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 unit-commitment-data-modelinggit 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/unit-commitment-data-modeling)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/unit-commitment-data-modeling"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/unit-commitment-data-modeling/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/unit-commitment-data-modeling"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/unit-commitment-data-modeling.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 | $0.00066 | $0.01516 |
| Opus 5 | $0.00033 | $0.00758 |
| Sonnet 5 | $0.00013 | $0.00303 |
| Haiku 4.5 | $0.00007 | $0.00152 |
Grade A, and why
unit-commitment-data-modeling 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- unit-commitment-data-modeling — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unit Commitment Structured Data Parsing
Use this skill when a unit commitment task provides structured data and you need to map fields into UC concepts. The source may be JSON, CSV, spreadsheets, database tables, or nested dictionaries. The prompt and schema are the source of truth; do not assume one benchmark or package.
Parsing Workflow
- Load data with structured parsers: JSON as objects, CSV/sheets as tables, databases as query results.
- Inspect schema: top-level keys, tables/sheets, resource groups, time-series fields, cost curves, startup tiers, and initial-condition fields.
- Identify the time axis: number of periods, labels, duration, and report convention.
- Identify resource sets: thermal, renewable, storage, imports, zones, reserve products, or network objects.
- Normalize fields into arrays/tables with explicit shapes.
- Preserve original names and source ordering for final reports.
- Run parser-level checks before modeling.
Map Concepts, Not Names
Different sources use different names. Map by meaning, units, shape, and context.
| UC concept | Look for |
|---|---|
| Horizon | periods, hours, timestamps, interval count |
| Demand | load, system demand, net load, zone load |
| Reserve requirement | spinning, operating, contingency, regulation reserve |
| Resource sets | thermal, renewable, storage, import/export |
| Commitment status | on/off, online, active, unit status |
| Output limits | minimum stable output, maximum output, availability |
| Ramping | ramp up/down, startup capability, shutdown capability |
| Minimum up/down | required duration after start/stop |
| Initial conditions | initial status, initial output, time already on/off |
| Must-run | forced online, fixed status |
| Startup data | fixed costs or tiers by prior offline duration |
| Production cost | linear coefficients, heat rate, piecewise or total-cost curves |
| Renewable availability | hourly min/max output or forecast bounds |
Common Data Shapes
- Scalar by resource: min up/down, ramp rates, startup ramp, must-run.
- Time series by system/zone: demand and reserve requirement.
- Time series by resource: renewable availability or outage status.
- Curve/tier tables: startup costs and production-cost breakpoints.
- Nested resource objects: generator-specific limits, status, and costs.
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 · 166 lines · 66 tokens per session scan A b67192fb7fcc
unit-commitment-data-modeling is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 1,516 once invoked, about $0.0003 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.
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