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/jpoutrin/product-forge/create-agentnpx skills add jpoutrin/product-forge --skill create-agentgit clone --depth 1 https://github.com/jpoutrin/product-forgeWhat 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 | $0.00048 | $0.00693 |
| Opus 5 | $0.00024 | $0.00347 |
| Sonnet 5 | $0.00010 | $0.00139 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
create-agent 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 2d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Agent Skill
Create new Claude Code agents with proper configuration and structure.
Agent File Format
Agents are markdown files in the agents/ directory with YAML frontmatter:
---
name: agent-name
description: Full description of what the agent does and when to use it
tools: Glob, Grep, Read, Write, Edit, Bash, WebFetch, WebSearch, TodoWrite
model: sonnet
color: green
---
# Agent Title
Agent prompt content goes here...
Required Frontmatter Fields
| Field | Description | Example |
|---|---|---|
name |
Unique identifier (kebab-case) | code-reviewer |
description |
One-line summary of agent's purpose | Reviews code for quality and best practices |
tools |
Comma-separated list of available tools | Glob, Grep, Read, Write, Edit, Bash |
model |
Model to use: opus, sonnet, or haiku |
sonnet |
color |
Status line color | green, blue, purple, red, orange, cyan, magenta, yellow, pink, teal, violet |
Model Selection Guidelines
- opus: Strategic decisions, complex architecture, security-critical, compliance, executive-level analysis
- sonnet: Technical implementation, code writing, general development tasks (most common)
- haiku: Quick lookups, simple transformations, fast responses
Available Tools
Common tool combinations by agent type:
Code Development Agents
Glob, Grep, Read, Write, Edit, Bash, TodoWrite
Research/Analysis Agents
Glob, Grep, Read, WebFetch, WebSearch, TodoWrite
Full-Featured Agents
Glob, Grep, Read, Write, Edit, Bash, WebFetch, WebSearch, TodoWrite
Creation Process
- Determine the agent's purpose and expertise area
- Choose appropriate model based on complexity
- Select tools needed for the agent's tasks
- Write clear, actionable agent prompt
- Save to
plugins/<plugin-name>/agents/<agent-name>.md
Example Agent
---
name: code-reviewer
description: Reviews code changes for quality, security, and best practices. Use when user wants code reviewed, needs security audit, or asks about code quality.
tools: Glob, Grep, Read, TodoWrite
model: sonnet
color: yellow
---
# Code Reviewer Agent
You are an expert code reviewer focused on quality, security, and maintainability.
## Review Checklist
- Code correctness and logic
- Security vulnerabilities
- Performance considerations
- Code style and consistency
- Test coverage
- Documentation
## Output Format
Provide structured feedback with:
1. Summary of changes
2. Issues found (categorized by severity)
3. Recommendations
4. Approval status
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.
- 2d ago First seen · 103 lines · 48 tokens per session scan A 0ab96a79cc4b
create-agent is a skill published in the GitHub repository jpoutrin/product-forge (15 stars, last pushed 6mo ago), licensed MIT. It adds 48 tokens to every session and 693 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.