Borrowing it
Nothing to install: this file belongs to ontorata/ratary. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ontorata/ratary/main/.cursor/skills/forge-intro/SKILL.mdgit clone --depth 1 https://github.com/ontorata/rataryWrote 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/ontorata/ratary/forge-intro)<a href="https://agentmods.dev/skills/ontorata/ratary/forge-intro"><img src="https://agentmods.dev/badge/skills/ontorata/ratary/forge-intro.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 | $0.00030 | $0.00342 |
| Opus 5 | $0.00015 | $0.00171 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
forge-intro 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.
What it actually says
Forge Intro
Agent Forge is this repo's memory-governed pipeline — different from generic skill packs:
- Recall / Remember use MCP
ratary(persistent handoffs) - Stages map to docs-ai Knowledge OS governance (constitution, ADR, phases)
- Skills live in
.cursor/skills/forge-*
Read first
- docs-ai/cross-cutting/agent-forge/PIPELINE.md
- .ai/START-HERE.md
- .ai/TASK_PROMPT.md if active work exists
Stage cheat sheet
| You are about to… | Skill |
|---|---|
| Start a session | forge-recall |
| Explore a vague idea | forge-intent |
| Code after design OK | forge-isolate → forge-blueprint → forge-execute |
| Write/fix tests | forge-prove |
| Review between tasks | forge-inspect |
| Finish a branch | forge-land |
| End session | forge-remember |
| Debug a failure | forge-diagnose |
Rules are mandatory, not suggestions — see .cursor/rules/agent-forge.mdc.
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 · 35 lines · 30 tokens per session scan A ad05e9de81be
forge-intro is a skill published in the GitHub repository ontorata/ratary (1 stars, last pushed 27d ago), licensed MIT. It adds 30 tokens to every session and 342 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 2026-08-31.
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