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 marysatasselshaped667/skills-collection-1 --skill agentfoliogit clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1Wrote 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/marysatasselshaped667/skills-collection-1/agentfolio)<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/agentfolio"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/agentfolio/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/marysatasselshaped667/skills-collection-1/agentfolio"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/agentfolio.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 | $0.00023 | $0.00900 |
| Opus 5 | $0.00012 | $0.00450 |
| Sonnet 5 | $0.00005 | $0.00180 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
agentfolio 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 11d 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
92% identical to agentfolio — 4 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentFolio
Role: Autonomous Agent Discovery Guide
Use this skill when you want to discover, compare, and research autonomous AI agents across ecosystems. AgentFolio is a curated directory at https://agentfolio.io that tracks agent frameworks, products, and tools.
This skill helps you:
- Find existing agents before building your own from scratch.
- Map the landscape of agent frameworks and hosted products.
- Collect concrete examples and benchmarks for agent capabilities.
Capabilities
- Discover autonomous AI agents, frameworks, and tools by use case.
- Compare agents by capabilities, target users, and integration surfaces.
- Identify gaps in the market or inspiration for new skills/workflows.
- Gather example agent behavior and UX patterns for your own designs.
- Track emerging trends in agent architectures and deployments.
How to Use AgentFolio
-
Open the directory
- Visit
https://agentfolio.ioin your browser. - Optionally filter by category (e.g., Dev Tools, Ops, Marketing, Productivity).
- Visit
-
Search by intent
- Start from the problem you want to solve:
- “customer support agents”
- “autonomous coding agents”
- “research / analysis agents”
- Use keywords in the AgentFolio search bar that match your domain or workflow.
- Start from the problem you want to solve:
-
Evaluate candidates
- For each interesting agent, capture:
- Core promise (what outcome it automates).
- Input / output shape (APIs, UI, data sources).
- Autonomy model (one-shot, multi-step, tool-using, human-in-the-loop).
- Deployment model (SaaS, self-hosted, browser, IDE, etc.).
- For each interesting agent, capture:
-
Synthesize insights
- Use findings to:
- Decide whether to integrate an existing agent vs. build your own.
- Borrow successful UX and safety patterns.
- Position your own agent skills and workflows relative to the ecosystem.
- Use findings to:
Example Workflows
1) Landscape scan before building a new agent
- Define the problem: “autonomous test failure triage for CI pipelines”.
- Use AgentFolio to search for:
- “testing agent”, “CI agent”, “DevOps assistant”, “incident triage”.
- For each relevant agent:
- Note supported platforms (GitHub, GitLab, Jenkins, etc.).
- Capture how they explain autonomy and safety boundaries.
- Record pricing/licensing constraints if you plan to adopt instead of build.
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.
- 11d ago First seen · 97 lines · 23 tokens per session scan A 2ab2d849c89f
agentfolio is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 900 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to agentfolio, differing in 4 lines, and is treated as a copy.
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