💡 Summary
HelloAGENTS is a structured workflow system that ensures coding tasks are evaluated, implemented, and verified.
🎯 Target Audience
🤖 AI Roast: “It's like a GPS for coding—just don't expect it to drive!”
The README suggests potential risks like executing shell commands and managing filesystem access. To mitigate, ensure strict validation of inputs and limit command execution capabilities.
HelloAGENTS
An intelligent workflow system that keeps going: evaluate → implement → verify.
📑 Table of Contents
- 🎯 Why HelloAGENTS?
- 📊 Data That Speaks
- 🔁 Before & After
- ✨ Features
- 🚀 Quick Start
- 🔧 How It Works
- 📖 Documentation
- ❓ FAQ
- 🛠️ Troubleshooting
- 📈 Version History
- 🔒 Security
- 🙏 Acknowledgments
- 📜 License
🎯 Why HelloAGENTS?
You know the pattern: the assistant gives a good analysis… then stops. Or it edits code but forgets the docs. Or it “finishes” without running anything.
HelloAGENTS is a structured workflow system (routing + stages + acceptance gates) that pushes the work through to a verifiable end.
| Challenge | Without HelloAGENTS | With HelloAGENTS | |---|---|---| | Inconsistent outputs | Depends on prompt quality | Unified output shell + deterministic stages | | Stops too early | “Here’s what you should do…” | Keeps going: implement → test → validate | | No quality gates | Manual review required | Stage / Gate / Flow acceptance | | Context drift | Decisions get lost | State variables + solution packages | | Risky commands | Easy to do damage | EHRB detection + workflow escalation |
💡 Best For
- ✅ Coders who want “done” to mean “verified”
- ✅ Teams that need consistent format and traceable changes
- ✅ Projects where docs are part of the deliverable
⚠️ Not For
- ❌ One-off snippets (a normal prompt is faster)
- ❌ Projects where you can’t keep outputs in Git
- ❌ Tasks that require hard guarantees (still review before production)
📊 Data That Speaks
No made-up “50% faster” claims here—just things you can verify in this repo:
| Item | Value | Where to verify |
|---|---:|---|
| Routing layers | 3 | AGENTS.md / CLAUDE.md (Context → Tools → Intent) |
| Workflow stages | 4 | Evaluate → Analyze → Design → Develop |
| Execution modes | 3 | Tweak / Lite / Standard |
| Commands | 12 | {BUNDLE_DIR}/skills/helloagents/SKILL.md |
| Reference modules | 23 | {BUNDLE_DIR}/skills/helloagents/references/ |
| Automation scripts | 7 | {BUNDLE_DIR}/skills/helloagents/scripts/ |
| Bundles in this repo | 5 | Codex CLI/, Claude Code/, Gemini CLI/, Grok CLI/, Qwen CLI/ |
🔁 Before & After
Sometimes the difference is easier to feel than to explain. Here’s a concrete “before vs after” snapshot:
| | Without HelloAGENTS | With HelloAGENTS | |---|---|---| | Start | You jump into implementation quickly | You start by scoring requirements and filling gaps | | Delivery | You assemble the steps manually | The workflow keeps pushing to “verified done” | | Docs | Often forgotten | Treated as a first-class deliverable | | Safety | Risky ops can slip through | EHRB detection escalates risky actions | | Repeatability | Depends on the prompt | Same stages + gates, every time |
Now let’s make it tangible. Below is a real “before/after” demo snapshot (Snake game generated with/without a structured workflow):
Without HelloAGENTS It works, but you’re still manually driving the process.
With HelloAGENTS More complete delivery, clearer controls, and verification steps baked in.
And here’s what the Evaluate stage looks like in practice: it asks the “boring but necessary” questions (platform, delivery form, controls, acceptance criteria) before writing code.
In plain words, you’ll typically be asked to clarify:
- runtime target (browser / desktop / CLI)
- delivery form (single file / repo / packaged build)
- control scheme
- rules and difficulty preferences
- acceptance criteria (screen size, scoring, audio, obstacles, etc.)
✨ Features
Let’s be practical—here’s what you get.
🧭 3-layer intelligent routing
- Continues the same task across turns
- Detects tool calls (SKILL/MCP/plugins) vs internal workflow
- Chooses tweak / lite / standard execution based on complexity
Benefit: less “prompt babysitting”
📚 4-stage workflow engine
- Evaluate → Analyze → Design → Develop
- Clear entry/exit gates
- Keeps artifacts as solution packages
Benefit: repeatable delivery, not lucky outputs
⚡ 3-layer acceptance
- Stage-level checks
- Inter-stage gates (e.g., validate solution package)
- Flow-level acceptance summary
Benefit: you can trust the result more
🛡️ EHRB safety detection
- Keyword scan + semantic analysis
- Escalates to confirmation when risky
- Flags destructive ops (e.g.,
rm -rf, force push)
Benefit: fewer “oops” moments
🚀 Quick Start
This repo ships multiple ready-to-copy bundles (one per AI CLI):
Codex CLI, Claude Code, Gemini CLI, Grok CLI, Qwen CLI.
1) Clone the repo
git clone https://github.com/hellowind777/helloagents.git cd helloagents
2) Install (placeholder-based)
Because every CLI stores its config in a different place, the README uses placeholders.
First, pick your bundle parameters:
| Your CLI | BUNDLE_DIR | CONFIG_FILE |
|---|---|---|
| Codex CLI | Codex CLI | AGENTS.md |
| Claude Code | Claude Code | CLAUDE.md |
| Gemini CLI | Gemini CLI | GEMINI.md |
| Grok CLI | Grok CLI | GROK.md |
| Qwen CLI | Qwen CLI | QWEN.md |
Then copy both the config file and the skills/helloagents/ folder into your CLI config root.
macOS / Linux (bash)
CLI_CONFIG_ROOT="..." BUNDLE_DIR="Codex CLI" CONFIG_FILE="AGENTS.md" mkdir -p "$CLI_CONFIG_ROOT/skills" cp -f "$BUNDLE_DIR/$CONFIG_FILE" "$CLI_CONFIG_ROOT/$CONFIG_FILE" cp -R "$BUNDLE_DIR/skills/helloagents" "$CLI_CONFIG_ROOT/skills/helloagents"
Windows (PowerShell)
$CLI_CONFIG_ROOT = "..." $BUNDLE_DIR = "Codex CLI" $CONFIG_FILE = "AGENTS.md" New-Item -ItemType Directory -Force "$CLI_CONFIG_ROOT\\skills" | Out-Null Copy-Item -Force "$BUNDLE_DIR\\$CONFIG_FILE" "$CLI_CONFIG_ROOT\\$CONFIG_FILE" Copy-Item -Recurse -Force "$BUNDLE_DIR\\skills\\helloagents" "$CLI_CONFIG_ROOT\\skills\\helloagents"
3) Verify it works
In your CLI, run:
/helloagentsor$helloagents
Expected: a welcome message that starts with something like:
💡【HelloAGENTS】- 技能已激活
4) Start using it
- Try
~helpto see all commands - Or just describe what you want; the router will pick the workflow
🔧 How It Works
flowchart TD Start([User input / 用户输入]) --> L1{Layer 1: Context / 上下文} L1 -->|Continue / 继续| Continue[Continue task / 继续任务] L1 -->|New request / 新请求| L2{Layer 2: Tools / 工具} L2 -->|External tool / 外部工具| Tool[Run tool + shell wrap / 执行工具+Shell包装] L2 -->|No tool / 无工具| L3{Layer 3: Intent / 意图} L3 -->|Q&A / 问答| Answer[Direct answer / 直接回答] L3 -->|Change / 改动| Eval[Evaluate / 需求评估] Eval -->|Score >= 7 / >=7| Complexity{Complexity / 复杂度} Eval -->|Score < 7 / <7| Clarify[Clarify / 追问补充] Complexity -->|Tweak / 微调| Tweak[Tweak mode / 微调模式] Complexity -->|Lite / 轻量| Analyze[Analyze / 项目分析] Complexity -->|Standard / 标准| Analyze Analyze --> Design[Design / 方案设计(方案包)] Design --> Develop[Develop / 开发实施(实现+测试)] Develop --> Done[✅ Done / 完成 + acceptance / 验收摘要] style Eval fill:#e3f2fd style Analyze fill:#fff3e0 style Design fill:#ede9fe style Develop fill:#dcfce7 style Done fill:#16a34a,color:#fff
Key artifacts you’ll see in real projects:
plan/YYYYMMDDHHMM_<feature>/solution packa
Pros
- Structured workflow enhances reliability
- Automated verification reduces manual errors
- Clear documentation as part of the process
Cons
- May not suit one-off coding tasks
- Requires Git for output management
- Still needs human review for production
Related Skills
pytorch
S“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”
agno
S“It promises to be the Kubernetes for agents, but let's see if developers have the patience to learn yet another orchestration layer.”
nuxt-skills
S“It's essentially a well-organized cheat sheet that turns your AI assistant into a Nuxt framework parrot.”
Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author hellowind777.
