delivers it through a blazing-fast terminal interface.
Think of it as having a research assistant who reads everything and only interrupts you for the important stuff.
cat > ~/.config/prismis/context.md << 'EOF'
:fabric <TAB> to browse)uv tool install git+https://github.com/nickpending/lspeak.git)make install
prismis-daemon migrate-config
prismis-daemon verify
```bash git clone https://github.com/nickpending/prismis.git cd prismis make install
make build ```
```bash
prismis-cli list --priority high --unread --json | llm "Summarize my reading queue" ```
prismis-daemon verify
[api] host = "0.0.0.0" # Allow access from other devices (default: 127.0.0.1) ```
prismis-cli source add reddit://programming
prismis-daemon migrate-config ```
Note: You can run the daemon however you prefer - in a tmux session, as a background process, or with any process manager you're comfortable with. It's just a regular Python program.
prismis-daemon verify is read-only and safe to run against a production daemon. It checks four things and exits 0 on all-pass / 1 on any failure: config validity, light LLM service reachable, deep LLM service reachable (or "not configured"), at least one active source.
edit ~/.config/prismis/.env
Prismis follows XDG standards: - Config: ~/.config/prismis/ - Data: ~/.local/share/prismis/ - Logs: Wherever you redirect them (stdout/stderr by default)
Security: API keys are stored in ~/.config/prismis/.env with 600 permissions (only you can read). Config references them with api_key = "env:VARIABLE_NAME" pattern for security.
Prismis routes LLM calls through two services so routine work and deep synthesis can use different models. Provider routing is handled by llm-core via ~/.config/llm-core/services.toml; the daemon only references service names.
```toml
[llm] light_service = "prismis-openai" # required — used for priority/summary/context analysis deep_service = "prismis-openai-deep" # optional — second-tier synthesis on HIGH items auto_extract = "high" # "high" | "medium" | "none" — gate for auto deep extraction
The light service handles every routine call (fetch-cycle priority, summarization, context analyzer). The deep service runs the second-tier synthesis prompt that produces the Counterintuitive / Buried lede / So what / Pushback sections plus quotables. When `deep_service` is unset, the daemon runs in light-only mode (graceful degradation — deep extraction failures never block storage).
Service definitions live in `~/.config/llm-core/services.toml`, where you map service names to providers, models, and API keys. Examples:
toml
[services.prismis-openai] provider = "openai" model = "gpt-4o-mini" key = "openai"
[services.prismis-openai-deep] provider = "openai" model = "gpt-5-mini" # reasoning-class model recommended for deep synthesis key = "openai"
[services.prismis-anthropic] provider = "anthropic" model = "claude-3-haiku-20240307" key = "anthropic"
[services.prismis-ollama] provider = "ollama" model = "llama2" base_url = "http://localhost:11434"
API keys are resolved by `apiconf` from `~/.config/apiconf/config.toml`; `key = "openai"` references the entry named `openai` there. Run `prismis-daemon migrate-config` once after upgrading from a pre-iter-12 install — it idempotently rewrites the config to dual-service shape and adds the `[services.prismis-openai-deep]` stub.
**Reddit API** (optional - improves reliability):bash
REDDIT_CLIENT_ID=your-reddit-client-id REDDIT_CLIENT_SECRET=your-reddit-client-secret ```
[audio] provider = "system" # Free macOS/Linux TTS (default)
Requires [lspeak](https://github.com/nickpending/lspeak) for TTS generation:bash uv tool install git+https://github.com/nickpending/lspeak.git
edit ~/.config/prismis/.env # Add your OPENAI_API_KEY
The daemon serves a beautiful web interface on your local network:
```bash
prismis # Launch instantly (local mode)
prismis --remote # Remote mode with incremental sync from server daemon
Essential Keys: - 1/2/3 - View HIGH/MEDIUM/LOW priority content - j/k - Navigate up/down (vim-style) - Enter - Read full article - +/- - Upvote/downvote content (trains AI prioritization) - i - Flag item as interesting (for context analysis) - : - Command mode (see below) - S - Manage sources - ? - Show all keyboard shortcuts - q - Quit
Command Mode (press : to enter): - :fabric <pattern> - Run any of 200+ AI patterns (tab completion available) - :fabric extract_wisdom - Extract key insights - :fabric summarize - Create concise summary - :fabric analyze_claims - Fact-check claims - :fabric explain_terms - Explain technical terms - :context suggest - Get LLM topic suggestions from flagged items (requires flagging with i) - :context edit - Open context.md in $EDITOR - :context review - Show count of flagged items ready for analysis - :audio - Generate audio briefing from HIGH priority items (requires lspeak) - :export sources - Copy all configured sources to clipboard for backup - :mark - Mark article as read/unread - :copy - Copy article content - :prune - Remove unprioritized items (with y/n confirmation) - :prune! - Force remove without confirmation - :prune 7d - Remove items older than 7 days - :help - Show all available commands
Query and export content for automation workflows:
```bash
```
The daemon exposes a REST API for custom integrations and the web interface.
Full API Documentation: See API.md for complete endpoint reference, authentication, request/response formats, and client examples.
Quick Examples:
```bash
Improve your context.md over time by flagging interesting unprioritized items:
1. Flag interesting items - Press i on unprioritized content that should have matched your topics but didn't 2. Get suggestions - Run :context suggest to analyze flagged items with LLM 3. Review analysis - Suggestions copied to clipboard with gap analysis: - Add: New topic area not covered - Expand: Existing topic too narrow - Narrow: Existing topic too broad - Split: One topic covering unrelated things 4. Update context.md - Run :context edit or manually update based on suggestions 5. Repeat - As you flag more items, patterns emerge and your context improves
The LLM studies your existing topic style (length, phrasing, tone) and matches it in suggestions.
Built-in AI analysis using 200+ Fabric patterns. Tab completion helps you discover all available patterns:
```bash
Prismis 是一个 AI 驱动的研究部门,始终保持活跃状态。它提供了一个美观的 Bubbletea 界面,能够在 <100ms 内启动。Prismis 允许您告诉它您关心的内容。
Prismis 的功能包括:即时 TUI(Terminal User Interface)启动,AI 驱动的优先级分析,深度提取(Two-tier LLM synthesis),以及偏好学习(Preference Learning)等。
Prismis 的环境依赖包括 macOS 或 Linux,Go 1.21+,Python 3.13+,LLM API 密钥(OpenAI,Anthropic,Groq 或本地 Ollama),以及 Fabric(可选)和 lspeak(可选)等。
安装 Prismis 可以使用 make install 命令。升级从旧版本安装的 Prismis 可以使用 prismis-daemon migrate-config 命令。
使用 Prismis 可以使用 prismis-cli list 命令来分析您的阅读队列。您可以使用 --priority high --unread --json 参数来指定优先级和未读状态。
Prismis 的配置文件位于 ~/.config/prismis/config.toml。您可以使用 prismis-daemon verify 命令来检查配置和 LLM 服务是否正常工作。
Prismis 的 API 允许您设置 API 密钥。您可以使用 edit ~/.config/prismis/.env 命令来添加 OPENAI_API_KEY。
Prismis 的工作流包括:标记感兴趣的未优先项,获取建议,和审查分析。Fabric 集成提供了 200+ 个 Fabric 模式的内置 AI 分析。
prismis是一个开源的AI工具,提供内容智能分析服务,支持RSS、Reddit和YouTube内容,值得关注
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,prismis 在AI工具赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | prismis |
| 原始描述 | 开源AI工具:A local-first content intelligence daemon that fetches RSS/Reddit/YouTube conten。⭐13 · Python |
| Topics | aipython |
| GitHub | https://github.com/nickpending/prismis |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-19 · 更新时间:2026-05-20 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。