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 MarecGents/marec-agent-skills --skill templategit clone --depth 1 https://github.com/MarecGents/marec-agent-skillsWrote 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/marecgents/marec-agent-skills/template)<a href="https://agentmods.dev/skills/marecgents/marec-agent-skills/template"><img src="https://agentmods.dev/badge/skills/marecgents/marec-agent-skills/template/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/marecgents/marec-agent-skills/template"><img src="https://agentmods.dev/badge/skills/marecgents/marec-agent-skills/template.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 | $0.00041 | $0.00404 |
| Opus 5 | $0.00020 | $0.00202 |
| Sonnet 5 | $0.00008 | $0.00081 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
your-skill-name scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
# compatibility: Describe any environment requirements (e.g., "Requires git and curl") What it actually says
Your Skill Name
Brief introduction explaining what this skill helps the agent accomplish.
When to Use
- Scenario or request pattern 1
- Scenario or request pattern 2
- Scenario or request pattern 3
Instructions
Provide step-by-step instructions the agent should follow when this skill is activated. Be specific and prescriptive.
Step 1: Understand the request
- Guideline or sub-step A
- Guideline or sub-step B
Step 2: Execute
- Action 1
- Action 2
Step 3: Verify
- Check result
- Confirm with user if needed
Examples
Example 1: Typical usage
User input: "..."
Agent should: ...
Example 2: Edge case
User input: "..."
Agent should: ...
Notes
- Edge cases or caveats the agent should watch for
- Any constraints or limitations
- References to related files in
references/orscripts/
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.
- 8d ago First seen · 70 lines · 41 tokens per session scan A d12dc1769594
your-skill-name is a skill published in the GitHub repository MarecGents/marec-agent-skills (3 stars, last pushed 10d ago), licensed MIT. It adds 41 tokens to every session and 404 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
fabric-fusion
Multi-model deliberation. Two to 8 distinct models answer in parallel with web-capable tools, then a judge compares consensus, contradictions, coverage gaps, unique insights, and blind spots. Act mode runs 1–4 read-only references, then one actor reconciles and executes. Use when the cost of being wrong justifies…
fabric-rlm
Recursively decomposes oversized tasks into bounded child Pi agents with fresh context windows. Use for whole-repo audits, massive-context analysis, and multi-file refactors that do not fit one context.
fabric-exec
Python-only troubleshooting and advanced host API reference for fabricexec. Routine pi. coding calls are documented by ambient guidance; load this skill only after an argument-shape error or when an advanced surface needs exact contracts.
fabric-schema
Uses Fabric's typed Schema evidence loop and, when enabled, its bounded local-file transaction channel. Use when surprise must void a plan and mutation claims need explicit postconditions.
fabric-workflow
Runs a dynamic Pi Fabric workflow with code-held phases, fan-out, pipelines, structured agents, and best-effort verification. Use for large audits, migrations, parallel research, or explicit workflow requests.
fabric-ambient
Creates a persistent Pi Fabric supervisor or advisor profile. Use for ambient supervision, ongoing peer review, an advisor, or a goal watcher without another extension.