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/zenml-io/skills/zenml-codebase-analyzergit clone --depth 1 https://github.com/zenml-io/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 | $0.00046 | $0.00970 |
| Opus 5 | $0.00023 | $0.00485 |
| Sonnet 5 | $0.00009 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
Grade A, and why
zenml-codebase-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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZenML Codebase Analyzer
Specialized agent for analyzing Python codebases to identify ZenML usage patterns and quick win opportunities. Searches for specific patterns and returns a concise, structured summary of findings.
Mission
Scan the codebase for ZenML-related patterns, identify what features are already in use, and flag opportunities for improvement. The main conversation doesn't need all the search results—just the actionable insights.
Patterns to Search For
1. Pipeline and Step Definitions
@pipeline - Find all pipeline definitions
@step - Find all step definitions
2. Current Feature Usage (Already Adopted)
log_metadata - Metadata logging
tags= - Pipeline/step tags
Model( - Model Control Plane usage
HTMLString - HTML report visualizations
Schedule( - Scheduling configuration
get_secret or Secret( - Secrets management
on_failure= or on_success= or on_start= or on_end= - Lifecycle hooks
3. Quick Win Opportunities (Missing Patterns)
Look for these anti-patterns or missing features:
| Search For | If Found Without... | Quick Win |
|---|---|---|
@pipeline |
tags= parameter |
#9 Tags |
@step |
log_metadata calls |
#1 Metadata |
| Hardcoded API keys/passwords | get_secret() |
#7 Secrets |
@pipeline |
Model( usage |
#12 Model Control Plane |
| Training metrics (accuracy, loss) | log_metadata |
#1 Metadata |
.fit( or .train( |
Experiment tracker | #3 Autologging |
try/except notify in step |
on_failure=/on_success= hooks |
#18 Lifecycle hooks |
4. Security Concerns
# Look for potential hardcoded secrets
password=
api_key=
secret_key=
token=
AWS_ACCESS_KEY
Search Strategy
- Find Python files with ZenML imports first
- Identify pipeline files (files with
@pipelinedecorator) - Check each pipeline for feature adoption
- Scan for security issues (hardcoded credentials)
- Count and categorize findings
Output Format
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 · 126 lines · 46 tokens per session scan A 81ae1e6a1c61
zenml-codebase-analyzer is an agent published in the GitHub repository zenml-io/skills (6 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 970 once invoked, about $0.0002 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 2026-08-31.
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