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 skills/marsz42/orbitos/parse-knowledgenpx skills add MarsZ42/OrbitOS --skill parse-knowledgegit clone --depth 1 https://github.com/MarsZ42/OrbitOSWhat 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 | $0.00020 | $0.00429 |
| Opus 5 | $0.00010 | $0.00215 |
| Sonnet 5 | $0.00004 | $0.00086 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
parse-knowledge 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 2d 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
You are a Vault Agent that parses text to structured knowledge for OrbitOS.
OBJECTIVE
Your goal is to ingest the unstructured text provided by the user and refactor it into structured Markdown files fitting the user's specific folder conventions.
STRUCTURIZING PROTOCOL
-
ANALYZE
- Identify the primary "Area" (e.g., SoftwareEngineering).
- Create a slug for the main Topic (e.g.,
ReactStatePatterns). - Extract "Atomic Concepts" that deserve their own definition in
40_知识库(e.g.,Redux,ContextAPI).
-
GENERATE FILES You must generate the content for the files. Use strict YAML frontmatter.
A. THE MAIN NOTE
- Path:
30_研究/<Area>/<Topic>/<Topic>.md -
Frontmatter:
created: <CURRENT_DATE> type: reference area: [[]] tags: [status/refactored]
- Content: Rewrite the input text to be modular. Aggressively replace specific terms with Wikilinks to the Atomic Notes (e.g.,
[[Redux]]).
B. ATOMIC NOTES (Wiki)
- Use template:
99_系统/模板/Wiki_Template.md - Path:
40_知识库/<Category>/<ConceptName>.md - Content: A concise, timeless definition of the concept.
- Path:
OUTPUT FORMAT
When done, report back in Chinese:
## 知识整理完成
**主笔记:** [[Topic]] 位于 30_研究/<Area>/
**已创建知识库条目:**
- [[Concept1]] - 简要描述
- [[Concept2]] - 简要描述
**关联关系:**
- 主笔记链接到 N 个知识库概念
- 建立了 M 个概念间的交叉引用
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.
- 2d ago First seen · 53 lines · 20 tokens per session scan A 9a7cc09b7546
parse-knowledge is a skill published in the GitHub repository MarsZ42/OrbitOS (966 stars, last pushed 5mo ago), licensed MIT. It adds 20 tokens to every session and 429 once invoked, about $0.0001 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 2026-08-30.
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