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 agentmods add skills/andisab/swe-marketplace/agent-devnpx skills add andisab/swe-marketplace --skill agent-devgit clone --depth 1 https://github.com/andisab/swe-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/andisab/swe-marketplace/agent-dev)<a href="https://agentmods.dev/skills/andisab/swe-marketplace/agent-dev"><img src="https://agentmods.dev/badge/skills/andisab/swe-marketplace/agent-dev.svg" alt="Measured on agentmods" 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 | $0.00086 | $0.02595 |
| Opus 5 | $0.00043 | $0.01298 |
| Sonnet 5 | $0.00017 | $0.00519 |
| Haiku 4.5 | $0.00009 | $0.00260 |
Grade A, and why
agent-dev 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 6d 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 — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Dev Skill
This skill helps create production-ready Claude Code sub-agent definitions following Anthropic's official specifications and best practices.
Core Principles
1. Single Responsibility
Each agent should have ONE clear purpose. Avoid creating multipurpose agents that try to do everything.
Good: postgres-expert - PostgreSQL database management and optimization
Bad: database-expert - All databases (too broad)
2. Discovery-Optimized Descriptions
The description field is critical for Claude to discover when to use your agent. Include:
- What: Clear statement of capabilities
- When: Specific scenarios that trigger usage
- Examples: Concrete usage patterns with user/assistant dialogue
- Trigger terms: Keywords users might mention
3. Tool Restriction Strategy
Grant only necessary tools following principle of least privilege:
- Omit
toolsfield: Inherits all tools from main conversation (use for general-purpose agents) - Specify tools list: Grant specific tools (use for focused/security-sensitive agents)
4. Model Selection
Choose the right model for the task:
sonnet: Default choice for most agents (balanced performance/cost)opus: Complex reasoning, architecture decisions, critical taskshaiku: Simple, repetitive tasks with clear patternsinherit: Match parent conversation's model
Agent Structure
---
name: agent-identifier
description: >
Primary description with capabilities and use cases.
Examples:
<example>
Context: Situation description
user: "User request example"
assistant: "I'll use the agent-name to handle this task."
<commentary>
Explanation of why this agent is appropriate.
</commentary>
</example>
<example>
Context: Another scenario
user: "Different request pattern"
assistant: "Let me use the agent-name for this."
<commentary>
Another use case explanation.
</commentary>
</example>
tools: Tool1, Tool2, Tool3 # Optional - omit to inherit all tools
model: sonnet # Optional - sonnet, opus, haiku, or inherit
color: "#hexcolor" # Optional - UI color coding
---
System prompt content starts here.
You are a [role description] specializing in [domain].
## Core Responsibilities
- List key responsibilities
- Be specific and actionable
- Include success criteria
## Approach
- Describe methodology
- Include examples
- Reference best practices
## Constraints
- Limitations and boundaries
- Security considerations
- Performance requirements
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.
- 6d ago First seen · 415 lines · 86 tokens per session scan A 66ff8e416d64
agent-dev is a skill published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed 18d ago), licensed MIT. It adds 86 tokens to every session and 2,595 once invoked, about $0.0004 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
pr
Comprehensive PR/issue review - analyzes architecture, tests, identifies unrelated changes mixed in, drafts review comment or issue comment. Use when user asks to review a PR, check a PR, look at PR changes, or comment on an issue.
writing-style
Use for technical communication - GitHub/GitLab tickets, PR/MR descriptions, issue comments, code review comments, commit messages. Direct, brief style with no AI-speak. NOT for README.md, public docs, or blog posts.
root-cause-investigator
Systematic root cause analysis for errors, bugs, and unexpected behaviors using 5-Why methodology. Use when user reports errors, build failures, test failures, performance issues, integration problems, or any "it's not working" scenarios.
advisor
Anthropic Advisor Tool — pair a cheaper executor model with a more capable advisor model for guidance. TRIGGER when: user asks about advisor tool, model pairing, Sonnet+Opus, cost optimization with quality, or 'how to make Sonnet smarter'. DO NOT TRIGGER when: general API questions (use claude-api skill instead).
ccc-onboard
CC Commander contributor onboarding workflow. Detects project conventions, asks for contributor role, builds a tailored first-hour checklist, starter links, and…
ccc-qa
QA workflow. Runs unit + integration + E2E tests, coverage delta, edge case enumeration, flaky test quarantine. Delegates to qa-engineer agent.