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    Voltpeer plot

    1

    Automated battery data plotting and manuscript figure auditing with strict scientific visualization standards.

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    Voltpeer plot

    Example session with this skill installed

    I have a NEWARE export for my NMC811 cell. Inspect the headers in cell_data.csv and plot the capacity retention and Coulombic efficiency using the clear journal style.

    • Read your context and instructions
    • Compiled the voltpeer plot
    • Generated the data export

    I have inspected the NEWARE export and mapped the columns for NMC811 cycling. The data contains 150 cycles with stable discharge capacity. I have applied the journal_clear style preset, which uses open/filled markers for charge/discharge and consistent rectangular spines.

    Battery Performance Plot

    The figure has been generated with cycle-by-cycle Coulombic efficiency (CE) and discharge capacity panels.

    MetricStatusMapping
    Source Filecell_data.csvVerified
    Capacity BasismAh g⁻¹Active Mass Mapped
    Retention98.2%Ref: Cycle 1
    CE Average99.85%Verified
    Output Pathresults/NMC811_cycling.pdfRendered
    
# Provenance metadata extract
{
  "source_id": "NMC811-04",
  "style": "preset:journal_clear",
  "axes": ["capacity", "efficiency"],
  "units": {"capacity": "mAh/g", "voltage": "V"},
  "evidence_state": "verified"
}

    Next steps

    1. Review the PDF export in the results/ folder for axis tick readability at final column width.
    2. Verify the active mass value used for normalization in the generated .voltpeer/mapping.json.
    3. Provide the electrolyte volume and N/P ratio if you wish to include these in the automated caption.

    cell_data.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Battery research is stalled by messy instrument exports and inconsistent figure standards. This skill automates the ingestion of raw battery test data and renders publication-ready figures that adhere to strict scientific visualization rules.

    What it does

    • Standardizes exports by mapping LAND, NEWARE, and other cycler formats into verified CSV schemas without manual column editing.
    • Renders performance plots including cycle-by-cycle Coulombic efficiency, discharge capacity, and capacity retention with precise marker and line rules.
    • Enforces scientific consistency by validating full-cell baselines, normalization units (e.g., mAh g⁻¹), and temperature/rate conditions.
    • Generates vector outputs in PDF, SVG, and high-resolution TIFF formats optimized for journal submission requirements.
    • Audits visual claims by checking panel labels, scale bars, and coordinate spines against a specialized battery figure grammar.

    How it works

    1. Inspect and map using the inspect command to identify headers and units from raw instrument files.
    2. Configure metadata to record author-confirmed experiment conditions, sample IDs, and evidence states.
    3. Execute plotting via the shipped plot_uploaded.py program using pre-defined style presets.
    4. Verify output through a check stage that audits the rendered image for scientific anomalies or clipping.

    Frameworks & tools

    This skill utilizes a fixed Python plotting library (batteryplot), specialized parsers for battery cycler exports, and JSON-based metadata mappings. It manages assets through a local .voltpeer/ directory and generates standards-compliant vector graphics.

    Why this beats prompting it yourself

    Generic LLMs often hallucinate trend lines or fail to distinguish between capacity and retention baselines. This skill uses a fixed, tested program to ensure every point reflects the raw data exactly while enforcing domain-specific rules like separate markers for cycle data and solid lines for voltage profiles.

    Use cases

    • Converting raw 蓝电 (LAND) or NEWARE spreadsheet exports into publication-ready cycling figures.
    • Planning multi-panel manuscript figures with consistent legends and shared coordinate axes.
    • Auditing existing battery figures for missing scientific metadata like N/P ratios or electrolyte loading.
    • Generating standardized electrochemical spectra or dQ/dV traces with verified axis labels.

    Known limitations

    Native binary files (CEX/NDA) require prior vendor export to text/Excel. The skill does not perform model fitting, impedance equivalent circuit modeling, or independent experiment authentication.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Verified clean 8 days ago

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    Requires

    Github
    Python

    Listed8 days ago

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