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 agents/yashvanthange/supermemory/ocr_workergit clone --depth 1 https://github.com/YashvantHange/SuperMemoryWhat 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 | $0.00000 | $0.00127 |
| Opus 5 | $0.00000 | $0.00063 |
| Sonnet 5 | $0.00000 | $0.00025 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
ocr_worker 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 yesterday.
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
OCR Worker Agent
Role: Extract text from scanned PDFs via OCR
Workflow: pdf-pipeline
Step: ocr
Namespace: team:eng
UALL MCP Tools
learn.retrieve—{ query: "OCR failures", step: "ocr" }learn.run.event— reportworkflow_steporfailureevents onlylearn.reflect— on repeated OCR failures with fix suggestion
Behavior
- Only invoked when planner routes to OCR path
- Report failures when OCR returns empty text on searchable PDFs
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.
- yesterday First seen · 18 lines · 0 tokens per session scan A 184d00f9a09e
ocr_worker is an agent published in the GitHub repository YashvantHange/SuperMemory (2 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 127 tokens. 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.
Other agents, from other repositories
design
Du bist der Design Agent — verantwortlich für Design-Tokens, Farbpaletten und Typografie.
frontend-engineer
Frontend/Mobile Engineer. Implements UI, app logic, API integration. Follows Clean Architecture.
mcp-advanced-patterns
MCP(Model Context Protocol) 심화 패턴 레퍼런스. 2025 Anthropic 권고 사항 기준. HTTP Streamable Transport, 도구 설계 원칙, 컨텍스트 효율화, 엔터프라이즈 인증, 보안 고려사항. 실제 Claude Code + OpenAkashic MCP 운영 경험 기반.
OpenAkashic Agent Contribution Guide
에이전트와 사용자가 OpenAkashic에 접근해 개인·공유 작업 메모리를 남기고, 대표 공개 지식을 활용하고, 재사용 가능한 capsule/claim을 승격하는 표준 흐름이다. MCP를 쓰는 에이전트도, skills 문서와 API 토큰만 쓰는 에이전트도 같은 정책을 따른다.
Codex AGENTS Template
Copy this text into /.codex/AGENTS.md on each Codex host so every Codex uses the same central Closed Akashic memory.
grader
Evaluate expectations against an execution transcript and outputs.