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 techygarg/lattice --skill review-refinergit clone --depth 1 https://github.com/techygarg/latticeWrote 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/techygarg/lattice/review-refiner)<a href="https://agentmods.dev/skills/techygarg/lattice/review-refiner"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/review-refiner/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/techygarg/lattice/review-refiner"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/review-refiner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00092 | $0.02876 |
| Opus 5 | $0.00046 | $0.01438 |
| Sonnet 5 | $0.00018 | $0.00575 |
| Haiku 4.5 | $0.00009 | $0.00288 |
Grade A, and why
review-refiner 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Refiner
What This Produces
- Output:
.lattice/standards/review-standards.md(or custom path from.lattice/config.yaml→paths.review_standards) - Two modes:
- Overlay (
mode: overlay): A slim document containing only sections that differ from the defaults. The review molecule reads its embedded defaults first, then applies this document's sections on top. This is the expected common case. - Override (
mode: override): A comprehensive standalone document that fully replaces the molecule's embedded defaults. For teams with fundamentally different review processes.
- Overlay (
- Default mode: Overlay -- produces only what the user wants to change
- Config key:
paths.review_standardsin.lattice/config.yaml - Consumed by: The review molecule (NOT an atom -- this is the first molecule-level config)
- Template: Read
./assets/template.mdfor the full document structure, default content, and interview guidance comments
Scope Clarification
This refiner configures the review process -- how the review molecule orchestrates atom output. It does NOT configure what atoms check for.
| Belongs here (process orchestration) | Belongs in atom refiners (quality standards) |
|---|---|
| Which atoms load and when | What checks an atom runs |
| Severity level definitions | What constitutes a violation |
| Report format and grouping | Checklist items and anti-patterns |
| Delta scope rules | Layer definitions, naming rules |
| Insight capture preferences | Domain modeling rules |
| Health log format | Security check thresholds |
| Custom review dimensions | Atom-specific validation logic |
If a user asks about changing what an atom checks for, redirect them to the appropriate atom refiner (architecture-refiner, clean-code-refiner, ddd-refiner).
Before You Begin
Check for existing documents
Before starting the interview, check whether a custom document already exists:
What ships with it
1 file 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.
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 · 232 lines · 92 tokens per session scan A ff438ff7ee3a
review-refiner is a skill published in the GitHub repository techygarg/lattice (186 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 2,876 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 2026-08-30.
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