Proma is a local-first AI desktop application that combines multi-model chat, general-purpose agents, workspaces, skills, MCP servers, remote bots, and shared memory. People use it as a workspace for personal workflows, letting agents handle files, browser tasks, long-running jobs, and collaborative subtasks while storing sessions and configuration locally.
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 proma-ai/Proma --skill session-cleanergit clone --depth 1 https://github.com/proma-ai/PromaWrote 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/proma-ai/proma/session-cleaner)<a href="https://agentmods.dev/skills/proma-ai/proma/session-cleaner"><img src="https://agentmods.dev/badge/skills/proma-ai/proma/session-cleaner/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/proma-ai/proma/session-cleaner"><img src="https://agentmods.dev/badge/skills/proma-ai/proma/session-cleaner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00125 | $0.01441 |
| Opus 5.5 | $0.00050 | $0.00576 |
| Sonnet 5.5 | $0.00025 | $0.00288 |
| Haiku 4.5 | $0.00013 | $0.00144 |
Grade A, and why
session-cleaner 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
1 file 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 · 103 lines · 125 tokens per session scan A eb8610c432cd
session-cleaner is a skill published in the GitHub repository proma-ai/Proma (2,229 stars, last pushed 27d ago), licensed AGPL-3.0. It adds 125 tokens to every session and 1,441 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
lume自我进化
A set of rules for how Lume learns lasting preferences and keeps its workspace and conversation files organized. Lume is the application whose configuration and memory it manages.
self_awareness
Ground self-referential answers in the agent's actual filesystem. Use when the user asks who the agent is, what it can do, what it knows, what it remembers, what it has built, what services are connected, or what rules it follows.
forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
consolidate-memory
Reflective pass over your memory files — merge duplicates, fix stale facts, prune the index.
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
kb-research
Structured approach to finding and synthesizing information from the user's knowledge base.