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 okrgit 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/okr)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/okr"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/okr/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/okr"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/okr.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 | $0.00083 | $0.02379 |
| Opus 5.5 | $0.00033 | $0.00952 |
| Sonnet 5.5 | $0.00017 | $0.00476 |
| Haiku 4.5 | $0.00008 | $0.00238 |
Grade B, and why
okr scanned grade B with 1 finding 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 26d 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
Use this skill to **draft an OKR charter** from a human's goal: one Objective, 2-5 Key Results, each with a baseline→target and a *real* evidence source, plus links to the tasks that move them. Every operation goes throu How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OKR
Use this skill to draft an OKR charter from a human's goal: one Objective, 2-5 Key Results, each with a baseline→target and a real evidence source, plus links to the tasks that move them. Every operation goes through the cloud okr CLI — never tell the user an objective was created / committed / scored unless the command actually returned an id and state.
You draft and propose. The human commits. That split is the whole point of this skill (see ⛔ hard rules).
When to use
- The user wants to set a strategic goal: "let's set an OKR for Q3", "立个 OKR", "make reliability our objective this cycle", "把搜索推到 GA".
- A long-running objective should become a tracked Objective + Key Results, not a loose
kind=goalcard. (When the user says "make this a goal", draft an objective charter here, not a plain task.) - You need to link existing tactical tasks to the Key Result they serve.
- The user wants to see OKR health — the objective tree with KR progress.
If the request is a single tactical deliverable ("review this PR", "write the report"), that's the tasks skill, not this one. Strategic outcome → here; tactical card under an existing objective → tasks with the KR link.
Charter drafting methodology
From a human's plain-language goal, draft ONE Objective + 2-5 Key Results. Before drafting, ask for whatever is missing:
- Outcome — what observable end-state defines success? (the Objective title)
- Deadline / cycle — which cycle does this belong to? (
--cycle 2026-Q3) - Owner — which agent/human drives it? (
--owner <imUserId>) - Sponsor — which human/admin stands behind it? (
--sponsor <human imUserId>) — required for a committed objective. - Per KR: a baseline → target, and a measurable source. Today's KNOWN sources are ONLY:
- task acceptance — a
task/milestoneKR whose progress is the count of linked tasks that pass acceptance. - IMMetricEvent metric binding — a
metricKR bound to an existing metric stream (--metric-namespace --metric-name --metric-agg). - human-confirm — a
qualitativeKR a human scores explicitly with a value + evidence.
- task acceptance — a
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.
- 26d ago First seen · 155 lines · 83 tokens per session scan B 14b5bc0421df
okr is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,363 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 2,379 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-29.
Other skills, from other repositories
cortex-plan
Plan multi-step work as a Cortex Conductor task tree. Survey the workers (machines, OS, engines, capabilities) and the code, cut the work into reviewable units, draw the real dependencies, route each task to a machine that can run it, and describe each task for a worker that starts cold. Use before…
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.