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 rl-post-traininggit 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/rl-post-training)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/rl-post-training"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/rl-post-training/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/rl-post-training"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/rl-post-training.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 | $0.00126 | $0.01411 |
| Opus 5 | $0.00063 | $0.00705 |
| Sonnet 5 | $0.00025 | $0.00282 |
| Haiku 4.5 | $0.00013 | $0.00141 |
Grade A, and why
rl-post-training 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 8d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RL Post-Training — Concepts & Diagnostic Guide
Core Concepts
RL post-training optimizes a language model's policy using reward signals. The standard pipeline:
prompt → generate completions → score with reward → compute advantages → policy gradient update
Each stage has distinct failure modes. When a model "shows no improvement," the bug could be anywhere in this pipeline.
Diagnostic Methodology
When RL training produces no improvement, work through these stages in order. Each stage depends on the previous one being correct.
Stage 1: Verify Reward Signal
- Are rewards non-constant? If all rewards are identical, there is no learning signal.
- Do rewards correlate with completion quality? Spot-check decoded completions against their scores.
- Is the reward function being called on the correct text? Check that decoding/stripping preserves the content the reward function needs to evaluate.
Stage 2: Verify Advantage Computation
- Are advantages non-zero when rewards vary? If they collapse to ~0, the policy gradient vanishes.
- Check the magnitude and dtype of every numerical-stability constant in the advantage path (additive epsilons, clipping bounds). Compare each to what the math requires.
- Check the group size
G.G ≤ 2makesstdeither undefined or extremely noisy.
Stage 3: Verify Log-Probability Computation
- Verify bounds: log-probs of valid tokens must be non-positive.
- Compare your implementation against
F.log_softmaxon a small deterministic input — a numerical match rules out sign errors, wrong gathering axis, and off-by-one subtraction. - On a near-one-hot input, confirm the dominant token's log-prob is close to 0 (not close to the min).
Stage 4: Verify Loss Computation
- Is the loss changing across steps? Flat loss suggests zero gradients upstream.
- Log the KL term and the policy-gradient term separately. Either dominating the other is diagnostic.
- Check the fraction of clipped samples. Near-100% clipping means the clip range is starving the signal.
What ships with it
3 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.
- 8d ago First seen · 89 lines · 126 tokens per session scan A 3ff7177b0200
rl-post-training is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 126 tokens to every session and 1,411 once invoked, about $0.0006 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 2026-09-03.
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.
dbscan-custom-metric
Run DBSCAN clustering with a custom distance metric using sklearn, including how to define weighted Euclidean metrics and extract cluster centroids.
json-data-extraction
Extract, parse, and query JSON data from large enterprise files efficiently.
fixed-tensor-testing
Test ML functions with fixed input tensors for reproducibility.
parallel-grid-search
Parallelize hyperparameter grid search using joblib for efficient multi-core execution.