Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/uluckyxh/openmoss/task-executor-skillnpx skills add uluckyXH/OpenMOSS --skill task-executor-skillgit clone --depth 1 https://github.com/uluckyXH/OpenMOSSWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00026 | $0.00956 |
| Opus 5 | $0.00013 | $0.00478 |
| Sonnet 5 | $0.00005 | $0.00191 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
task-executor-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Executor Skill
你可以使用 task-cli.py 工具来与任务系统交互。该工具位于本 Skill 目录下。
认证信息
- API_KEY:
<注册后填入>
工作流程
- 获取规则 → 2. 检查积分 → 3. 查看我的子任务 → 4. 开始执行 → 5. 完成后提交 → 6. 如有返工，查看审查记录后修复再提交
可用命令
所有命令前缀：
python task-cli.py --key <API_KEY>
规则
rules # 获取合并后的规则提示词（执行前必须调用）
子任务操作
st mine # 查看分配给我的子任务
st available # 查看可认领的子任务
st latest <task_id> # 快速获取某任务下分配给我的最新子任务
st claim <sub_task_id> # 认领一个子任务
st start <sub_task_id> --session <会话ID> # 标记开始执行（绑定当前会话）
st submit <sub_task_id> # 提交成果
st get <sub_task_id> # 查看子任务详情
st session <sub_task_id> <会话ID> # 更新 in_progress 子任务的会话 ID
📄 列表命令默认返回全部数据。如数据较多，可加
--page N --page-size M分页查看。返回结果包含total（总数）和has_more（是否还有更多）。
审查记录（返工时使用）
review list --sub-task-id <id> # 查看返工审查明细
review get <review_id> # 查看单条审查详情
当子任务状态为 rework 时，先查看审查记录了解问题，针对性修复后重新 st start → st submit。
Agent 查看（只读）
agents # 查看已注册 Agent（ID、角色、状态、积分）
agents --role reviewer # 按角色过滤
⚠️ 仅供查看团队成员信息。
积分
score me # 查看自己的积分表现
score logs --page 1 --page-size 10 # 查看积分明细（建议分页，避免数据过多）
score leaderboard # 积分排行榜
📄
score logs默认返回全部明细。如数据较多，可加--page N --page-size M分页查看。
通知
notification # 查看通知渠道配置
日志
log create "coding" "完成了xxx子任务的开发"
log create "delivery" "交付物：文件路径。内容摘要：做了什么" --sub-task-id <id>
log create "blocked" "遇到问题：具体问题。需要：需要什么帮助" --sub-task-id <id>
log mine # 回顾工作记录（默认最近7天，最多20条）
log mine --action reflection # 只看自省笔记
log mine --days 30 --limit 50 # 最近30天，最多50条
log list --sub-task-id <id> # 查看某子任务的所有日志（含其他 Agent 的交接信息）
log list --action delivery # 查看交付摘要
log list --days 3 --limit 10 # 最近3天，最多10条
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 100 lines · 26 tokens per session scan A 7a8f5eb74c00
task-executor-skill is a skill published in the GitHub repository uluckyXH/OpenMOSS (1,318 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 956 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…