Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mhattingpete/claude-skills-marketplace --skill feature-planninggit clone --depth 1 https://github.com/mhattingpete/claude-skills-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mhattingpete/claude-skills-marketplace/feature-planning)<a href="https://agentmods.dev/skills/mhattingpete/claude-skills-marketplace/feature-planning"><img src="https://agentmods.dev/badge/skills/mhattingpete/claude-skills-marketplace/feature-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mhattingpete/claude-skills-marketplace/feature-planning"><img src="https://agentmods.dev/badge/skills/mhattingpete/claude-skills-marketplace/feature-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.00835 |
| Opus 5 | $0.00016 | $0.00417 |
| Sonnet 5 | $0.00006 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
feature-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Planning
Systematically analyze feature requests and create detailed, actionable implementation plans.
When to Use
- Requests new feature ("add user authentication", "build dashboard")
- Asks for enhancements ("improve performance", "add export")
- Describes complex multi-step changes
- Explicitly asks for planning ("plan how to implement X")
- Provides vague requirements needing clarification
Planning Workflow
1. Understand Requirements
Ask clarifying questions:
- What problem does this solve?
- Who are the users?
- Specific technical constraints?
- What does success look like?
Explore the codebase:
Use Task tool with subagent_type='Explore' and thoroughness='medium' to understand:
- Existing architecture and patterns
- Similar features to reference
- Where new code should live
- What will be affected
2. Analyze & Design
Identify components:
- Database changes (models, migrations, schemas)
- Backend logic (API endpoints, business logic, services)
- Frontend changes (UI, state, routing)
- Testing requirements
- Documentation updates
Consider architecture:
- Follow existing patterns (check CLAUDE.md)
- Identify reusable components
- Plan error handling and edge cases
- Consider performance implications
- Think about security and validation
Check dependencies:
- New packages/libraries needed
- Compatibility with existing stack
- Configuration changes required
3. Create Implementation Plan
Break feature into discrete, sequential tasks:
## Feature: [Feature Name]
### Overview
[Brief description of what will be built and why]
### Architecture Decisions
- [Key decision 1 and rationale]
- [Key decision 2 and rationale]
### Implementation Tasks
#### Task 1: [Component Name]
- **File**: `path/to/file.py:123`
- **Description**: [What needs to be done]
- **Details**:
- [Specific requirement 1]
- [Specific requirement 2]
- **Dependencies**: None (or list task numbers)
#### Task 2: [Component Name]
...
### Testing Strategy
- [What types of tests needed]
- [Critical test cases to cover]
### Integration Points
- [How this connects with existing code]
- [Potential impacts on other features]
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 149 lines · 32 tokens per session scan A fdc307080cfd
feature-planning is a skill published in the GitHub repository mhattingpete/claude-skills-marketplace (671 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 835 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
security-compliance
Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR, HIPAA), conducting threat modeling and risk assessments, managing security operations and incident response, and embedding security throughout the SDLC.
stride-analysis-patterns
Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
prompts-chat
Use when searching, installing, or improving AI skills and prompts via prompts.chat or skills.sh. Triggers on skill search, prompt lookup, install skill, improve prompt, prompts.chat.
humanize-korean
A Korean editing tool that removes translation-like phrasing and common AI writing habits while preserving facts, numbers, names, and quotations. It is intended for existing Korean prose, not for writing new marketing copy.
continuous-learning-v2
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.