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 ai-ecoverse/slicc --skill gelatieregit clone --depth 1 https://github.com/ai-ecoverse/sliccWrote 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/ai-ecoverse/slicc/gelatiere)<a href="https://agentmods.dev/skills/ai-ecoverse/slicc/gelatiere"><img src="https://agentmods.dev/badge/skills/ai-ecoverse/slicc/gelatiere/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/ai-ecoverse/slicc/gelatiere"><img src="https://agentmods.dev/badge/skills/ai-ecoverse/slicc/gelatiere.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 | $0.00220 | $0.01918 |
| Opus 5.5 | $0.00088 | $0.00767 |
| Sonnet 5.5 | $0.00044 | $0.00384 |
| Haiku 4.5 | $0.00022 | $0.00192 |
Grade A, and why
gelatiere 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gelatiere
The gelatiere is a persistent scoop no cone owns (folder gelatiere, a read-only tab; the user cannot prompt it). Nightly, and after a chat ends, it reviews the archived sessions, the durable memory, the installed skills, the skill catalog at https://www.sliccy.com/skills/catalog.json and the man-page sitemap, and folds a handful of suggestions into /shared/.gelatiere/suggestions.json. The suggestions sprinkle (/shared/sprinkles/suggestions/suggestions.shtml) shows the open ones as a stream of cards. Each suggestion names the cones it is for (cones, folder names); a delivery licks only those, and installation-wide suggestions go to the primary cone. Feature flag Memory v2 under Settings → Experimental creates the unit at boot; gelatiere init does it by hand.
If you are a cone: gelatiere-suggestions arrived
{
"action": "gelatiere-suggestions",
"data": {
"added": 2,
"open": 3,
"suggestions": [
{
"id": "skill-github",
"kind": "skill",
"title": "…",
"body": "…",
"skill": "github",
"install": "upskill ai-ecoverse/skills --path skills/ --skill github",
"evidence": "You told the welcome wizard you live in GitHub.",
"cones": ["cone"]
},
{
"id": "use-case-fswatch-deploy",
"kind": "use-case",
"title": "…",
"body": "…",
"prompt": "…"
}
],
"path": "/shared/.gelatiere/suggestions.json",
"skill": "/workspace/skills/gelatiere/SKILL.md"
}
}
Reply with one short sentence — how many suggestions arrived and the gist of the best one — then post the stream so the user can act with a click:
The gelatiere left 2 new suggestions; the GitHub skill would have saved the PR detour from Tuesday.
Do not repeat the list in prose, do not install anything, and do not edit /shared/CLAUDE.md. Each card carries its own button — Install for a skill, Try it for a use-case, Draft it for a skill-idea, Report it for an issue — beside a quiet Dismiss.
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 Changed · +1 lines ad7788fe5480
- 1mo ago Changed · +7 lines · +17 tokens per session 90423f656a93
- 1mo ago First seen · 117 lines · 203 tokens per session scan A 166222ada2b5
gelatiere is a skill published in the GitHub repository ai-ecoverse/slicc (34 stars, last pushed yesterday), licensed Apache-2.0. It adds 220 tokens to every session and 1,918 once invoked, about $0.0009 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 2026-09-12.
Other skills, from other repositories
learnings
Use for repeated-looking troubleshooting, solved-issue recall, adding durable troubleshooting notes, and maintaining the local LEARNINGS archive. Reads LEARNINGS-SUMMARY.md first, follows referenced archive/source files before applying lessons, and verifies against current system evidence.
contextdb-autopilot
Automatic ContextDB session lifecycle — init, session events, checkpoints, and continuity artifacts. Use when you need AIOS context persistence without prompt injection. NOT for general task execution — only for ContextDB session management. TRIGGER: contextdb、session persist、checkpoint save.
memo
AIOS project memory — read/write/search your own memory using the memo capabilities. You (the model) decide what to remember, what to correct, and what was useful; the harness only executes your decision. Use when you want to recall prior work, persist a durable fact, supersede a wrong one, or mark a recalled memory…
aios-offload-recall
Use when recalling prior AIOS tool/browser outputs from offloaded refs; inspect Mermaid canvas first, then read only matching node-level evidence.
memory-search
Runs read-only SQL over the local EvoFlux session database (evoflux.db) for structured analysis that ordinary recall cannot answer, such as aggregating tool failures across sessions, filtering turns by role, agent, or time range, reconstructing a complete execution chain, or checking a remembered claim against what a…
map
Per-site navigation memory for tandem's shared browser. Profiles that describe a site's skeleton (routes), its durable locators, navigation recipes, and gotchas, so you navigate KNOWING instead of re-deriving the DOM each time. Use it when you start operating a site (check whether a profile already exists), when you…