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 timurgaleev/memex --skill cross-modal-reviewgit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/cross-modal-review)<a href="https://agentmods.dev/skills/timurgaleev/memex/cross-modal-review"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/cross-modal-review/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/timurgaleev/memex/cross-modal-review"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/cross-modal-review.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 | $0.00074 | $0.01561 |
| Opus 5 | $0.00037 | $0.00781 |
| Sonnet 5 | $0.00015 | $0.00312 |
| Haiku 4.5 | $0.00007 | $0.00156 |
Grade A, and why
cross-modal-review 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.
This is a copy
86% identical to cross-modal-review — 76 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Modal Review
Convention: see conventions/cross-modal.yaml (via
get_skill conventions/cross-modal) for the review pairs and refusal routing chain.
Relationship to
memex eval: This skill is the manual mid-flow gate (one model reviews work product before commit, with refusal routing). Thememex evalCLI is a sibling surface: it scores retrieval and skill behavior against documented fixtures before tests cement behavior. Use this skill for ad-hoc second opinions; usememex evalfor the skillify quality gate. The two are complementary, not redundant.
Contract
This skill guarantees:
- Work product is reviewed by a different model before finalizing.
- The review is graded against the originating skill's Contract section (what was promised), not vibes.
- Agreement and disagreement are reported transparently.
- Refusal from one model triggers a silent switch to the next in chain.
- The user always makes the final decision (user sovereignty).
When to invoke (v1.1 gating)
Invoke this skill when:
- Significant code changes — any commit touching 5+ files or 100+ lines. Architecture decisions, refactors, API changes.
- Security-sensitive changes — auth flows, brain-write trust boundaries, webhook transforms, cross-skill data passing.
- Stuck or churning — 2+ iterations on the same problem without progress.
- Pre-bulk-operation — before running batch enrichment, migrations,
or bulk writes (see conventions/test-before-bulk.md via
get_skill conventions/test-before-bulk). - Skill creation / modification — new or rewritten skills that affect operational behavior.
- Brain-page quality concerns — when brain writes need validation against the originating skill's Contract.
Do NOT invoke for:
- Simple memory writes or brain-page updates
- Single-file typo fixes
- Routine scheduled-job output or heartbeat operations
- Git commit / push of already-reviewed work
Phases
- Capture the work product. The brain page, analysis, code diff, or decision to be reviewed.
- Load the Contract. Read the originating skill's Contract section
(what was promised) —
get_skillreturns it. - Spawn review model. Send the work + Contract to a different
model. Use conventions/model-routing.md (via
get_skill conventions/model-routing) for model selection — for brain-side passes that means the Bedrock tiers (Haiku = utility, Sonnet = synthesis); for a genuinely different model family, use a reviewer from the agent harness's own tooling. - Grade. Model evaluates: did the output follow the Contract? Pass / fail with specific citations.
- Report. Present agreement / disagreement to the user. Never auto-apply the reviewer's suggestions.
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 · 202 lines · 74 tokens per session scan A c8b52c4663c6
cross-modal-review is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,561 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to cross-modal-review, differing in 76 lines, and is treated as a copy.
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