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Agent skill

Unit Commitment Data Modeling

by benchflow-ai in benchflow-ai/skillsbench

A skill your agent uses for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources…

Apache-2.0Auto-check passedDocuments & Office

Install Unit Commitment Data Modeling

skills CLI
$ npx skills add benchflow-ai/skillsbench --skill unit-commitment-data-modeling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install benchflow-ai/skillsbench unit-commitment-data-modeling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/energy-unit-commitment/environment/skills/unit-commitment-data-modeling .claude/skills/unit-commitment-data-modeling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
unit-commitment-data-modeling
GitHub stars
1.8k
Token cost
~1.7k tokens
SKILL.md length
616 words
Files
1
Skills in repo
180
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources…

  • Works in 7 steps: Load data with structured parsers: JSON… → Inspect schema: top-level keys,… → Identify the time axis: number of… → …
  • Parsing structured unit commitment input data from JSON
  • SKILL.md covers Parsing Workflow, Map Concepts, Not Names, Common Data Shapes and Time, Ordering, And Units, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Unit Commitment Data Modeling is an agent skill from benchflow-ai/skillsbench. Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering Database schema design, Excel spreadsheets and CSV and tabular files. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

  • Parsing structured unit commitment input data from JSON
  • Benchmark cases
  • Finding fields for time periods
  • Generator limits

Example prompts

  • “/unit-commitment-data-modeling”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Load data with structured parsers: JSON as objects, CSV/sheets as tables, databases as query results.
  2. Inspect schema: top-level keys, tables/sheets, resource groups, time-series fields, cost curves, startup tiers, and initial-condition…
  3. Identify the time axis: number of periods, labels, duration, and report convention.
  4. Identify resource sets: thermal, renewable, storage, imports, zones, reserve products, or network objects.
  5. Normalize fields into arrays/tables with explicit shapes.
  6. Preserve original names and source ordering for final reports.
  7. Run parser-level checks before modeling.

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Unit Commitment Data Modeling loads about 1.7k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 616 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 616 words, ~1,731 tokens.

Download SKILL.mdSave it as .claude/skills/unit-commitment-data-modeling/SKILL.md (or your agent's skills folder).
name
unit-commitment-data-modeling
description
Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

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

  1. Load data with structured parsers: JSON as objects, CSV/sheets as tables, databases as query results.
  2. Inspect schema: top-level keys, tables/sheets, resource groups, time-series fields, cost curves, startup tiers, and initial-condition fields.
  3. Identify the time axis: number of periods, labels, duration, and report convention.
  4. Identify resource sets: thermal, renewable, storage, imports, zones, reserve products, or network objects.
  5. Normalize fields into arrays/tables with explicit shapes.
  6. Preserve original names and source ordering for final reports.
  7. Run parser-level checks before modeling.

Map Concepts, Not Names

Different sources use different names. Map by meaning, units, shape, and context.

UC conceptLook for
Horizonperiods, hours, timestamps, interval count
Demandload, system demand, net load, zone load
Reserve requirementspinning, operating, contingency, regulation reserve
Resource setsthermal, renewable, storage, import/export
Commitment statuson/off, online, active, unit status
Output limitsminimum stable output, maximum output, availability
Rampingramp up/down, startup capability, shutdown capability
Minimum up/downrequired duration after start/stop
Initial conditionsinitial status, initial output, time already on/off
Must-runforced online, fixed status
Startup datafixed costs or tiers by prior offline duration
Production costlinear coefficients, heat rate, piecewise or total-cost curves
Renewable availabilityhourly 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.

Normalize into a small representation:

python
case = {
    "periods": periods,                  # length T
    "thermal_names": thermal_names,      # length G, source order
    "renewable_names": renewable_names,  # length R, source order
    "demand": demand,                    # shape (T,)
    "reserve_requirement": reserve,       # shape (T,)
    "thermal": thermal_params,
    "renewable_min": renewable_min,       # shape (R, T)
    "renewable_max": renewable_max,       # shape (R, T)
}

Time, Ordering, And Units

  • Use the input horizon as authoritative.
  • Preserve source period order.
  • Keep zero-based internal indexes separate from one-based/timestamped report labels.
  • Verify every time-series length equals T.
  • Preserve resource order unless the prompt requires sorting.
  • Treat resource IDs as opaque strings.
  • Keep thermal and renewable sets separate when constraints differ.
  • Check power units, period duration, ramp-rate units, and cost units before converting anything.

Basic checks:

python
assert len(demand) == T
assert len(reserve_requirement) == T
for r in renewable_resources:
    assert len(r["min"]) == T
    assert len(r["max"]) == T
Show full SKILL.md (243 more words)Show less

Production Convention

Many UC models use output above minimum internally, while reports often require actual MW.

python
actual_output = pmin * commitment + output_above_min
output_above_min = actual_output - pmin * commitment

Pick one internal convention and convert carefully for reporting, ramping, reserve deliverability, and cost.

Startup Tiers

Startup tiers are usually keyed by prior offline duration. Parse thresholds and costs without assuming order.

python
def choose_startup_tier(tiers, prior_offline_duration):
    tiers = sorted(tiers, key=lambda x: x["lag"])
    chosen = tiers[0]
    for tier in tiers:
        if tier["lag"] <= prior_offline_duration:
            chosen = tier
        else:
            break
    return chosen

Keep prior offline duration consistent with initial status and transition timing.

Cost Curves

Identify whether points are total cost, marginal cost, incremental segment cost, or heat-rate data. For total-cost breakpoints:

python
def interpolate_total_cost(points, output_mw):
    pts = sorted((float(p["mw"]), float(p["cost"])) for p in points)
    if output_mw <= pts[0][0]:
        return pts[0][1]
    if output_mw >= pts[-1][0]:
        return pts[-1][1]
    for (x0, y0), (x1, y1) in zip(pts, pts[1:]):
        if x0 <= output_mw <= x1:
            a = (output_mw - x0) / (x1 - x0)
            return y0 + a * (y1 - y0)
    raise ValueError("output outside cost curve")

If the first point is at minimum output, it may represent online minimum-output cost. Do not invent additional no-load or shutdown costs unless provided.

Renewables

  • Parse hourly minimum and maximum output.
  • If min equals max, output is fixed in that period.
  • If curtailment is allowed, output can be anywhere between min and max.
  • Do not count renewable headroom as spinning reserve unless explicitly allowed.
  • Renewable cost is zero unless the task/data says otherwise.

Parser-Level Validation

Before solving, check:

python
assert np.all(np.isfinite(demand))
assert np.all(np.isfinite(reserve_requirement))
assert np.all(thermal_pmin <= thermal_pmax)
assert np.all(renewable_min <= renewable_max)
assert all(len(curve) >= 2 for curve in production_curves.values())
assert all(len(tiers) >= 1 for tiers in startup_tiers.values())

Also check missing required fields, duplicate IDs, mismatched lengths, negative impossible limits, repeated cost points, nonmonotone startup lags, and inconsistent initial status/output.

Common Mistakes

  • Hard-coding a familiar schema instead of inspecting the data.
  • Losing ordering when converting dictionaries or tables into arrays.
  • Joining tables on the wrong key or duplicating resources.
  • Confusing total output with output above minimum.
  • Confusing reserve, capacity, availability, and dispatch.
  • Treating every cost curve as marginal cost.
  • Ignoring startup tier lags or initial offline duration.
  • Assuming renewable maximum output must always be used.

© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in tasks/energy-unit-commitment/environment/skills/unit-commitment-data-modeling of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Compare with similar skills

Unit Commitment Data Modeling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Unit Commitment Data Modeling compared with similar skills
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Unit Commitment Data Modeling this skillbenchflow-ai/skillsbench1.8k—~1.7kAutomated safety check: PassApache-2.0
Relational Database Designpproenca/dot-skills214—~2.5kAutomated safety check: PassMIT
Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences2742 repos~2.7kAutomated safety check: PassApache-2.0
Convert Fileduckdb/duckdb-skills5991 repos~720Automated safety check: NotesMIT
Research Integrity Auditxuzhougeng/wisp-science1k—~2.6kAutomated safety check: PassAGPL-3.0
Jmh Benchmark Compareeclipse-rdf4j/rdf4j420—~804Automated safety check: PassBSD-3-Clause

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Categories

Questions about Unit Commitment Data Modeling

What does Unit Commitment Data Modeling do?

A skill your agent uses for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources…. Unit Commitment Data Modeling is an agent skill from benchflow-ai/skillsbench. Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

When should I use Unit Commitment Data Modeling?

Unit Commitment Data Modeling fits situations like: parsing structured unit commitment input data from JSON; benchmark cases; finding fields for time periods; generator limits.

How do I install Unit Commitment Data Modeling in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill unit-commitment-data-modeling -a claude-code`. Or copy the skill folder (tasks/energy-unit-commitment/environment/skills/unit-commitment-data-modeling in benchflow-ai/skillsbench) into .claude/skills/unit-commitment-data-modeling in your project. Claude Code loads it when a task matches its description.

How do I install Unit Commitment Data Modeling in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill unit-commitment-data-modeling -a codex`. Or copy the skill folder (tasks/energy-unit-commitment/environment/skills/unit-commitment-data-modeling in benchflow-ai/skillsbench) into .agents/skills/unit-commitment-data-modeling in your project. Codex loads it when a task matches its description.

Can I use Unit Commitment Data Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill unit-commitment-data-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/unit-commitment-data-modeling, .gemini/skills/unit-commitment-data-modeling, .github/skills/unit-commitment-data-modeling and .opencode/skills/unit-commitment-data-modeling in your project.

What does Unit Commitment Data Modeling need to run?

SKILL.md names no scripts, command-line tools or credentials: Unit Commitment Data Modeling is instructions for the agent only. Our summary lists: Python 3.

Does Unit Commitment Data Modeling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Unit Commitment Data Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Unit Commitment Data Modeling use?

Unit Commitment Data Modeling is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Unit Commitment Data Modeling use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Unit Commitment Data Modeling?

Skills that share tags, products or a category with Unit Commitment Data Modeling: Relational Database Design (pproenca/dot-skills, 214 stars), Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Convert File (duckdb/duckdb-skills, 599 stars) and Research Integrity Audit (xuzhougeng/wisp-science, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Unit Commitment Data Modeling?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 180 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.