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 JunMystery/Agent-Guidance-Python --skill knowledge-opsgit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWrote 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/junmystery/agent-guidance-python/knowledge-ops)<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/knowledge-ops"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/knowledge-ops/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/junmystery/agent-guidance-python/knowledge-ops"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/knowledge-ops.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 | $0.00053 | $0.01402 |
| Opus 5.5 | $0.00021 | $0.00561 |
| Sonnet 5.5 | $0.00011 | $0.00280 |
| Haiku 4.5 | $0.00005 | $0.00140 |
Grade A, and why
knowledge-ops 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 1mo 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.
This is a copy
100% identical to knowledge-ops — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Operations
Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across multiple stores.
Prefer the live workspace model:
- code work lives in the real cloned repos
- active execution context lives in GitHub, Linear, and repo-local working-context files
- broader human-facing notes can live in a non-repo context/archive folder
- durable cross-machine memory belongs in the knowledge base, not in a shadow repo workspace
When to Activate
- User wants to save information to their knowledge base
- Ingesting documents, conversations, or data into structured storage
- Syncing knowledge across systems (local files, MCP memory, Supabase, Git repos)
- Deduplicating or organizing existing knowledge
- User says "save this to KB", "sync knowledge", "what do I know about X", "ingest this", "update the knowledge base"
- Any knowledge management task beyond simple memory recall
Knowledge Architecture
Layer 1: Active execution truth
- Sources: GitHub issues, PRs, discussions, release notes, Linear issues/projects/docs
- Use for: the current operational state of the work
- Rule: if something affects an active engineering plan, roadmap, rollout, or release, prefer putting it here first
Layer 2: Claude Code Memory (Quick Access)
- Path:
~/.claude/projects/*/memory/ - Format: Markdown files with frontmatter
- Types: user preferences, feedback, project context, reference
- Use for: quick-access context that persists across conversations
- Automatically loaded at session start
Layer 3: MCP Memory Server (Structured Knowledge Graph)
- Access: MCP memory tools (create_entities, create_relations, add_observations, search_nodes)
- Use for: Semantic search across all stored memories, relationship mapping
- Cross-session persistence with queryable graph structure
Layer 4: Knowledge base repo / durable document store
- Use for: curated durable notes, session exports, synthesized research, operator memory, long-form docs
- Rule: this is the preferred durable store for cross-machine context when the content is not repo-owned code
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.
- 1mo ago First seen · 155 lines · 53 tokens per session scan A a1fecc085d51
knowledge-ops is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 1,402 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to knowledge-ops, differing in 0 lines, and is treated as a copy.
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