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 knowledge-basegit 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/knowledge-base)<a href="https://agentmods.dev/skills/open-curiosity/gini-agent/knowledge-base"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/knowledge-base/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/knowledge-base"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/knowledge-base.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 6 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00119 | $0.02397 |
| Opus 5.5 | $0.00048 | $0.00959 |
| Sonnet 5.5 | $0.00024 | $0.00479 |
| Haiku 4.5 | $0.00012 | $0.00240 |
Grade A, and why
knowledge-base 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base (Wiki)
You maintain a compounding knowledge base: a wiki of interlinked markdown
notes that lives in the workspace. Unlike a chat that forgets, the wiki is
written once and kept correct — every new source is folded into existing
pages and cross-referenced, so the knowledge graph gets denser over time. The
files are plain markdown with [[wikilinks]], so the user can also open them
in Obsidian or any editor.
When to use
- "Set up a knowledge base / wiki", "start a company wiki", "organize my notes".
- "Add this to the wiki", "ingest this article/doc/page", "remember this for the team".
- A question that the wiki should answer ("what do we know about X?").
- "Audit / lint / health-check the wiki", "find broken links / orphans".
When NOT to use
- One-off facts about the user or your own behavior → that's memory (retain), not the wiki.
- Throwaway scratch work, or content the user doesn't want kept.
Layout
The wiki lives under wiki/ in the workspace (use an existing root such as
knowledge-base/ if one is already there). Structure:
wiki/
├── SCHEMA.md # the domain, conventions, and tag taxonomy — defined once
├── index.md # catalog of every page, grouped by type, each with a one-line summary
├── log.md # append-only action log (newest entries at the bottom)
├── raw/ # captured sources, IMMUTABLE — you read these, never edit them
│ ├── articles/
│ ├── docs/
│ └── transcripts/
└── pages/ # the wiki pages (one entity/concept/comparison/query each)
Page filenames are lowercase, hyphenated slugs, no spaces: acme-robotics.md,
atlas-robot.md. A [[Display Name]] link resolves to the page whose slug
equals the slugified display name, so [[Acme Robotics]] and [[acme-robotics]]
both point at pages/acme-robotics.md.
Reading the wiki
When the user asks how to read or browse what you've built, tell them: it's
plain markdown they can open in any editor, and the pages are Obsidian-native
by design — they can open the wiki/ folder as an Obsidian vault to get graph
view, backlinks, and clickable [[wikilinks]] for free. (The files live in the
workspace, so point the vault at the workspace wiki/ folder.)
What ships with it
2 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 · 197 lines · 119 tokens per session scan A aa0a3abcb87d
knowledge-base is a skill published in the GitHub repository Open-Curiosity/gini-agent (2,034 stars, last pushed 3mo ago), licensed MIT. It adds 119 tokens to every session and 2,397 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
memory-consolidate
Consolidate L1 memory atoms into L2 scene blocks and L3 persona. Invoked by the memory-consolidator agent, or manually via /memory-consolidate.
tmem-cli
Inspect/manage the local memory store via the tmem CLI — counts, keyword/hybrid search, view persona, list/open scenes (tmem scene ), recent changes, sync vectors, thresholds, and cross-project search (tmem search --all, tmem projects). Trigger on "how many memories", "show my persona", "what scenes exist", "search…
memory-view
Open the memory visualiser in the browser — a health check on the memory store: is it working, what is wrong, and what to run to fix it. Run manually via /memory-view.
memory-seed
Extract L1 memory atoms from Claude Code conversation history. Run manually via /memory-seed.
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…