Borrowing it
Nothing to install: this file belongs to blackmo18/realm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/blackmo18/realm/main/.cursor/agents/realm-agent-planning.mdgit clone --depth 1 https://github.com/blackmo18/realmWrote 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/agents/blackmo18/realm/realm-agent-planning)<a href="https://agentmods.dev/agents/blackmo18/realm/realm-agent-planning"><img src="https://agentmods.dev/badge/agents/blackmo18/realm/realm-agent-planning/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/agents/blackmo18/realm/realm-agent-planning"><img src="https://agentmods.dev/badge/agents/blackmo18/realm/realm-agent-planning.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 | $0.00018 | $0.00088 |
| Opus 5.5 | $0.00007 | $0.00035 |
| Sonnet 5.5 | $0.00004 | $0.00018 |
| Haiku 4.5 | $0.00002 | $0.00009 |
Grade A, and why
realm-agent-planning 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.
What it actually says
Locate realm-planning/SKILL.md under .agents/skills or .cursor/skills, read it
completely, then follow its canonical two-phase procedure. Treat live code as ground
truth. Respect Cursor plan-mode
boundaries and do not modify implementation files while planning.
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 · 11 lines · 18 tokens per session scan A 484ba8872220
realm-agent-planning is an agent published in the GitHub repository blackmo18/realm (0 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 88 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 2026-10-02.
Other agents, from other repositories
context-finder
Read-only, memory- and index-aware codebase search. Use for any investigation — "where is X", "how does Y work", "what calls Z", "is W still used", "where is V configured", "does this event/pattern get emitted anywhere" — BEFORE reaching for grep. Consults the code index and prior session memory first and greps last…
wiki-ingest
Use this agent when ingesting URLs, files, or pasted text into the vault during automated maintenance cycles. Typical triggers include dev-loop IDLE DISCOVERY ingestion, batch source processing, or converting raw captures to typed-knowledge pages. See "When to invoke" in the agent body for worked scenarios.
memory-curator
Background monitoring agent that identifies memorable events during a session and suggests storing them with user confirmation. Use PROACTIVELY when autocapture is enabled and significant decisions, outcomes, or patterns emerge during a session.
data scientist
Ecosystem Data Scientist.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.