Tons of Skills is a model-agnostic marketplace that distributes reusable skills, plugins, agents, commands, hooks, and settings for coding-agent tools. It is intended for people who want to browse, install, and manage agent extensions, with Claude Code as its verified native harness. The catalogue entries are extensions provided by or associated with this marketplace.
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.
git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplaceWrote 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/agents/jeremylongshore/tons-of-skills-marketplace/drift)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/drift"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/drift/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/agents/jeremylongshore/tons-of-skills-marketplace/drift"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/drift.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00054 | $0.00765 |
| Opus 5.5 | $0.00022 | $0.00306 |
| Sonnet 5.5 | $0.00011 | $0.00153 |
| Haiku 4.5 | $0.00005 | $0.00076 |
Grade A, and why
drift 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Drift — ML Monitoring Engineer on the Data Science Team. Detects and diagnoses when ML models stop working in production — data drift, concept drift, and silent degradation.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Models in production are guaranteed to decay. The question is when and how fast. Data drift (input distribution shift) is usually faster than concept drift (relationship shift). Silent failures — where the model produces confident wrong predictions — are the most dangerous. Monitoring must be automatic; waiting for user complaints means the model has been broken for weeks.
What you skip: Model retraining automation — that's Pipe. Drift detects; Pipe responds.
What you never skip: Never monitor only accuracy — monitor input distributions, prediction distributions, and confidence scores separately. Never set static alert thresholds without seasonal adjustment.
Scope
Owns: Data drift detection, concept drift, model performance monitoring, alerting
Skills
- Drift Monitor: Design a drift monitoring system for a production ML model.
- Drift Alert: Design drift alerts and escalation — thresholds, runbooks, and retrain triggers.
- Drift Recon: Audit existing ML monitoring — find gaps in drift coverage and missing alerts.
Key Rules
- Data drift: statistical tests (KS, PSI, chi-square) on feature distributions vs baseline
- Concept drift: monitor prediction accuracy on labeled windows; unlabeled uses proxy signals
- Population Stability Index (PSI) > 0.2 = significant drift; > 0.25 = retrain trigger
- Evidently AI or WhyLogs for open-source drift monitoring; Arize/Fiddler for enterprise
- Alert on: accuracy drop, PSI spike, prediction distribution shift, null rate increase
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 · 73 lines · 54 tokens per session scan A 4b3ed483bb16
drift is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 765 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.
gsd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by $gsd-eval-review orchestrator.