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 skill-reviewgit 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/skill-review)<a href="https://agentmods.dev/skills/techygarg/lattice/skill-review"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/skill-review.svg" alt="Measured on agentmods" 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 | $0.00180 | $0.02473 |
| Opus 5 | $0.00090 | $0.01236 |
| Sonnet 5 | $0.00036 | $0.00495 |
| Haiku 4.5 | $0.00018 | $0.00247 |
Grade A, and why
skill-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 8d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Review
Core responsibility: Find real behavioral gaps in a Lattice skill by reviewing it through three independent personas. Each persona sees the skill with different eyes, cares about different things, and may surface different gaps. The combined findings should be more practical and complete than any single review.
Input: One skill path or skill name.
Output: A unified findings report — only the high-confidence, practical gaps from all three personas, merged, deduplicated, pruned, and ordered by severity — with proposed fixes.
Review standard: Prefer omission over speculation. Only report findings you are highly confident would surface in normal use, belong to this skill's responsibility, and would materially improve outcomes if fixed. A valid review may conclude that no material practical gaps remain.
How to verify this skill did its job:
- Every reported finding is grounded in a realistic scenario and tied to specific evidence in the skill
- Zero findings is acceptable if no high-confidence practical gaps remain
- Every gap has a specific proposed fix, not just a flag
- Overlapping findings from multiple personas are merged into one entry with a note that multiple perspectives agree
- Multiple personas agreeing raises confidence only after the finding survives the practicality filter
- The final report is ordered: critical gaps first, warnings second, observations last — or explicitly says no material practical gaps were found
- After fixes are applied, a second run of this skill on the same skill file shows no high-confidence practical gaps
Step 1: Read the skill
Read the full SKILL.md and all referenced files (defaults.md, template.md, references/).
Form a clear understanding of:
- What the skill claims to do and who uses it
- What it produces (documents, reports, code, changes)
- What its inputs are and what states they can be in
- Where it sits in the Lattice pipeline (upstream / downstream connections)
If the skill is composed by molecules, consumes refiner output, or depends on other skills, read the relevant upstream/downstream files too. Review against actual runtime usage, not an imagined standalone use case.
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.
- 8d ago First seen · 213 lines · 180 tokens per session scan A 23c5efb063a4
skill-review is a skill published in the GitHub repository techygarg/lattice (185 stars, last pushed yesterday), licensed MIT. It adds 180 tokens to every session and 2,473 once invoked, about $0.0009 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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