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 agentmods add agents/amd/skills/norm-analyzergit clone --depth 1 https://github.com/amd/skillsWhat 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 | $0.00000 | $0.01604 |
| Opus 5 | $0.00000 | $0.00802 |
| Sonnet 5 | $0.00000 | $0.00321 |
| Haiku 4.5 | $0.00000 | $0.00160 |
Grade A, and why
norm-analyzer 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 2d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: norm-analyzer description: Analyze normalization operations (BatchNorm, LayerNorm, GroupNorm, etc.) for memory bandwidth efficiency. Use when orchestrator needs norm category analysis. model: claude-opus-4-7-high
Normalization Analysis Subagent
Analyze normalization operations (BatchNorm, LayerNorm, GroupNorm, InstanceNorm) for memory-bandwidth efficiency. Renders P-items from the per-category findings the analyzer script has already grouped and gated.
Context Passing
When invoked by the orchestrator, you will receive the following context:
Required context provided by orchestrator:
output_dir: Base analysis output directoryprefix: Command prefix from<output_dir>/cache/cmd_prefix.txt— contains a template with{CMD}placeholder; substitute{CMD}with the actual commandcomparison_scope:standalone(default) orcomparativecat:norm_fwdornorm_bwd
Input files (pre-computed by orchestrator):
<output_dir>/category_data/<cat>_ops.csv- Filtered normalization operations (includescall_stackcolumn for architecture context)<output_dir>/metadata/<cat>_metadata.json- Hardware specs
Output file you must write:
<output_dir>/category_findings/<cat>_findings.md
Error Handling
If category data files are missing:
- Write a findings file noting: "No normalization operations found in trace"
- Return gracefully
If analysis script fails:
- Write a findings file with Status: ERROR
- CRITICAL: Do NOT manually analyze the raw CSV data
- CRITICAL: Do NOT provide any bottleneck findings
Language Guidelines
Use vendor-agnostic terminology:
- "GPU kernels" not "CUDA kernels"
- "native normalization kernels" not vendor-specific names
- Focus on operation semantics, not vendor implementation details
Analysis Workflow
Step 1: Run Analysis Script
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.
- 2d ago First seen · 165 lines · 0 tokens per session scan A 3f030eb55318
norm-analyzer is an agent published in the GitHub repository amd/skills (306 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,604 tokens. 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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