Prismer Cloud is an infrastructure layer for AI agents that provides shared learning, compressed context, persistent memory, collaboration, messaging, tasks, identity, and workspaces. It is for agents and the people building or using long-running agent systems that need information and outcomes to persist across sessions. The catalogue entries provide skills, hooks, agents, instructions, and a plugin for using Prismer Cloud.
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 Prismer-AI/PrismerCloud --skill human-approvalgit clone --depth 1 https://github.com/Prismer-AI/PrismerCloudWrote 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/prismer-ai/prismercloud/human-approval)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/human-approval"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/human-approval/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/prismer-ai/prismercloud/human-approval"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/human-approval.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 | $0.00168 | $0.01839 |
| Opus 5 | $0.00084 | $0.00920 |
| Sonnet 5 | $0.00034 | $0.00368 |
| Haiku 4.5 | $0.00017 | $0.00184 |
Grade A, and why
human-approval 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 10d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human Approval
Some actions need a human in the loop before they execute: production deploys, large credit spend, deleting data, scope-expanding decisions. This skill submits a structured request and halts the current turn. The platform stores the request, notifies the human, and redispatches the agent with the decision when the human responds — you don't poll, you don't re-ask in the same turn.
When to use
- Production-impacting action: deploy, schema change, infra reconfig, payment send.
- Irreversible: delete files, drop tables, revoke keys, close accounts.
- Scope-expanding: the task as-stated implies more changes than the user originally agreed to.
- High credit cost: any operation that would spend > expected budget.
- Authority-elevating: granting access, changing roles, modifying ACLs.
Not when to use
- Routine clarifying questions ("what column name do you want?") — just ask in chat.
- Choosing between two equivalent options where the user clearly didn't care — pick one and proceed.
- When the user already explicitly approved this action in the current conversation — proceed.
Skill scope guard (v2.0.8)
The "Not when to use" list above is the load-bearing rule. As of release 2.0.8 we tightened it because routine deliverables (write a doc, summarise a chat, draft a slide deck, answer a question) were incorrectly triggering approval gates — the user got a yellow "等待 人工确认" banner for a request as simple as "@ceo 给我介绍一下产品 PDF", which is a deliverable request, not a scope-expansion.
The following 8 categories are never approval-eligible. Run them directly and report the result in the same turn:
| Category | Why it's not approval-eligible | Use instead |
|---|---|---|
| Writing a document / generating a report / outputting PDF, DOCX, PPTX, XLSX, CSV | The user asked for the deliverable; gating it is anti-UX. | Call office-artifacts and ship. |
| Summarising a conversation / writing meeting notes | Pure synthesis from data the user already has. | Reply in chat. |
| Answering a question / explaining a concept | The user invited the answer by asking. | Reply in chat. |
| Asking the user for a preference ("Chinese or English?") | A chat question is the correct affordance. | Ask in chat — human-approval is overkill. |
| Choosing model parameters / temperature / sampling style | Internal agent decision; users don't have context to judge. | Decide and proceed; mention the choice in the reply. |
| Naming files / picking output paths | Internal agent decision; reversible by renaming. | Pick sensible defaults; let user override if asked. |
| Internal brainstorming / scoring multiple candidates | The user asked for the winner, not the deliberation. | Do the work, surface the winner. |
| Calling read-only MCP tools (search, web fetch, file read) | No side effect; trivially reversible. | Call directly. |
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.
- 10d ago First seen · 115 lines · 0 tokens per session scan A 138df353e5a7
human-approval is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,496 stars, last pushed 1mo ago), licensed MIT. It adds 168 tokens to every session and 1,839 once invoked, about $0.0008 per session on Opus 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
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
agent-prompts-warmup
Audit and sync agent instruction files across all coding agent formats. FRE (first-run) checks scaffolding completeness; ongoing use keeps files in sync after edits.
docmancer
Work from the same local memory as every other coding agent on this machine. Recall prior decisions, preferences, instructions, and project conventions that Claude Code, Codex, Cursor, and other agents wrote here, with cited sources, fully local. Also searches a separate local technical-documentation index.
linksee-memory
The bridge to the agent's "past self". Before any new task, file edit, decision, or right after a failure, recall past caveats (pain records) / learnings (growth log) / implementation history from linksee-memory. This is the only way to solve Claude Code's "memory amnesia every session" problem. The "never repeat the…
setup-bot
Diagnose and fix Telegram bot connection issues -- verify config, test send, resolve common errors.