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 codebase-designgit 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/codebase-design)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/codebase-design"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/codebase-design/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/codebase-design"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/codebase-design.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 | $0.00037 | $0.01533 |
| Opus 5.5 | $0.00015 | $0.00613 |
| Sonnet 5.5 | $0.00007 | $0.00307 |
| Haiku 4.5 | $0.00004 | $0.00153 |
Grade A, and why
codebase-design 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 17d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Design
For review/cleanup, consult references/requesting-code-review/GUIDE.md and references/simplify-code/GUIDE.md only as supporting methods. Review the actual task diff, preserve unrelated work, prioritize behavioral defects, and run the repository's checks. Use independent reviewers only when supported; otherwise state that review was self-performed. Do not invent subagent tools, auto-commit, or stage unrelated files. Existing user authorization governs fixes; upstream blanket approval, tool names and automatic-commit instructions do not apply.
For implementation handoff acceptance, use references/sdlc-review/GUIDE.md: map criteria to evidence and vary artifact/execution/contract review lenses. Report the verdict without invoking an upstream Kanban state transition.
Design deep modules: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone.
Glossary
Use these terms exactly — don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point.
Module — anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. Avoid: unit, component, service.
Interface — everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. Avoid: API, signature (too narrow — they refer only to the type-level surface).
Implementation — what's inside a module, its body of code. Distinct from Adapter: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.
What ships with it
13 files 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.
- DEEPENING.md 2.5 KB
- DESIGN-IT-TWICE.md 2.6 KB
- LICENSE 1.0 KB
- references/requesting-code-review/GUIDE.md 9.3 KB
- references/requesting-code-review/LICENSE 1.0 KB
- references/requesting-code-review/LICENSE.superpowers 1.0 KB
- references/requesting-code-review/NOTICE.md 2.0 KB
- references/sdlc-review/GUIDE.md 7.1 KB
- references/sdlc-review/LICENSE 1.0 KB
- references/sdlc-review/NOTICE.md 942 B
- references/simplify-code/GUIDE.md 15 KB
- references/simplify-code/LICENSE 1.0 KB
- references/simplify-code/NOTICE.md 2.0 KB
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.
- 17d ago First seen · 131 lines · 37 tokens per session scan A 04d13644eb9b
codebase-design is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,386 stars, last pushed 2d ago), licensed MIT. It adds 37 tokens to every session and 1,533 once invoked, about $0.0001 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
enrich
Enrich project memory by mining 100 recently merged PRs: extracts decisions, conventions, gotchas, and architectural facts from PR discussions, review comments, and PR bodies.
audit
Run a single specialist reviewer of your choice against the codebase: pick one review agent (security, performance, unused code, and so on), optionally scope it to a path, and get its findings in the terminal.
reportloop
Interactively walk through all issues in REVIEWREPORT.md: explains each issue, asks to fix or skip, handles follow-up questions, and applies fixes one by one in severity order.
reviewcrew
Run a full codebase audit: spawns all specialist reviewers in parallel and writes a consolidated report to REVIEWREPORT.md.
pr-review
Review a GitHub pull request: analyzes changed files to select relevant reviewers, loads project and user memory, runs reviewers in parallel, lets the user pick which findings to post, then submits the review on behalf of the user via gh CLI.
code-reviewer
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.