Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Write comprehensive implementation plans assuming the implementer has zero context for the codebase and questionable taste. Document everything they need: which files to touch, complete code, testing commands, docs to check, how to verify. Give them bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.
Assume the implementer is a skilled developer but knows almost nothing about the toolset or problem domain. Assume they don't know good test design very well.
Core principle: A good plan makes implementation obvious. If someone has to guess, the plan is incomplete.
Always use before:
Don't skip when:
Each task = 2-5 minutes of focused work.
Every step is one action:
Too big:
### Task 1: Build authentication system
[50 lines of code across 5 files]
Right size:
### Task 1: Create User model with email field
[10 lines, 1 file]
### Task 2: Add password hash field to User
[8 lines, 1 file]
### Task 3: Create password hashing utility
[15 lines, 1 file]
Every plan MUST start with:
# [Feature Name] Implementation Plan
> **For Hermes:** Use subagent-driven-development skill to implement this plan task-by-task.
**Goal:** [One sentence describing what this builds]
**Architecture:** [2-3 sentences about approach]
**Tech Stack:** [Key technologies/libraries]
---
Each task follows this format:
### Task N: [Descriptive Name]
**Objective:** What this task accomplishes (one sentence)
**Files:**
- Create: `exact/path/to/new_file.py`
- Modify: `exact/path/to/existing.py:45-67` (line numbers if known)
- Test: `tests/path/to/test_file.py`
**Step 1: Write failing test**
```python
def test_specific_behavior():
result = function(input)
assert result == expected
```
**Step 2: Run test to verify failure**
Run: `pytest tests/path/test.py::test_specific_behavior -v`
Expected: FAIL — "function not defined"
**Step 3: Write minimal implementation**
```python
def function(input):
return expected
```
**Step 4: Run test to verify pass**
Run: `pytest tests/path/test.py::test_specific_behavior -v`
Expected: PASS
**Step 5: Commit**
```bash
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature"
```
Read and understand:
Use Hermes tools to understand the project:
# Understand project structure
search_files("*.py", target="files", path="src/")
# Look at similar features
search_files("similar_pattern", path="src/", file_glob="*.py")
# Check existing tests
search_files("*.py", target="files", path="tests/")
# Read key files
read_file("src/app.py")
Decide:
Create tasks in order:
For each task, include:
src/config/settings.py)Check:
mkdir -p docs/plans
# Save plan to docs/plans/YYYY-MM-DD-feature-name.md
git add docs/plans/
git commit -m "docs: add implementation plan for [feature]"
Bad: Copy-paste validation in 3 places Good: Extract validation function, use everywhere
Bad: Add "flexibility" for future requirements Good: Implement only what's needed now
# Bad — YAGNI violation
class User:
def __init__(self, name, email):
self.name = name
self.email = email
self.preferences = {} # Not needed yet!
self.metadata = {} # Not needed yet!
# Good — YAGNI
class User:
def __init__(self, name, email):
self.name = name
self.email = email
Every task that produces code should include the full TDD cycle:
See test-driven-development skill for details.
Commit after every task:
git add [files]
git commit -m "type: description"
Bad: "Add authentication" Good: "Create User model with email and password_hash fields"
Bad: "Step 1: Add validation function" Good: "Step 1: Add validation function" followed by the complete function code
Bad: "Step 3: Test it works"
Good: "Step 3: Run pytest tests/test_auth.py -v, expected: 3 passed"
Bad: "Create the model file"
Good: "Create: src/models/user.py"
After saving the plan, offer the execution approach:
"Plan complete and saved. Ready to execute using subagent-driven-development — I'll dispatch a fresh subagent per task with two-stage review (spec compliance then code quality). Shall I proceed?"
When executing, use the subagent-driven-development skill:
delegate_task per task with full contextBite-sized tasks (2-5 min each)
Exact file paths
Complete code (copy-pasteable)
Exact commands with expected output
Verification steps
DRY, YAGNI, TDD
Frequent commits
A good plan makes implementation obvious.
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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