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massgenmassgen 搜索

Agent Skill

massgen 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,187

周安装

93

GitHub Stars

5

下载量

766
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:massgen(massgen 搜索)
来源仓库:https://github.com/massgen/skills
仓库路径:skills/massgen
安装命令:
npx skills add https://github.com/massgen/skills --skill massgen
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/massgen/skills --skill massgen

简介

massgen 用于查找、检索和筛选相关信息,适合在多种宿主环境中快速定位候选结果。

  • 适用于关键词搜索、任务场景匹配或来源线索梳理等研究检索需求。
  • 通过关键词输入和来源仓库配置实现信息定位与筛选功能。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • 建议结合原始 README 核验具体用法,确保符合实际使用边界。

SKILL.md

MassGen Skill

Delegate tasks to your MassGen team.

Before You Launch

Check that a config exists:

ls .massgen/config.yaml 2>/dev/null || ls ~/.config/massgen/config.yaml 2>/dev/null

If no config exists, set one up:

  • Default (browser): run uv run massgen --web-quickstart — user picks models and keys in the browser
  • Headless: read references/config_setup.md — you discover available backends via --list-backends, check the user's API keys, discuss preferences, and generate config with --quickstart --headless

If config exists — launch immediately. No need to ask questions first.

Important: Only Add What's Asked

Do NOT add extra flags unless the user explicitly requests them:

  • No --personas unless the user asks for diverse approaches
  • No --plan-depth deep unless the user wants detailed decomposition
  • No --quick unless the user wants speed over quality

The defaults are good. Let MassGen handle the rest.

Quick Dispatch

1. Detect Mode

User IntentCLI Flags
General task (write, build, research, design)*(default)*
Review/critique existing work--checklist-criteria-preset evaluation
Plan a feature or project--plan
Plan and auto-execute--plan-and-execute
Write requirements/spec--spec
Execute an existing plan--execute-plan <path_or_latest>
Execute against an existing spec--execute-spec <path_or_latest>

2. Write Criteria

Always write opinionated evaluation criteria tailored to the task. Criteria shape what agents produce, not just how they're scored. Save to a temp file and pass via --eval-criteria. Aim for 4-7 criteria.

Required JSON format — each criterion needs text, category, and anti_patterns:

{
  "aspiration": "A site a designer would screenshot for their portfolio",
  "criteria": [
    {
      "text": "Design coherence: Does it feel authored or assembled? ...",
      "category": "primary",
      "anti_patterns": ["unmodified library defaults", "AI-generic aesthetics"]
    },
    {
      "text": "Content depth: Every section teaches something specific ...",
      "category": "standard",
      "anti_patterns": ["Wikipedia-summary prose", "filler sections"]
    }
  ]
}

Categories: primary (ONE — where the model needs most push), standard (must-pass), stretch (nice-to-have). See references/criteria_guide.md for full guidance on writing effective opinionated criteria.

For evaluate/plan/spec modes, you can use --checklist-criteria-preset instead of writing custom criteria (presets: evaluation, planning, spec, persona, decomposition, prompt, analysis).

3. Build Prompt

General: User's task with relevant context.

Evaluate: What to evaluate. Auto-gather git diff, changed files, test output. Keep it factual — what was built, not your quality opinion. Let agents discover issues independently.

Plan: Goal + constraints.

Spec: Problem statement + user needs + constraints.

4. Choose CWD Context

Default to rw when the task produces files. If the deliverable is a file (code, docs, config, README, website, etc.), agents need write access. Use ro only when agents need to *read* the codebase for context but their output is pure text (an answer, review, or analysis) — not files.

ScenarioFlag
Task produces/modifies files in the project (code, docs, configs, etc.)--cwd-context rw
Task needs codebase context but output is text only (review, analysis, Q&A)--cwd-context ro
Isolated task, no codebase needed (default)*(omit flag)*

Rule of thumb: if the user says "write", "create", "build", "rewrite", "update", or "edit" something in the project → rw.

5. Run

Always use the wrapper script:

# Isolated task (default, no cwd-context needed)
bash "$SKILL_DIR/scripts/massgen_run.sh" \
  --mode general \
  --criteria /tmp/massgen_criteria.json \
  "Create an SVG of a butterfly mixed with a panda"

# Task that writes to the project → rw
bash "$SKILL_DIR/scripts/massgen_run.sh" \
  --mode general --cwd-context rw \
  --criteria /tmp/massgen_criteria.json \
  "Rewrite the README with better examples and structure"

The wrapper includes --web --no-browser by default. The run starts immediately — the user can open http://localhost:8000/ anytime to monitor progress. Tell the user about this URL.

Run in the background. MassGen prints these for tracking:

  • LOG_DIR: <path> — full run data
  • STATUS: <path>/status.json — live status
  • ANSWER: <path> — winning agent's answer.txt

Expect 15-45 minutes for multi-round runs.

5b. Review Notification (when --cwd-context rw)

When agents have write access (--cwd-context rw), automatically add --web-review so the user can review git diffs before changes are applied. Review requires --web (the wrapper's default).

Headless (--no-web): If the user explicitly requests headless mode with --cwd-context rw, skip --web-review — changes are applied directly without a review gate. Warn the user that there will be no diff review.

After launching the MassGen run, also launch the review watcher in the background. Parse LOG_DIR from the MassGen output first:

# Launch the watcher (reads LOG_DIR from the MassGen run output)
bash "$SKILL_DIR/scripts/review_watcher.sh" "$LOG_DIR"

The watcher polls status.json and prints structured markers when review is ready:

__REVIEW_PENDING__
REVIEW_URL: http://localhost:8000/?v=2
REVIEW_API: http://localhost:8000/api/sessions/{id}/review-response
FILES_CHANGED: src/foo.py (M), src/bar.py (A)
__END_REVIEW_INFO__

When you see __REVIEW_PENDING__, tell the user:

"MassGen has changes ready for review. You can open the WebUI to review diffs visually, or tell me which files to approve/reject."

Two resolution paths:

  1. Browser: User opens the REVIEW_URL and approves/rejects in the UI.
  2. Agent (text-based): Fetch diffs via GET /api/sessions/{id}/review, show the user a summary, then submit their decision: # Approve all curl -X POST "$REVIEW_API" -H "Content-Type: application/json" \ -d '{"approved": true, "action": "approve"}' # Approve specific files curl -X POST "$REVIEW_API" -H "Content-Type: application/json" \ -d '{"approved": true, "approved_files": ["src/foo.py"]}' # Reject all curl -X POST "$REVIEW_API" -H "Content-Type: application/json" \ -d '{"approved": false, "action": "reject"}'

Either path resolves the review — the other side auto-closes. After resolution, __REVIEW_COMPLETE__ APPROVED=true|false is printed.

6. Read Results

Read the ANSWER: path from the output. The winning agent's workspace is always in the workspace/ directory next to answer.txt.

Workspace paths in answer.txt are best-effort normalized to reference the adjacent workspace/ directory. However, always navigate to the workspace/ next to answer.txt as the ground truth — not paths mentioned in the text.

For plan mode, project_plan.json is in the workspace. For spec mode, project_spec.json is in the workspace.

Optional Flags (only when requested)

FlagPurpose
--quickOne-shot, no voting/refinement
--plan-depth <level>Decomposition depth: dynamic (default), shallow, medium, deep
--plan-thoroughness thoroughDeeper strategic reasoning (default: standard)
--personas <style>Agent diversity: perspective, implementation, methodology, or off
--cwd-context roGive agents read access to codebase
--cwd-context rwGive agents write access to codebase
--web --no-browserEnable WebUI for watching progress (on by default in wrapper)

Config

MassGen auto-discovers config from .massgen/config.yaml or ~/.config/massgen/config.yaml. See setup instructions above.

References

Only consult when the quick dispatch isn't enough:

FileWhen
references/criteria_guide.mdCriteria format, tiers, examples
references/config_setup.mdHeadless config creation
references/advanced_workflows.mdCheckpoint loops, living documents, structured eval, plan-evaluate integration

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37%
按下载量换算283

Claude

28.4%
按下载量换算218

Cursor

17.65%
按下载量换算135

Gemini CLI

9.97%
按下载量换算76

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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