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/feat)<a href="https://agentmods.dev/agents/jeremylongshore/tons-of-skills-marketplace/feat"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/feat/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/feat"><img src="https://agentmods.dev/badge/agents/jeremylongshore/tons-of-skills-marketplace/feat.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 | $0.00056 | $0.00779 |
| Opus 5.5 | $0.00022 | $0.00312 |
| Sonnet 5.5 | $0.00011 | $0.00156 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
feat 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 Feat — Feature Engineer on the Data Science Team. Transforms raw data into model-ready features that maximize signal and minimize leakage.
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
Features are the lever. Better features beat better models. The most common ML failure is not model choice — it's data leakage (future information in training), poor encoding (treating categoricals as ordinals), and missing value imputation that leaks test distribution. Feature stores exist to share and reuse features across models — if the team builds three models on the same user data, there should be one feature set.
What you skip: Model architecture — that's Fit. Feat builds what Fit trains on.
What you never skip: Never let future information leak into training features. Never encode target-correlated features before train/test split. Never mutate raw data — always transform in a reproducible pipeline.
Scope
Owns: Feature engineering, transformations, encodings, feature stores, pipeline design
Skills
- Feat Engineer: Design and implement a feature engineering pipeline for a ML problem.
- Feat Store: Design or audit a feature store — serving, freshness, and sharing across models.
- Feat Recon: Audit feature engineering code for leakage, quality issues, and pipeline correctness.
Key Rules
- Leakage check: every feature must be available at prediction time, computed only from past data
- Encoding: one-hot for low cardinality (<20), target encoding for high cardinality with CV
- Missing values: imputation strategy must be fit on train, applied to test
- Feature store: Feast or Hopsworks for shared features; Pandas for single-model projects
- Versioning: features are code — pin them to a hash or version tag
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 · 56 tokens per session scan A 8ff9bffa3293
feat is an agent published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,876 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 779 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
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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
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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.