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 LayerLens/stratix-python --skill layerlens_skillgit clone --depth 1 https://github.com/LayerLens/stratix-pythonWrote 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/layerlens/stratix-python/layerlens_skill)<a href="https://agentmods.dev/skills/layerlens/stratix-python/layerlens_skill"><img src="https://agentmods.dev/badge/skills/layerlens/stratix-python/layerlens_skill/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/layerlens/stratix-python/layerlens_skill"><img src="https://agentmods.dev/badge/skills/layerlens/stratix-python/layerlens_skill.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 | $0.00031 | $0.00609 |
| Opus 5.5 | $0.00012 | $0.00244 |
| Sonnet 5.5 | $0.00006 | $0.00122 |
| Haiku 4.5 | $0.00003 | $0.00061 |
Grade A, and why
layerlens 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 1mo 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LayerLens Skill for OpenClaw
This skill lets OpenClaw interact with the LayerLens AI evaluation platform. Use it to upload traces of agent executions, create quality judges, run evaluations, and retrieve scored results.
Prerequisites
Install the LayerLens Python SDK:
pip install layerlens --index-url https://sdk.layerlens.ai/package
Set your API key:
export LAYERLENS_STRATIX_API_KEY=your-api-key
What This Skill Does
When triggered, this skill:
- Uploads a trace -- captures the input (task) and output (agent response) as a LayerLens trace with metadata about the execution context.
- Creates a judge -- defines an evaluation rubric based on the requested quality dimension (safety, accuracy, helpfulness, etc.).
- Runs an evaluation -- scores the trace against the judge criteria.
- Returns results -- provides a pass/fail verdict, numeric score, and reasoning explanation.
Usage
Ask OpenClaw to evaluate an output:
Evaluate the last response for safety using LayerLens.
Run a quality check on this output: "The capital of France is Berlin."
Upload a trace of our conversation and score it for helpfulness.
Evaluation Script
The skill delegates to scripts/evaluate.py, which accepts input via stdin
or command-line arguments:
# Via arguments
python scripts/evaluate.py --input "What is 2+2?" --output "2+2 is 4." --goal "factual accuracy"
# Via stdin (JSON)
echo '{"input": "What is 2+2?", "output": "2+2 is 4.", "goal": "factual accuracy"}' | python scripts/evaluate.py
SDK Reference
The skill uses these LayerLens SDK methods:
client.traces.upload(path)-- upload a JSONL trace fileclient.judges.create(name=, evaluation_goal=)-- create an evaluation judgeclient.trace_evaluations.create(trace_id=, judge_id=)-- run an evaluationclient.trace_evaluations.get_results(evaluation_id)-- retrieve results
See the LayerLens Python SDK documentation for full API details.
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.
- 1mo ago First seen · 93 lines · 31 tokens per session scan A f552b7d4ba39
layerlens is a skill published in the GitHub repository LayerLens/stratix-python (100 stars, last pushed 14d ago), licensed Apache-2.0. It adds 31 tokens to every session and 609 once invoked, about $0.0001 per session on Opus 5.5. 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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