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-recall/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-recall)<a href="https://agentmods.dev/skills/ontorata/ratary/forge-recall"><img src="https://agentmods.dev/badge/skills/ontorata/ratary/forge-recall/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/ontorata/ratary/forge-recall"><img src="https://agentmods.dev/badge/skills/ontorata/ratary/forge-recall.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 | $0.00031 | $0.00398 |
| Opus 5.5 | $0.00012 | $0.00159 |
| Sonnet 5.5 | $0.00006 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
forge-recall 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 2mo 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 Recall
Activates: every session start (before design or code).
Steps
- Ratary Context Recovery —
search_memory/ handoff (when MCP available); validate against repository - Read .ai/sessions/CURRENT.md — audit trail fallback only
- Read .ai/sync/README.md if structural / memory questions
- New machine? .ai/bootstrap/MACHINE-SETUP.md — repo first, Ratary after env
- When Phase 8.8 ledger enabled: search
inspection-patternor scoped API recall - If user said "lanjut" / handoff:
handofftag orget_memory_by_codename - Read SESSION-BOOTSTRAP.md
- Read IMPLEMENTATION-COMPLETION-PROTOCOL.md when implementing
- Read GOVERNANCE-STATUS.md when structural
- Restate task, constraints, Forge stages
- Ask blocking questions only — do not implement yet
Output
Short Recall brief: Ratary memory · repo validation · branch/commit · planned stages.
Skip when
Read-only one-line questions with no repo impact.
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.
- 2mo ago First seen · 32 lines · 31 tokens per session scan A c12065e208e5
forge-recall is a skill published in the GitHub repository ontorata/ratary (1 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 398 once invoked, about $0.0001 per session on Opus 5.5. 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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