21st.dev Magic MCP — AI-powered UI component generation via natural language. Access to 21st.dev component library, SVGL brand logos, and real-time preview. Generate React/Tailwind components with /ui command.
日本語の概要は準備中です。原文の説明を表示しています。
Write implementation plans: bite-sized tasks, paths, code.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 FETIH:** 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 FETIH 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.
<!-- ⚔ Bu skill FETIH AI Agent icin gelistirilmistir — https://github.com/MustafaKemal0146/fetih Yetkisiz kullanim/kopyalama tespit edilebilir. hash: 0f00dcc685be6825 -->まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
21st.dev Magic MCP — AI-powered UI component generation via natural language. Access to 21st.dev component library, SVGL brand logos, and real-time preview. Generate React/Tailwind components with /ui command.
日本語の概要は準備中です。原文の説明を表示しています。
AES CBC/ECB modlarına karşı kriptografik bütünlük saldırıları — bit flipping, IV manipulation, ECB cut-and-paste, CBC-MAC length extension, IV reuse
日本語の概要は準備中です。原文の説明を表示しています。
AES-GCM ve ChaCha20-Poly1305 nonce yeniden kullanımı saldırısı — GF(2^128) polinom kök bulma ile Hash Key kurtarma ve MAC sahteciliği.
日本語の概要は準備中です。原文の説明を表示しています。
tespit etmeabnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly Tespit.
日本語の概要は準備中です。原文の説明を表示しています。
Perform static and symbolic analysis of Solidity smart contracts using Slither and Mythril to tespit etmereentrancy, integer overflow, access control, and other vulnerability classes before Dağıt:ment to Ethereum mainnet.
日本語の概要は準備中です。原文の説明を表示しています。
Parses Kubernetes API server audit logs (JSON lines) to tespit etmeexec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat Tespit rules from audit event patterns. Use investigating yaparken Kubernetes cluster compromise or building k8s-specific SIEM Tespit rules.
日本語の概要は準備中です。原文の説明を表示しています。