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 stark-ai-de/agent-skills --skill cursor-memory-curatorgit clone --depth 1 https://github.com/stark-ai-de/agent-skillsWrote 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/stark-ai-de/agent-skills/cursor-memory-curator)<a href="https://agentmods.dev/skills/stark-ai-de/agent-skills/cursor-memory-curator"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/cursor-memory-curator/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/stark-ai-de/agent-skills/cursor-memory-curator"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/cursor-memory-curator.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 | $0.00099 | $0.03190 |
| Opus 5 | $0.00049 | $0.01595 |
| Sonnet 5 | $0.00020 | $0.00638 |
| Haiku 4.5 | $0.00010 | $0.00319 |
Grade A, and why
cursor-memory-curator 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor Memory Curator
Goal
Audit Cursor durable context as user-owned agent state: expose stale, unsafe, duplicated, ignored, conflicting, or misplaced rules; propose better destinations; and route review, planning, persistence, and cleanup through one explicit contract.
Keep the subject scoped to Cursor Project Rules, legacy .cursorrules, AGENTS.md, User Rules, Team Rules, and user-maintained Cursor memory-bank artifacts. Do not treat this as a Codex memory curator.
When to use
- Use for review, placement, planning, or cleanup of the Cursor durable surfaces named in the description.
- Use when those surfaces are stale, conflicting, sensitive, ignored, over-broad, or causing Cursor to forget or reuse old guidance.
When not to use
- Do not use for Codex memory, ordinary docs, or generic prompt work without Cursor durable context.
- Keep review requests read-only. Keep User/Team Rules manual unless a documented file-backed artifact or explicitly approved export path is in scope.
Workflow selection
Always expose these workflows in this order. plan-run-cleanup-file is always first and Recommended:
| Workflow | Delivery | Result |
|---|---|---|
plan-run-cleanup-file (Recommended) |
One redacted file record | Full review, user-approved cleanup plan, backup, execution, and verification. |
review-chat |
Chat only | Full read-only review and recommendations. |
review-file |
One redacted file record | Full read-only review and recommendations. |
cleanup-chat |
Chat plus backup | Full review followed by direct high-confidence atomic cleanup and verification. |
cleanup-file |
One redacted file record plus backup | Persist the review, then directly apply high-confidence atomic cleanup and verification. |
plan-cleanup-chat |
Chat only | Full review and user-approved cleanup plan; no cleanup. |
plan-cleanup-file |
One redacted file record | Full review and user-approved cleanup plan; no cleanup. |
plan-run-cleanup-chat |
Chat plus backup | Full review, user-approved cleanup plan, backup, execution, and verification. |
What ships with it
10 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.
- assets/cleanup-plan-template.md 1.9 KB
- assets/review-report-template.md 2.2 KB
- references/classification-rubric.md 4.6 KB
- references/conflict-resolution.md 1.7 KB
- references/context-surface-anatomy.md 2.6 KB
- references/example-review-report.md 2.3 KB
- references/safe-editing-procedure.md 5.1 KB
- scripts/backup-cursor-context.mjs 40 KB runs code
- scripts/inventory-cursor-context.mjs 6.9 KB runs code
- scripts/scan-cursor-context-risks.mjs 10 KB runs code
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 · 181 lines · 99 tokens per session scan A 0901e7601128
cursor-memory-curator is a skill published in the GitHub repository stark-ai-de/agent-skills (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 99 tokens to every session and 3,190 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
levelup-specify
Extract Context Directive Records (CDRs) from the current session after completing work. Identifies reusable patterns (rules, personas, examples, evals) and captures directive compliance cases for team-ai-directives.
change-publish
Promote accepted Change Decision Records (ChDRs) from drafts to project memory at .adlc/memory/chdr/, write OKF-style frontmatter, and regenerate the boot-facing .adlc/memory/chdr.md index that team-boot injects at session start. Use after /change-clarify has accepted ChDRs.
captain-recall
Use when the user asks WHY or WHAT-HAPPENED questions about this system — historical decisions, past incidents, why code/config is shaped a certain way, where something is deployed, who decided what, or the state of work in flight. Queries the tsubasa knowledge graph for cited answers.
captain-inject
Use when the user STATES A FACT about the system or tells the captain to remember/learn something — "update your knowledge", "remember this", "we dropped X for Y", "the outage was caused by Z", environment URLs, team contacts, deployment flows, tribal knowledge, corrections to what the Captain believes. Validates…
decision
Single entry point for all decision operations: capture, search, manage. Routes based on natural language intent.
capture-mcp-context
Record durable context from MCP servers and design or project tools into repository memory as proposed. Use when working with an MCP server like Figma, Linear, Jira, or Sentry, or after pulling design, ticket, or error context, to persist it for future sessions.