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 terrylica/cc-skills --skill mlflow-pythongit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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/terrylica/cc-skills/mlflow-python)<a href="https://agentmods.dev/skills/terrylica/cc-skills/mlflow-python"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/mlflow-python/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/terrylica/cc-skills/mlflow-python"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/mlflow-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 174 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 41 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 42 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Rogue Agent · line 188 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.01724 |
| Opus 5.5 | $0.00012 | $0.00690 |
| Sonnet 5.5 | $0.00006 | $0.00345 |
| Haiku 4.5 | $0.00003 | $0.00172 |
Grade A, and why
mlflow-python 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 14d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLflow Python Skill
Unified read/write MLflow operations via Python API with QuantStats integration for comprehensive trading metrics.
ADR: 2025-12-12-mlflow-python-skill
Note: This skill uses Pandas (MLflow API requires it). The
mlflow-pythonpath is auto-skipped by the Polars preference hook.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
When to Use This Skill
CAN Do:
- Log backtest metrics (Sharpe, max_drawdown, total_return, etc.)
- Log experiment parameters (strategy config, timeframes)
- Create and manage experiments
- Query runs with SQL-like filtering
- Calculate 70+ trading metrics via QuantStats
- Retrieve metric history (time-series data)
CANNOT Do:
- Direct database access to MLflow backend
- Artifact storage management (S3/GCS configuration)
- MLflow server administration
Prerequisites
Authentication Setup
MLflow uses separate environment variables for credentials (NOT embedded in URI):
# Option 1: .env.local + uv --env-file (recommended)
# Create .env.local (gitignored) in the skill directory with the lines below,
# then run scripts as: uv run --env-file .env.local scripts/<script>.py
MLFLOW_TRACKING_URI=http://mlflow.example.com:5000
MLFLOW_TRACKING_USERNAME=<user>
MLFLOW_TRACKING_PASSWORD=<password>
# Option 2: Direct environment variables
export MLFLOW_TRACKING_URI="http://mlflow.example.com:5000"
export MLFLOW_TRACKING_USERNAME="<user>"
export MLFLOW_TRACKING_PASSWORD="<password>"
Verify Connection
/usr/bin/env bash << 'SKILL_SCRIPT_EOF'
ROOT="$(cc-plugin-root devops-tools)"
cd "$ROOT/skills/mlflow-python"
uv run scripts/query_experiments.py experiments
SKILL_SCRIPT_EOF
Quick Start Workflows
A. Log Backtest Results (Primary Use Case)
/usr/bin/env bash << 'SKILL_SCRIPT_EOF_2'
ROOT="$(cc-plugin-root devops-tools)"
cd "$ROOT/skills/mlflow-python"
uv run scripts/log_backtest.py \
--experiment "crypto-backtests" \
--run-name "btc_momentum_v2" \
--returns path/to/returns.csv \
--params '{"strategy": "momentum", "timeframe": "1h"}'
SKILL_SCRIPT_EOF_2
What ships with it
9 files 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.
- references/authentication.md 2.0 KB
- references/evolution-log.md 683 B
- references/migration-from-cli.md 3.6 KB
- references/quantstats-metrics.md 5.6 KB
- references/query-patterns.md 3.9 KB
- scripts/create_experiment.py 3.1 KB runs code
- scripts/get_metric_history.py 3.8 KB runs code
- scripts/log_backtest.py 8.9 KB runs code
- scripts/query_experiments.py 6.5 KB runs code
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.
- 14d ago Changed 17e6b35623a4
- 27d ago Changed · -1 lines 51ea4a5cea4b
- 1mo ago First seen · 191 lines · 31 tokens per session scan A 01ef8687b7f8
mlflow-python is a skill published in the GitHub repository terrylica/cc-skills (0 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,724 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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