Completes the Windows-gating coverage for the built-in skills/ tree. Every
bundled SKILL.md now carries an explicit platforms: declaration so the
loader (agent.skill_utils.skill_matches_platform) can skip-load skills
that don't fit the current OS.
74 skills declared cross-platform (platforms: [linux, macos, windows]):
Creative (16): ascii-art, ascii-video, architecture-diagram, baoyu-comic,
baoyu-infographic, claude-design, creative-ideation, design-md,
excalidraw, humanizer, manim-video, p5js, pixel-art,
popular-web-designs, pretext, sketch, songwriting-and-ai-music,
touchdesigner-mcp
Autonomous agents: claude-code, codex, hermes-agent, opencode
Data/devops: jupyter-live-kernel, kanban-orchestrator, kanban-worker,
webhook-subscriptions, dogfood, codebase-inspection
GitHub: github-auth, github-code-review, github-issues,
github-pr-workflow, github-repo-management
Media: gif-search, heartmula, songsee, spotify, youtube-content
MCP / email / gaming / notes / smart-home: native-mcp, himalaya,
pokemon-player, obsidian, openhue
mlops (non-broken): weights-and-biases, huggingface-hub, llama-cpp,
outlines, segment-anything-model, dspy, trl-fine-tuning
Productivity: airtable, google-workspace, linear, maps, nano-pdf,
notion, ocr-and-documents, powerpoint
Red-teaming / research: godmode, arxiv, blogwatcher, llm-wiki,
polymarket
Software-dev: debugging-hermes-tui-commands, hermes-agent-skill-authoring,
node-inspect-debugger, plan, requesting-code-review, spike,
subagent-driven-development, systematic-debugging,
test-driven-development, writing-plans
Misc: yuanbao
5 skills gated from Windows (platforms: [linux, macos]):
mlops/inference/vllm (serving-llms-vllm)
vLLM is officially Linux-only; Windows requires WSL.
mlops/training/axolotl
Axolotl's flash-attn + deepspeed + bitsandbytes stack is Linux-first.
mlops/training/unsloth
Requires Triton + xformers + flash-attn — Linux only in practice.
mlops/models/audiocraft (audiocraft-audio-generation)
torchaudio ffmpeg backend + encodec dependencies are Linux-first.
mlops/inference/obliteratus
Research abliteration workflow; relies on Linux-focused pytorch
kernels and MLX — no first-class Windows path.
Same strict-over-lenient policy as the optional-skills sweep: when the
underlying tool's Windows support is rough, missing, or WSL-only, gate the
skill. Easier to un-gate after verified Windows support lands than to leak
partial support that manifests as mid-task failures.
Combined with prior commits in this branch, every bundled SKILL.md
(skills/ + optional-skills/) now has a platforms: declaration.
7.1 KiB
| name | description | version | author | license | platforms | metadata | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| writing-plans | Write implementation plans: bite-sized tasks, paths, code. | 1.1.0 | Hermes Agent (adapted from obra/superpowers) | MIT |
|
|
Writing Implementation Plans
Overview
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.
When to Use
Always use before:
- Implementing multi-step features
- Breaking down complex requirements
- Delegating to subagents via subagent-driven-development
Don't skip when:
- Feature seems simple (assumptions cause bugs)
- You plan to implement it yourself (future you needs guidance)
- Working alone (documentation matters)
Bite-Sized Task Granularity
Each task = 2-5 minutes of focused work.
Every step is one action:
- "Write the failing test" — step
- "Run it to make sure it fails" — step
- "Implement the minimal code to make the test pass" — step
- "Run the tests and make sure they pass" — step
- "Commit" — step
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]
Plan Document Structure
Header (Required)
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]
---
Task Structure
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"
```
Writing Process
Step 1: Understand Requirements
Read and understand:
- Feature requirements
- Design documents or user description
- Acceptance criteria
- Constraints
Step 2: Explore the Codebase
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")
Step 3: Design Approach
Decide:
- Architecture pattern
- File organization
- Dependencies needed
- Testing strategy
Step 4: Write Tasks
Create tasks in order:
- Setup/infrastructure
- Core functionality (TDD for each)
- Edge cases
- Integration
- Cleanup/documentation
Step 5: Add Complete Details
For each task, include:
- Exact file paths (not "the config file" but
src/config/settings.py) - Complete code examples (not "add validation" but the actual code)
- Exact commands with expected output
- Verification steps that prove the task works
Step 6: Review the Plan
Check:
- Tasks are sequential and logical
- Each task is bite-sized (2-5 min)
- File paths are exact
- Code examples are complete (copy-pasteable)
- Commands are exact with expected output
- No missing context
- DRY, YAGNI, TDD principles applied
Step 7: Save the Plan
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]"
Principles
DRY (Don't Repeat Yourself)
Bad: Copy-paste validation in 3 places Good: Extract validation function, use everywhere
YAGNI (You Aren't Gonna Need It)
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
TDD (Test-Driven Development)
Every task that produces code should include the full TDD cycle:
- Write failing test
- Run to verify failure
- Write minimal code
- Run to verify pass
See test-driven-development skill for details.
Frequent Commits
Commit after every task:
git add [files]
git commit -m "type: description"
Common Mistakes
Vague Tasks
Bad: "Add authentication" Good: "Create User model with email and password_hash fields"
Incomplete Code
Bad: "Step 1: Add validation function" Good: "Step 1: Add validation function" followed by the complete function code
Missing Verification
Bad: "Step 3: Test it works"
Good: "Step 3: Run pytest tests/test_auth.py -v, expected: 3 passed"
Missing File Paths
Bad: "Create the model file"
Good: "Create: src/models/user.py"
Execution Handoff
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:
- Fresh
delegate_taskper task with full context - Spec compliance review after each task
- Code quality review after spec passes
- Proceed only when both reviews approve
Remember
Bite-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.