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 Semiotronika/LINZA-MCP --skill linza-operatorgit clone --depth 1 https://github.com/Semiotronika/LINZA-MCPWrote 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/semiotronika/linza-mcp/linza-operator)<a href="https://agentmods.dev/skills/semiotronika/linza-mcp/linza-operator"><img src="https://agentmods.dev/badge/skills/semiotronika/linza-mcp/linza-operator/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/semiotronika/linza-mcp/linza-operator"><img src="https://agentmods.dev/badge/skills/semiotronika/linza-mcp/linza-operator.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 | $0.00061 | $0.01375 |
| Opus 5.5 | $0.00024 | $0.00550 |
| Sonnet 5.5 | $0.00012 | $0.00275 |
| Haiku 4.5 | $0.00006 | $0.00137 |
Grade A, and why
linza-operator 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.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LINZA Operator
Core Rule
Do not present LINZA as a flat MCP tool list. LINZA is a review-gated workflow:
load/index -> analyze -> review intents -> explicit apply -> context export
The user reviews meaning and approvals. The agent operates the tools.
Default Entry Points
Use these first:
agent_workspace(action="doctor")for readiness and safety status.guide_next_stepsfor the current onboarding/review stage.agent_workspacefor workspace maps, teaching, supervised growth, artifacts, trace review, memory search, review intents, graph connect, and context export.
When calling guide_next_steps, pass the user's language when known:
language="en" for English sessions and language="ru" for Russian sessions.
When the user asks "what is here", "where should we start", or "what should
the agent do next", use agent_workspace(action="map") first. Present the
user view, then use the agent view only to choose the next precise action.
When the user asks "what connects X and Y", use
agent_workspace(action="connect", source="X", target="Y") first. Present the
route and confidence labels, then read exact source files only if needed.
When the user wants the agent to learn the base style before continuing, use
agent_workspace(action="teach"). Show the small read-only seed batch and ask
the user to accept exact rq-* items that look right.
When showing agent_workspace(action="review_next"), present the readable
review-intent view from review_cards or human_view.cards. Treat raw
rq-* / aw-* IDs as apply handles, not as the main explanation.
When the user has accepted initial seed domains/material formats/hierarchy and
wants the agent to continue building the base, use
agent_workspace(action="grow", mode="assisted"). Keep the first batch dry-run,
show the selected items and selected_rules, then use dry_run=false only for
a small approved batch. This is supervised growth, not blind autopilot.
Use low-level tools only to support a clear workflow: indexing, search, explanation, review queue generation, and exact dry-run apply. Profiles, specialized reports, tag/property helpers, and legacy apply helpers are advanced tools, not the normal operator surface.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 153 lines · 61 tokens per session scan A 8c560185fcb5
linza-operator is a skill published in the GitHub repository Semiotronika/LINZA-MCP (5 stars, last pushed 9d ago), licensed MIT. It adds 61 tokens to every session and 1,375 once invoked, about $0.0002 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.
Other skills, from other repositories
mneme-prime
Use when the user starts a new task that may have prior vault context worth surfacing. Invokes the mnemeprime MCP tool to build a token-budgeted preamble of recent sessions and topic matches.
mneme-search
Use when the user asks a factual question whose answer might live in the vault. Invokes mnemesearch. Production mnemesearch is FTS5 BM25. The experimental feature-hashed lexical-vector backend is not wired into MCP. KG enrichment is gated to summarize or timeline when full-profile graph state is active.
siyuan-plugin-development
Official frontend plugin development workflow. After the user explicitly opts in, clarify missing requirements, confirm a short plan, implement in a managed copy, verify, and package. Installation and activation require separate authorization.
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
wenlan-handoff
End a work session. Stores durable captures, writes a narrative session log, and automatically applies typed item-level deltas to the current Space Brief. Invoked as /wenlan-handoff.
remnic-entities
Browse entities in the Remnic knowledge graph and surface their facts and relationships. Trigger phrases include "tell me about the entity", "look up", "what do we know about".