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 codex-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/codex-memory-curator)<a href="https://agentmods.dev/skills/stark-ai-de/agent-skills/codex-memory-curator"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/codex-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/codex-memory-curator"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/codex-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 | $0.00087 | $0.03265 |
| Opus 5 | $0.00044 | $0.01632 |
| Sonnet 5 | $0.00017 | $0.00653 |
| Haiku 4.5 | $0.00009 | $0.00327 |
Grade A, and why
codex-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 9d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Memory Curator
Goal
Audit Codex memory as user-owned durable state; classify stale, unsafe, duplicated, or misplaced claims and route review, planning, persistence, and cleanup. Even when invoked from Cursor, inspect only Codex memory/config, not Cursor state.
Core principle
Memory is context, not truth. The latest user request, current repo files, AGENTS.md, package files, ADRs, and live evidence override stored memories.
When to use
- Use for review, placement, configuration, planning, or cleanup of Codex memory and its durable configuration.
- Use when memory is stale, conflicting, sensitive, noisy, cross-repository, or causing degraded behavior.
When not to use
- Do not use for ordinary docs, generic prompt work, or Cursor state unless Codex memory/config is explicitly involved.
- Keep review requests read-only and inspect no personal files beyond Codex memory/config plus the minimum repository evidence needed for conflicts.
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
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.
- agents/openai.yaml 526 B
- assets/cleanup-plan-template.md 1.9 KB
- assets/openai-icon.png 92 KB
- assets/review-report-template.md 2.1 KB
- references/classification-rubric.md 8.0 KB
- references/config-modes.md 6.1 KB
- references/conflict-resolution.md 1.6 KB
- references/example-review-report.md 3.9 KB
- references/memory-store-anatomy.md 2.2 KB
- references/safe-editing-procedure.md 5.1 KB
- scripts/backup-memories.mjs 39 KB runs code
- scripts/inventory-memories.mjs 4.6 KB runs code
- scripts/scan-memory-risks.mjs 8.6 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.
- 9d ago First seen · 194 lines · 87 tokens per session scan A e3291a1898bb
codex-memory-curator is a skill published in the GitHub repository stark-ai-de/agent-skills (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 87 tokens to every session and 3,265 once invoked, about $0.0004 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.