Gini Agent is a personal AI-agent runtime that manages conversations, runs, tasks, approvals, memory, skills, jobs, tools, traces, audit events, and runtime health across its clients. It is for an agent that can remember, learn from task outcomes, and operate through desktop or mobile interfaces with user controls. Catalogue add-ons extend the runtime with skills and instructions.
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 Open-Curiosity/gini-agent --skill lineargit clone --depth 1 https://github.com/Open-Curiosity/gini-agentWrote 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/open-curiosity/gini-agent/linear)<a href="https://agentmods.dev/skills/open-curiosity/gini-agent/linear"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/linear/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/open-curiosity/gini-agent/linear"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/linear.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 | $0.00038 | $0.01911 |
| Opus 5.5 | $0.00015 | $0.00764 |
| Sonnet 5.5 | $0.00008 | $0.00382 |
| Haiku 4.5 | $0.00004 | $0.00191 |
Grade C, and why
linear scanned grade C with 1 finding 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
**Do not refuse a Linear-related ask** unless you've actually checked. "Linear doesn't support that" is a claim that requires evidence. How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linear
Linear is reachable through the mcp_call tool. Each call hits the hosted Linear MCP server; the runtime resolves the Authorization header from the connected Linear token. You never see the token or the endpoint — you pass server: "linear" and a tool name.
Every Linear call has the same shape:
mcp_call({
server: "linear",
tool: "<tool name>",
arguments: { ... }
})
The response is a JSON string. Parse it before reporting back to the user — Linear sometimes returns nested pageInfo or arrays you should summarize, not dump.
Discovering what's available
The full inventory of Linear MCP tools is in your system prompt under "Configured MCP servers — linear — tools: ...". That list is authoritative; do not assume a tool doesn't exist just because it isn't called out below. The categories below give you defaults and taste; reach for tools from the inventory list whenever the user asks for something outside them.
When you're unsure of a tool's argument shape, either:
- Call
mcp_call({ server: "linear", tool: "search_documentation", arguments: { query: "..." } })to query Linear's own docs, or - Just try the call — the server returns a clear validation error on bad args, which is recoverable.
Do not refuse a Linear-related ask unless you've actually checked. "Linear doesn't support that" is a claim that requires evidence.
Defaults and common shapes
Issues — list / read / save
save_issue handles both create and update — with an id it updates, without one it creates.
mcp_call({ server: "linear", tool: "list_issues",
arguments: { assignee: "me", state: "started" } })
mcp_call({ server: "linear", tool: "get_issue",
arguments: { id: "LIN-123" } })
mcp_call({ server: "linear", tool: "save_issue",
arguments: { team: "ENG", title: "Login fails on Safari 17",
description: "Steps to reproduce…" } })
Useful argument quirks Linear's tool descriptions sometimes leave terse:
assignee: "me"resolves to the authenticated viewer — no user-id lookup needed.teamaccepts the team key (ENG) or UUID.stateaccepts either the workflow state id or its display name ("In Progress","Done"). Workflow state groups for filtering:backlog,unstarted,started,completed,cancelled.priorityis0|1|2|3|4where1is urgent,4is low,0is no priority.list_issuestruncatesdescriptionfor compactness. Follow up withget_issuewhen the user needs the full body.- Paginate by passing the
cursorfrompageInfo.endCursorback into the same call.
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 · 124 lines · 38 tokens per session scan C a643ab7a47ae
linear is a skill published in the GitHub repository Open-Curiosity/gini-agent (2,103 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 1,911 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
autogpt-agents
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
stale-sweep
Sweep the googleapis/mcp-toolbox repo for issues and PRs with no real activity in N days (default 60), sort each by whose silence it is (the author's, ours, or nobody's), and draft the nudge or close comment. Use whenever a maintainer asks for a stale sweep, backlog cleanup, or an SLO check, e.g. "stale sweep", "find…
linear-tickets
Create or update high-quality Linear tickets while preserving the originating report, diagnostic links, attachments, and reporter/requester attribution. Read this whenever someone asks to create, recreate, or update a Linear issue.
clinical-reports
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
clinical-decision-support
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading…