adhd
Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming
供给资产档案
编程与开发 Agent
代码审查、仓库分析、测试、CI、GitHub、DevOps 与开发工作流 Skill。
场景
编程 Agent
我需要一个能理解仓库、修改代码并审查 Pull Request 的编程 Agent。
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add darkroomengineering/cc-settings --skill adhd
维护状态
新鲜
距上次推送 2 天
风险
需审查
Dependency or permission surface needs review
GitHub 质量
42
63/100 质量 · 73/100 信任
覆盖标签
审查说明
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
有潜力有用的候选项,但采用前应与替代方案比较。
信任
仅限沙盒有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
Stars
42 个 GitHub Stars
仓库活跃度
42 个 Star,3 个 Fork
维护状态
距上次推送 2 天
许可证
MIT
安装
npx skills add darkroomengineering/cc-settings --skill adhd
安装安全性
标准软件包或运行时安装路径
权限范围
shell or command execution, network or browser access
Agent 结果
暂未有 Agent 结果数据
文档
README/SKILL.md 上下文充分
风险摘要
生产前审查
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 已声明许可证
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- 编程 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- Inspect source files
适用 Agent
安装决策
- 命令
- npx skills add darkroomengineering/cc-settings --skill adhd
- 策略
- 审查
- 人工审查
- 是
信任与风险
- 信任
- 65/100
- 审计
- 77/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- production agents without a repository review
- Low GitHub adoption signal
- 高风险权限提示:Shell 或命令执行
- Dependency or permission surface needs review
替代 Skill
Frontend Design
170.9K Stars
npx skills add anthropics/skills --skill frontend-design
替代 Skill
Taste Skill: Anti-Slop Frontend
79.0K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
替代 Skill
Canvas Design
170.9K Stars
npx skills add anthropics/skills --skill canvas-design
替代 Skill
Anthropic Brand Guidelines
170.9K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent 安全 v2
45/100 · 避免自动安装
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
高
Shell 或命令执行
Skill 元数据引用了终端、CLI、Shell、子进程或命令执行工作流。
中
Browser automation
Skill may drive a browser or interact with web pages.
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
数据库访问
Skill 可能检查 Schema、查询数据库或处理持久化存储。
- 高风险权限提示:Shell 或命令执行
- Dependency or permission surface needs review
安装目标
在你的 Agent 工作流中安装此 Skill
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install darkroomengineering-adhdAgent 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/darkroomengineering-adhd/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
Task: Use adhd in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install
Install command: npx skills add darkroomengineering/cc-settings --skill adhd
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/darkroomengineering-adhd/install
LLM 文本格式
/api/skills/darkroomengineering-adhd/install?format=text
寻找替代方案
/api/skills/search?q=adhd&limit=3
Agent 提示词
Use adhd for this task. Review https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install, then install with: npx skills add darkroomengineering/cc-settings --skill adhdRegistry 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Agent 决策面板
Fallback candidate for Coding agents
先用此 Skill 做原型验证,并保留备选方案。
栈中角色
备选候选
主要匹配
编程 Agent
信任标签
先做原型验证
安装路径
命令已就绪
适用场景
- 编程 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 63/100 质量档案
- 3 个 OpenAgentSkill 交互事件
先审查
- Low GitHub adoption signal
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次编程 Agent任务。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信任档案
仅限沙盒
有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
GitHub 采用度
检查42 个 GitHub Stars
Star/Fork 活跃度
检查42 个 Star,3 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过距上次推送 2 天
许可证清晰度
通过MIT
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
- GitHub adoption: 42 GitHub stars
- Stars/forks activity: 42 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, network or browser surface
- Permission surface: shell or command execution, network or browser access
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
质量档案
有潜力 适用于 Agent 工作流的候选
有用的候选项,但采用前应与替代方案比较。
工作流匹配
在这些场景使用此 Skill
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
工作流匹配
加入完整工作流
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
Frontend Design
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Taste Skill: Anti-Slop Frontend
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Canvas Design
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Anthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
概览
--- name: adhd argument-hint: "[problem]" description: Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming, API/SDK surface, and fuzzy debugging where the obvious answer is expensive to get wrong. Triggers "/adhd", "adhd mode", "brainstorm", "ideate", "widen the option space", "divergent ideas", "we keep landing on the same idea". Skip for lookups, syntax, bugs with a known root cause, or closed phrasing ("quick", "standard", "canonical", "textbook"). Use /oracle compare to evaluate options you already have — adhd generates the option space; use /plan-ceo-review to challenge whether to build at all. context: main license: MIT ---
# ADHD
Stop picking the textbook answer. The first three answers the model would give are the answers a senior engineer would give in thirty seconds. Correct. Forgettable. The interesting answers live past number three, in the awkward middle nobody walks into. This skill makes the model walk there.
## When to use vs siblings
- `/adhd` — **generate** the option space when you don't have candidates yet. - `/oracle` (compare mode) — **evaluate** options you already have. - `/plan-ceo-review` — challenge whether the thing should be built at all. - `/verify` — adversarially check a conclusion you've already reached.
They compose: `/adhd` to widen, `/oracle` to weigh the shortlist.
## Pre-flight (run before Phase 1)
This skill is expensive. About 10 Agent calls, 30 to 90 seconds wall clock, 5 to 10x a single answer. Do not pay that cost when a direct answer is better. Run this gate before Phase 1.
**Step 1. Explicit invocation check.**
If the user typed `/adhd` or explicitly asked for ADHD mode, "use the adhd skill", or "run ADHD on this", **SKIP the rest of this section and go straight to Phase 1**. The user opted in. Do not second-guess.
**Step 2. Self-judge (only if Step 1 did not match).**
Ask yourself three questions. If the answer to any is no, ABORT.
1. **Open-ended?** Would a senior engineer give multiple viable answers here, or is there one canonical answer? If canonical, abort. 2. **High-stakes?** Is the cost of the obvious answer being wrong actually high? Architecture decisions, public API surfaces, naming a real product, fuzzy bugs with no known root cause, schema design = yes. Side project at 11pm = no. 3. **Open phrasing?** Did the user avoid words like "quick", "standard", "canonical", "textbook", "just", "one-line"? If they used any of those, they want the direct answer. Abort.
If all three checks pass, proceed to Phase 1.
If any fails, ABORT and answer the question directly. Optionally append one sentence: *"If you want a wider exploration under parallel cognitive frames with explicit trap detection, run `/adhd <your problem>`."*
## The loop
Two strict phases. Mixing them kills idea quality, because the critic strangles the generator.
### Phase 1 — Diverge (no critic)
For the problem P:
1. Pick 5 cognitive frames from the table below. Bias toward engineering tags when the problem is code-shaped. Always include at least one wild frame to keep range.
2. Spawn 5 **parallel** Agent tool calls in ONE message. One per frame. Each Agent gets only: - the problem P - any context the user provided - the chosen frame's vantage prompt - a system instruction that forbids evaluation
The exact instruction to give each Agent:
> You are in DIVERGENT mode. You are a generator, not a critic. > Generate 6 short distinct ideas under this frame. Each idea is one > phrase or one sentence. Do not evaluate. Do not rank. Do not hedge. > The first three obvious answers everyone would give are banned. > Push past them into the awkward middle. > Output a JSON array only. No prose before or after. > `[{"text": "...", "rationale": "..."}, ...]`
3. **Critical invariant.** The Agent calls must be parallel and isolated. Do NOT serialize them. Do NOT pass one branch's output as context to another. Branches that see each other anchor each other and the whole method collapses to a wider single thought.
### Phase 2 — Focus (critic on)
After all branches return:
1. **Score.** Rate each idea on three axes 0 to 10: novelty (distance from the obvious default), viability (could it actually ship), fit (does it address the stated problem). For any idea that looks attractive but is a trap (hidden cost, false economy, will not scale, premature abstraction), flag it with a one-line reason.
2. **Cluster.** Group ideas into 3 to 6 clusters by their underlying angle, not by surface keywords. Label clusters by angle: "remove the server plays", "cache-shaped plays", "batched-window plays", "race-multiple- backends plays".
3. **Deepen the top 3.** Rank by weighted score (novelty 0.35 + viability 0.40 + fit 0.25), exclude traps, take top 3. For each, spawn one Agent call that produces: - a 4 to 8 sentence sketch of how the idea works - the load-bearing risk - the first concrete step a builder would take - 3 to 5 child ideas (variations, hybrids, unlocks)
Deepen Agent instruction:
> You are in FOCUS mode. Take one promising idea and connect dots. > Sketch how it would actually work in 4 to 8 sentences. Name the > load-bearing risk. Name the first concrete step a coder would take. > Then generate 3 to 5 sub-ideas that branch off (variations, > combinations with other domains, things this unlocks). > Output JSON only.
Scoring, clustering, and the final synthesis stay in the main session — that is judgment work and belongs on the session's top-tier model. The generator and deepen agents are fan-out subagents and inherit `CLAUDE_CODE_SUBAGENT_MODEL` (Sonnet), which fits the quota doctrine: roomy pools carry volume, the scarce pool does the judging.
## Frames
Pick 5 per run.
| Frame | Vantage prompt | Tags | |---|---|---| | **hardware engineer** | You think in latency, memory layout, and physical constraints. Re-ask this as a hardware/firmware problem. What does the bus topology, cache, timing budget tell you? | code, wild | | **regulator** | You audit systems for compliance and failure modes. What must be provable, traceable, or refusable here? | design, general | | **10-year-old** | You are a curious 10 year old who has never seen software. Describe naive but unencumbered approaches. Ignore convention. | general, wild | | **competitor trying to break it** | You are a hostile competitor or attacker. Generate approaches that exploit, fail, or sabotage the obvious solution. Then invert into ideas. | code, design | | **biology** | Transplant a mechanism from biology (immune systems, neural plasticity, cell signaling, evolution, gut flora). Force-fit it onto this engineering problem. | code, wild | | **logistics** | Steal mechanisms from logistics: queues, batching, just-in-time, hub-and-spoke, returns, last-mile. Apply them literally. | code, design | | **game design** | Approach this as a game designer. What are the loops, rewards, friction, save-states, speedrun tricks? Treat the user as a player. | design, general | | **markets** | Treat the problem as a market. Buyers, sellers, market-makers. What does an auction, a futures contract, a clearing house look like here? | design, wild | | **inversion** | Ask the OPPOSITE question. If goal is X, brainstorm how to guarantee NOT X. Then negate each answer back. | code, design, general | | **extreme: $0 budget, 1 hour** | No money, no team, one hour. What is the crudest version that still does the load-bearing thing? | code, general | | **extreme: infinite budget, 10 years** | Infinite compute, infinite engineers, a decade. What is the maximalist version? | design, wild | | **remove the load-bearing assumption** | Name the thing everyone treats as fixed (framework, database, request-response model, network). Imagine it is gone. What is possible? | code, design, wild | | **speedrunner** | You are a speedrunner. Find glitches, skips, out-of-bounds tricks, frame-perfect shortcuts. What is the abusive-but-legal path? | code, wild | | **ant colony** | No central planner. Many dumb agents, local rules, pheromone trails. How does the problem solve itself emergently? | code, wild | | **3am on-call** | You are the on-call engineer woken at 3am when this breaks. What design would let you not get paged? | code, design |
### Picking frames
For code-shaped problems: pick 4 frames tagged `code` or `design`, plus 1 tagged `wild`. For open product or strategy problems: a mix from all tags. Vary the picks across sessions so the same problem produces different candidate sets when re-run.
## Output shape
After Phase 2, render in this order. Do not collapse it into a wall of prose. The structure is the point.
1. **Brief.** One or two lines confirming the problem and any reframe used. 2. **Wide set.** Full pool grouped by cluster. Each cluster labeled by underlying angle. Each idea is one short phrase. Show score chips like `[N7 V8 F9]` next to each. 3. **Converge.** A 2 to 4 idea shortlist. State why each is on the list. Mark the non-obvious-but-viable pick explicitly with ★. List traps separately, each with the one-line reason it is a trap. 4. **Focus.** The 3 deepened branches. For each: the sketch, the load- bearing risk, the first concrete step, and the child ideas. 5. **Provocation.** One wildcard question or idea that opens a new direction the user can push into if nothing landed.
## Anti-patterns
These are how this skill goes wrong. Watch for them.
- **Convergence disguised as divergence.** Ten minor variations of one idea is not breadth. If every candidate shares the same underlying assumption, you have not diverged. You have decorated. - **Weird-for-weird's-sake with no convergence.** A pile of 30 unsorted absurdities is as useless as one safe answer. Always converge. - **Walls of equally-weighted prose.** Cluster, label, pull out the best. Structure is half the value. - **Refusing to commit.** After diverging, take a position on what is actually promising. "Here are 20 ideas, you decide" is a cop-out. Generate wide, but converge with a real opinion. - **Skipping the isolation invariant.** If you simulate parallel branches by writing them sequentially in one context, you have not done ADHD. You have done a wider single thought. The Agent tool gives each branch a fresh context. Use it.
## Calibration
- **How many ideas?** Scale to stakes. Quick "name this function" = 3 frames × 4 ideas. "How should I position this product" = 5 frames × 8 ideas. Default is 5 × 6 = 30. - **How weird?** Read the room. Serious strategy work: flag the wild cards clearly so they do not read as unserious. Open brainstorming or play: let it run loose. Absurd ideas earn their place by seeding viable ones. - **When to stop diverging?** Stop when new candidates start repeating the shape of existing ones. The space is mapped. Do not pad to hit a number.
## Cost
5 diverge + 1 score + 1 cluster + 3 deepen ≈ 10 Agent calls per run. About 5 to 10x a single-shot answer. Not for every keystroke. For decision points where the cost of the obvious answer is high. Diverge/deepen agents run on the Sonnet subagent pool, so the Opus/Fable cost of a run is one synthesis pass.
## Attribution
Ported from [`UditAkhourii/adhd`](https://github.com/UditAkhourii/adhd) (MIT). Upstream ships the same loop as an npm CLI (`adhd-agent`) plus evals and a source spec on divergent ideation; this port keeps the skill-only form (no install) and adds the cc-settings sibling-skill routing and subagent model notes.
技术详情
- 版本
- 1.0.0
- 许可证
- MIT
- 最近更新
- 2026年8月20日
- 发布时间
- 2026年8月20日
决策摘要
备选候选
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 adhd 准备的场景化草稿,可手动发布到 X。
adhd: Parallel divergent ideation — spawns N isolated generator agents under different cognitive fr... 42 stars https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x
可选:带安装命令的回复
Listing + install path for adhd: https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x Install: npx skills add darkroomengineering/cc-settings --skill adhd
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 darkroomengineering,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd/audit)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)作者
darkroomengineering
@darkroomengineering
平台适配
健康信号
- GitHub Stars
- 42
- 质量评分
- 35/100
- 最近 GitHub 推送
- 2026年8月20日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 3
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
仅限沙盒
- GitHub 采用度42 个 GitHub Stars检查
- Star/Fork 活跃度42 个 Star,3 个 Fork; 当前元数据中没有议题活跃度信息检查
- 近期维护距上次推送 2 天通过
- 许可证清晰度MIT通过
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