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.
git clone --depth 1 https://github.com/Sivan757/dsh-agent-plugins-marketWrote 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/sivan757/dsh-agent-plugins-market/create-plugin)<a href="https://agentmods.dev/commands/sivan757/dsh-agent-plugins-market/create-plugin"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/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/sivan757/dsh-agent-plugins-market/create-plugin"><img src="https://agentmods.dev/badge/commands/sivan757/dsh-agent-plugins-market/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 | $0.03483 |
| Opus 5.5 | $0.00006 | $0.01393 |
| Sonnet 5.5 | $0.00003 | $0.00697 |
| Haiku 4.5 | $0.00002 | $0.00348 |
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 today.
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 — 436 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 DeepSeek Harness 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
skilltool 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 todo_write: 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 the 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)
- Commands: User-invoked slash entries with argument hints (deploy, configure, analyze)
- 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.
- today First seen · 436 lines · 16 tokens per session scan A a62f5a1565f9
create-plugin is a command published in the GitHub repository Sivan757/dsh-agent-plugins-market (13 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 3,483 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-18.
Other commands, from other repositories
memory-report
Calibration + inventory check of your causal memory (prediction ledger accuracy, what you know).
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.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.