SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill attention-variants-from-papersgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/attention-variants-from-papers)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/attention-variants-from-papers"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/attention-variants-from-papers/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/benchflow-ai/skillsbench/attention-variants-from-papers"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/attention-variants-from-papers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00532 |
| Opus 5 | $0.00020 | $0.00266 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
attention-variants-from-papers 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 13d 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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Attention Variants from Papers
Use this skill when a paper changes how attention scores, branches, normalization, or head sharing work, but the module still needs to behave like a drop-in transformer attention block.
Workflow
- Read the paper for invariants, not names. Use paper-to-implementation.md to extract the external contract, the changed computation, and the training-time constraints.
- Build a shape ledger before coding. Use shape-ledger.md to track projections, head grouping, branch count, and output width.
- Choose the mechanism pattern. Use mechanism-patterns.md for subtractive attention, branch mixing, learned gates, and extra normalization.
- Preserve the module boundary. Keep the same input and output shape, mask semantics, positional encoding flow, and cache behavior unless the task explicitly changes them.
- Validate in layers. Start with random-tensor smoke tests, then compare against a baseline attention path. Use stability-and-validation.md.
- Integrate into the stack last. Swap the new module into one transformer block, verify the residual path, then roll it through the full model. Use transformer-integration.md.
Checklist
- extract the paper's invariants before writing code
- account for every reshape, branch, and repeat in a shape ledger
- preserve output width at concatenation or output projection
- apply masks and positional terms at the intended stage
- confirm random smoke tests stay finite
- compare unchanged behaviors against a baseline attention implementation
Reference Map
- paper-to-implementation.md: turn paper text into module invariants and coding decisions
- shape-ledger.md: keep dimensions consistent while branch structure changes
- mechanism-patterns.md: reusable patterns for nonstandard score composition and mixing
- stability-and-validation.md: numerical checks, smoke tests, and baseline comparisons
- transformer-integration.md: wire the custom module into an existing transformer block
What ships with it
5 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.
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.
- 13d ago First seen · 41 lines · 41 tokens per session scan A d9b08a7127b6
attention-variants-from-papers is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 532 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 2026-08-30.
Other skills, from other repositories
rag-eval
NVIDIA RAG Blueprint evaluation guidance for measuring retrieval and answer quality with stable datasets, baselines, and reproducible scoring workflows.
local-llm-ops
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.
fixed-tensor-testing
Test ML functions with fixed input tensors for reproducibility.
dbscan-custom-metric
Run DBSCAN clustering with a custom distance metric using sklearn, including how to define weighted Euclidean metrics and extract cluster centroids.
parallel-grid-search
Parallelize hyperparameter grid search using joblib for efficient multi-core execution.
json-data-extraction
Extract, parse, and query JSON data from large enterprise files efficiently.