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Agent Skill · 研究检索

opencode-runner(opencode runner 搜索)

opencode-runner 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。

opencode-runner 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 58,786。

总安装 404周安装 17GitHub Stars 68下载 141核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:opencode-runner(opencode runner 搜索)
来源仓库:https://github.com/luongnv89/skills
仓库路径:skills/opencode-runner
安装命令:
npx skills add https://github.com/luongnv89/skills --skill opencode-runner
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/luongnv89/skills --skill opencode-runner

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

opencode-runner 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装使用。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/opencode-runner

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/luongnv89/skills --skill opencode-runner;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

opencode-runner 怎么安装?

在终端执行 npx skills add https://github.com/luongnv89/skills --skill opencode-runner。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

opencode-runner 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

opencode-runner 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/luongnv89/skills --skill opencode-runner 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

opencode-runner 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

opencode-runner 用的人多吗?

公开数据是累计安装 404 次,近一周安装 17 次,下载 141 次,GitHub 68 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

opencode-runner 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
opencode-runner

opencode runner 搜索 · 本页

40468执行命令未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

OpenCode Runner

Delegate coding tasks to opencode using free models — zero cost, fully automated.

OpenCode (opencode.ai) is a terminal AI coding assistant that supports multiple providers and models. This skill automates the process of selecting the best available free model, launching the task, and reporting progress back to you.

Prerequisites

  • opencode installed: which opencode must succeed; install via npm i -g opencode-ai@latest or brew install opencode if missing
  • Internet access: required for cloud model selection and task execution via OpenCode Zen
  • Project context: the current working directory should be the project root for file-context tasks

Critical Rules

  1. Never do the task yourself. This skill exists solely to delegate work to opencode. If opencode is not installed, fails to run, or no free cloud model is available — report the problem to the user and stop. Do not fall back to editing files directly, writing code yourself, or using any other tool to accomplish the user's coding task. The whole point is that opencode does the work.
  2. Only select cloud models. Never select local models (e.g., ollama/*, lmstudio/*, or any model running on localhost). Only select models from the opencode/* provider namespace, which are cloud-hosted on OpenCode Zen. Local models have unpredictable availability, performance, and may not support the tool-use capabilities opencode needs.
  3. Always clean up after yourself. opencode spawns background processes (LSP servers, MCP servers, node workers) that persist after the task finishes. Every execution path — success, failure, error, timeout — must end with the cleanup steps in Phase 5. Orphaned opencode processes silently eat CPU and memory, and users won't notice until their machine slows to a crawl.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting files in an existing repository, sync the current branch with remote:

branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin
git pull --rebase origin "$branch"

If the working tree is not clean, stash first, sync, then restore:

git stash push -u -m "pre-sync"
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"
git stash pop

If origin is missing or conflicts occur, stop and ask the user before continuing.

Phase 1: Verify Installation

Check that opencode is installed and at the latest version.

Step 1: Check if installed

which opencode && opencode --version

If opencode is not found, tell the user:

opencode is not installed. Install it with one of these commands: ``bash curl -fsSL https://opencode.ai/install | bash ` or `bash npm i -g opencode-ai@latest ` or (macOS) `bash brew install opencode ``

Then stop completely — do not proceed to any other phase, do not attempt the task yourself, do not edit any files. Wait for the user to install opencode and re-invoke this skill.

Step 2: Check for updates

opencode upgrade

This will upgrade to the latest version if one is available, or confirm already up to date. If the upgrade fails, inform the user of the error and suggest running the command manually. If the upgrade itself breaks opencode, stop and report — do not continue.

Phase 2: Discover Free Models

Query the available models and identify which free ones are accessible.

opencode models --verbose 2>/dev/null || opencode models

Free model priority list

Select the first available model from this ordered list:

PriorityModel IDName
1opencode/minimax-m2.5-freeMiniMax M2.5 Free
2opencode/kimi-k2.5Kimi K2.5 (via Zen, check if free tier)
3opencode/glm-5GLM 5 (via Zen, check if free tier)
4opencode/mimo-v2-pro-freeMiMo V2 Pro Free
5opencode/mimo-v2-omni-freeMiMo V2 Omni Free
6opencode/big-pickleBig Pickle (last resort)
7opencode/gpt-5-nanoGPT 5 Nano (fallback)
8opencode/nemotron-3-super-freeNemotron 3 Super Free (fallback)

The model IDs above are based on OpenCode Zen's free tier. When checking opencode models output, look for models with "$0" or "Free" pricing. The exact IDs may change — match by name if the ID format differs.

Selection logic:

  1. Parse the output of opencode models to find available models
  2. Filter out all local models — ignore anything from ollama/*, lmstudio/*, or any provider that runs locally. Only consider models from the opencode/* provider namespace (cloud-hosted on OpenCode Zen)
  3. Cross-reference the remaining cloud models with the priority list above
  4. Pick the highest-priority model that appears in the available list
  5. If none of the priority models are found, look for any opencode/* model listed as free ($0 or "Free")
  6. If no free cloud models exist at all, inform the user and stop — do not fall back to local models or paid models, and do not attempt the task yourself

Report the selected model to the user before proceeding:

Selected free model: [model name] ([model-id]) Reason: Highest priority free model available.

Privacy note: Free models on OpenCode Zen may use collected data for model improvement. Mention this to the user when selecting a free model so they can make an informed choice.

Phase 3: Execute the Task

Run the coding task with the selected free model.

opencode run -m "[selected-model-id]" "[user's task prompt]"

Handling multi-line or complex prompts

For tasks that reference files or need detailed context, use the --file flag:

opencode run -m "[selected-model-id]" --file path/to/relevant-file.py "[task description]"

Running in the background

For long-running tasks, run in the background and monitor:

opencode run -m "[selected-model-id]" "[task]" --format json > /tmp/opencode-output.json 2>&1 &
OPENCODE_PID=$!

Phase 4: Monitor and Report

While the task is running, provide periodic updates to the user.

For foreground execution

The output streams directly. Summarize key milestones as they appear:

  • When opencode starts processing
  • When it reads/analyzes files
  • When it begins generating code
  • When it writes output files
  • When it completes or errors

For background execution

Check progress periodically:

# Check if still running
kill -0 $OPENCODE_PID 2>/dev/null && echo "Still running..." || echo "Completed"

# Check partial output
tail -20 /tmp/opencode-output.json

Progress report format

Provide updates in this format:

OpenCode Progress Report - Model: [model name] - Status: [Running / Completed / Error] - Duration: [time elapsed] - Current activity: [what opencode is doing]

On completion

When the task finishes:

  1. Report the final status (success or failure)
  2. Show a summary of what opencode produced (files modified, code generated, etc.)
  3. Report token usage if available via opencode stats
  4. If the task failed, suggest the user try with a different free model from the priority list
  5. Run Phase 5 cleanup — this is mandatory, even on success

On error or timeout

If opencode errors or takes too long (>5 minutes with no output):

  1. Report the error to the user
  2. Suggest retrying with the next free cloud model in the priority list
  3. If all free cloud models have been tried, inform the user that no free option worked and suggest checking their opencode configuration
  4. Never attempt the task yourself as a fallback — the user invoked this skill because they want opencode to do the work, not you
  5. Run Phase 5 cleanup — even on error or timeout, always clean up

Phase 5: Cleanup (mandatory)

Every execution — success, failure, error, or timeout — must end with cleanup. opencode spawns child processes (LSP servers, MCP servers, node workers) that persist after the main process exits. Without cleanup, these orphaned processes accumulate and drain system resources.

Step 1: Kill the opencode process tree

If you launched opencode in the background with a tracked PID:

# Kill the main process and its children
kill $OPENCODE_PID 2>/dev/null
# Wait briefly for graceful shutdown
sleep 2
# Force kill if still running
kill -9 $OPENCODE_PID 2>/dev/null

Step 2: Find and kill orphaned opencode processes

After the task completes, scan for any lingering opencode processes from this session:

# List any remaining opencode processes
ps aux | grep '[o]pencode' | grep -v grep

If orphaned processes are found, kill them:

# Kill all opencode run processes (be specific to avoid killing the user's TUI)
pkill -f "opencode run" 2>/dev/null

Be careful to only kill opencode run processes, not the user's interactive TUI session (opencode without subcommand). If the user has an interactive opencode session open, leave it alone.

Step 3: Clean up temp files

rm -f /tmp/opencode-output.json 2>/dev/null

Step 4: Confirm cleanup

Report to the user:

Cleanup complete — all opencode processes from this task have been terminated.

If you couldn't kill some processes (permission denied, etc.), warn the user:

Warning: Some opencode processes may still be running. Run pkill -f "opencode run" manually to clean up.

Expected Output

A successful run produces the following visible output to the user:

Phase 2 — Model selection report:

Selected free model: MiniMax M2.5 Free (opencode/minimax-m2.5-free)
Reason: Highest priority free model available.

Note: Free models on OpenCode Zen may use collected data for model improvement.

Phase 4 — Progress report (streaming):

OpenCode Progress Report
- Model: MiniMax M2.5 Free
- Status: Completed
- Duration: 1m 42s
- Current activity: Wrote 3 files, 147 lines added

Summary: opencode added a `retry` decorator to `utils/http.py`, updated
`tests/test_http.py` with 4 new test cases, and modified `README.md` to
document the retry behavior. All tests pass per the final output.

Phase 5 — Cleanup confirmation:

Cleanup complete — all opencode processes from this task have been terminated.

If the task fails or times out, the output instead shows which model was tried, the error message from opencode, a recommendation to retry with the next free model in the priority list, and the cleanup confirmation.


Edge Cases

  • opencode not installed — which opencode returns nothing. The skill prints installation instructions for three methods (curl, npm, brew) and stops. It does not attempt the coding task itself.
  • opencode upgrade fails — The upgrade command errors. The skill reports the failure, suggests running the command manually, and stops. It does not continue with a potentially broken binary.
  • No free cloud models available — opencode models output contains no opencode/* models with $0 or "Free" pricing. The skill informs the user that no free option is available and stops. It does not fall back to local models (ollama, lmstudio) or paid models.
  • All priority free models tried and all fail — After retrying with every model in the priority list, none produced usable output. The skill reports this, suggests the user check their opencode auth configuration (opencode auth list), and stops.
  • Task timeout (>5 minutes with no output) — The skill kills the opencode process tree, suggests retrying with the next free model, and runs Phase 5 cleanup. It does not attempt the task itself.
  • User has an interactive opencode TUI session open — The cleanup step detects running opencode processes. The skill kills only opencode run child processes, leaving the interactive TUI (opencode without a subcommand) untouched.
  • Task prompt contains multi-line content or file references — Use the --file flag to pass context files separately, keeping the command-line prompt concise and avoiding shell escaping issues.
  • opencode produces output but exits non-zero — Report the exit code and last lines of output to the user, run cleanup, and suggest verifying the task result manually before relying on it.

Acceptance Criteria

The skill run is considered successful when all of the following are verifiable:

  • Installation verified — Phase 1 confirms opencode is on PATH and reports its version before any other action.
  • Only cloud models considered — The model selection step filters out all ollama/*, lmstudio/*, and any other local-provider models. No local model is ever selected.
  • Selected model reported to user — Before execution begins, the user sees the chosen model name, its ID, and the privacy note about free-tier data collection.
  • Task delegated to opencode — The coding task is executed via opencode run, not by the skill editing files directly or writing code itself.
  • Progress reported — At least one progress update is provided while the task runs, showing model, status, duration, and current activity.
  • Completion summary delivered — After opencode finishes, the user receives a summary of what was produced (files modified, lines changed, etc.).
  • Cleanup runs on every exit path — Phase 5 runs whether the task succeeded, failed, errored, or timed out. No orphaned opencode run processes remain after the skill exits.
  • Temp files removed — /tmp/opencode-output.json (and any other temp files created) are deleted during cleanup.
  • No fallback to self — If opencode is unavailable or all free models fail, the skill stops and reports the problem. It never falls back to editing files or writing code itself.

Step Completion Reports

After completing each major phase, output a status report in this format:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          √ pass (note if relevant)
  [Check 3]:          × fail — [reason]
  [Check 4]:          √ pass
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Adapt the check names to match what the step actually validates. Use √ for pass, × for fail, and — to add brief context. The "Criteria" line summarizes how many acceptance criteria were met. The "Result" line gives the overall verdict.

Installation (phase 1 of 5)

◆ Installation (phase 1 of 5 — opencode readiness)
··································································
  opencode found:         √ pass — /usr/local/bin/opencode
  Version current:        √ pass — already at latest
  [Criteria]:             √ 2/2 met
  ____________________________
  Result:                 PASS

Model Discovery (phase 2 of 5)

◆ Model Discovery (phase 2 of 5 — free model selection)
··································································
  Models queried:         √ pass — 12 models available
  Free model selected:    √ pass — opencode/minimax-m2.5-free
  Tier ranking applied:   √ pass — priority 1 model chosen
  [Criteria]:             √ 3/3 met
  ____________________________
  Result:                 PASS

Execution (phase 3 of 5)

◆ Execution (phase 3 of 5 — task delegation)
··································································
  Task submitted:         √ pass
  Progress monitored:     √ pass — streaming output observed
  Output captured:        × fail — timeout after 5 min with no output
  [Criteria]:             √ 2/3 met
  ____________________________
  Result:                 PARTIAL

Monitor and Report (phase 4 of 5)

◆ Monitor and Report (phase 4 of 5 — progress tracking)
··································································
  Progress observed:        √ pass — streaming output detected
  Status reported:          √ pass — completion summary delivered
  Token usage logged:       × fail — stats unavailable
  [Criteria]:               √ 2/3 met
  ____________________________
  Result:                   PARTIAL

Cleanup (phase 5 of 5)

◆ Cleanup (phase 5 of 5 — process termination)
··································································
  Processes killed:       √ pass — OPENCODE_PID terminated
  Temp files cleaned:     √ pass — /tmp/opencode-output.json removed
  [Criteria]:             √ 2/2 met
  ____________________________
  Result:                 PASS

Quick Reference

CommandPurpose
opencode --versionCheck installed version
opencode upgradeUpdate to latest
opencode modelsList available models
opencode run -m MODEL "prompt"Run task with specific model
opencode statsView usage statistics
opencode auth listCheck authenticated providers
pkill -f "opencode run"Kill orphaned run processes
`ps aux \grep opencode`Find running opencode processes

平台分布

Codex

38.72%
按下载量换算55

Claude

29.32%
按下载量换算41

Cursor

18.7%
按下载量换算26

Gemini CLI

9.2%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/luongnv89/skills --skill opencode-runner 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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