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 spec-intakegit 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/spec-intake)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/spec-intake"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/spec-intake/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/spec-intake"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/spec-intake.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 | $0.00057 | $0.03169 |
| Opus 5.5 | $0.00023 | $0.01268 |
| Sonnet 5.5 | $0.00011 | $0.00634 |
| Haiku 4.5 | $0.00006 | $0.00317 |
Grade A, and why
spec-intake 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 19d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spec-intake
把一个原子 feature/gap 请求一次性做成可派发的 task：create + spec-set（SPEC 自动折进 dispatch 可见的 linkedAssetIds）+ acceptanceCriteria + provenance（apc/05 §1 C1 · S3）。核心纪律：SPEC 折进 dispatch 由服务端自动做，你不手动折；期望写进 description/SPEC，coding agent 才看得到。
什么时候用：一个原子级 feature/gap 要进研发链——需要建 task、写清 SPEC 与验收标准，让下游 coding agent 一 dispatch 就拿到完整上下文。
承重约束（先记死）
- 原子级 only：S3 是"一个 gap → 一个 task"。请求里裹了两个及以上独立 feature（epic）→ 拒绝，不许 silently 拆。epic 拆分/依赖排序不在本 skill，交人先拆。
- SPEC 折进 dispatch 是服务端自动的：
cloud task spec-set落 SPEC asset 后，服务端已把它折进metadata.assets.linkedAssetIds（task-spec.service.tsC1/S3）。你不要再cloud task meta set手动折 SPEC——那会重复且可能覆盖兄弟键。 - 期望进 description + SPEC：dispatch 只把 title/description +
linkedAssetIds喂给 coding agent。验收期望要么写进 description，要么写进 SPEC（会被折进 linkedAssetIds），否则 agent 看不到。 - provenance 用
--set-string：sha / id 用--set会被推断成 number（全数字 sha 精度毁）。写 sha 一律--set-string。 - metadata 写权限：
cloud task meta set走 gate = creator / orchestrator / admin / owner。spec-intake 是 intake 步、由主 agent 跑，主 agent 建的 task 天然是 creator——所以能写。assignee 不在内。
工具契约（签名以此为准，先核后用）
| 命令 | 作用 | 关键 flag |
|---|---|---|
cloud task create --title <t> |
建 task；期望写进 --description |
--title（必填）· --description · --kind work_item|goal（默认 work_item）· --assignee-id · --json |
cloud task spec-set <task-id> -m <md> |
写 SPEC.md（服务端自动折进 linkedAssetIds） | -m/--markdown 或 -f/--file（内容源） · --json |
cloud task add-criterion <task-id> --mode <m> --expectation <md> |
加一条验收标准 | --mode qualitative|quantitative|agent-self-check|manual（必填）· --expectation（必填）· --optional · --json |
cloud task acceptance <task-id> |
读回验收标准 + 汇总状态 | --json |
cloud task get <task-id> |
读回 task（含 metadata） | --json |
cloud task meta set <task-id> --set-string k=v |
写 provenance（非 SPEC 的 id / sha） | --set-string（值恒字符串） |
--kind合法值恰好两个：work_item（默认，可派发工作项）|goal（长期目标投影）。intake 用work_item。--mode合法值恰好四个：qualitative|quantitative|agent-self-check|manual。- 各命令
--json下 stdout 是结构化响应；成功退出码0，失败非零。
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.
- 19d ago First seen · 170 lines · 57 tokens per session scan A 9eb310706391
spec-intake is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,384 stars, last pushed 3d ago), licensed MIT. It adds 57 tokens to every session and 3,169 once invoked, about $0.0002 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 2026-09-29.
Other skills, from other repositories
req-create
Create structured requirement documents from user ideas. Use when capturing new features, tasks, or user stories.
repository-onboarding
Orient an agent without loading the entire repository.
grooming
Shape ambiguous work into an agreed, executable plan. Use when the user asks to groom, scope, decompose, or prepare work before implementation.
grooming-evidence
Ground work grooming in existing product evidence. Use while shaping tasks from code, documentation, tracker state, and user feedback.
planning-with-files
Organize complex work with file-based planning artifacts such as taskplan.md, findings.md, and progress.md. Use when the user needs a multi-step task, project, or research effort broken down and tracked across many tool calls.
V3 Swarm Coordination
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.