Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add SummerSec/SumSec-Skills/plugin install plugin-devWrote 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/commands/summersec/sumsec-skills/create-plugin)<a href="https://agentmods.dev/commands/summersec/sumsec-skills/create-plugin"><img src="https://agentmods.dev/badge/commands/summersec/sumsec-skills/create-plugin/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/commands/summersec/sumsec-skills/create-plugin"><img src="https://agentmods.dev/badge/commands/summersec/sumsec-skills/create-plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00016 | $0.03546 |
| Opus 5.5 | $0.00006 | $0.01418 |
| Sonnet 5.5 | $0.00003 | $0.00709 |
| Haiku 4.5 | $0.00002 | $0.00355 |
Grade A, and why
create-plugin 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 1mo 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.
This is a copy
100% identical to create-plugin — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 450 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plugin Creation Workflow
Guide the user through creating a complete, high-quality Claude Code plugin from initial concept to tested implementation. Follow a systematic approach: understand requirements, design components, clarify details, implement following best practices, validate, and test.
Core Principles
- Ask clarifying questions: Identify all ambiguities about plugin purpose, triggering, scope, and components. Ask specific, concrete questions rather than making assumptions. Wait for user answers before proceeding with implementation.
- Load relevant skills: Use the Skill tool to load plugin-dev skills when needed (plugin-structure, hook-development, agent-development, etc.)
- Use specialized agents: Leverage agent-creator, plugin-validator, and skill-reviewer agents for AI-assisted development
- Follow best practices: Apply patterns from plugin-dev's own implementation
- Progressive disclosure: Create lean skills with references/examples
- Use TodoWrite: Track all progress throughout all phases
Initial request: $ARGUMENTS
Phase 1: Discovery
Goal: Understand what plugin needs to be built and what problem it solves
Actions:
- Create todo list with all 7 phases
- If plugin purpose is clear from arguments:
- Summarize understanding
- Identify plugin type (integration, workflow, analysis, toolkit, etc.)
- If plugin purpose is unclear, ask user:
- What problem does this plugin solve?
- Who will use it and when?
- What should it do?
- Any similar plugins to reference?
- Summarize understanding and confirm with user before proceeding
Output: Clear statement of plugin purpose and target users
Phase 2: Component Planning
Goal: Determine what plugin components are needed
MUST load plugin-structure skill using Skill tool before this phase.
Actions:
- Load plugin-structure skill to understand component types
- Analyze plugin requirements and determine needed components:
- Skills: Specialized knowledge OR user-initiated actions (deploy, configure, analyze). Skills are the preferred format for both — see note below.
- Agents: Autonomous tasks? (validation, generation, analysis)
- Hooks: Event-driven automation? (validation, notifications)
- MCP: External service integration? (databases, APIs)
- Settings: User configuration? (.local.md files)
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.
- 1mo ago First seen · 450 lines · 16 tokens per session scan A f2c634dfc0b5
create-plugin is a command published in the GitHub repository SummerSec/SumSec-Skills (8 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 3,546 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to create-plugin, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
revise-agents-md
Update AGENTS.md with learnings from this session.
hello-stranger
Say hello stranger.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.