Afm Force Curve Analyzer 1.0.0

Analyzes AFM force-distance curves and nanoindentation data to extract Young's modulus, adhesion, and deformation maps using multiple indentation models.

Security Scan

The skill's code and instructions are consistent with an AFM force-curve analysis tool and do not request credentials or network access, but there are small implementation/packaging inconsistencies and missing install steps you should be aware of before use.

Purpose & Capability

Name, description, SKILL.md, and analyze.py all focus on AFM force-distance / nanoindentation analysis (Sneddon/Hertz/JKR/DMT models, parsing vendor formats, generating plots and CSV/JSON/MD outputs). Required capabilities requested by the skill match its stated purpose; no unrelated credentials, binaries, or external services are requested.

Instruction Scope

SKILL.md instructs running a CLI (afm-force-curve-analyzer) to process local data files and produce local outputs; there are no instructions to read system config, fetch remote endpoints, or exfiltrate data. Note: SKILL.md triggers on several natural-language phrases (including non-English), which is expected for an invocation trigger.

Install Mechanism

This is instruction-only (no install spec). analyze.py is included but SKILL.md presents a CLI name (afm-force-curve-analyzer) that is not provided by an install step or wrapper — the skill does not include a packaged executable or installation instructions to expose that command. Also the code depends on scientific Python packages (numpy, pandas, scipy, lmfit, matplotlib) but the skill provides no mechanism to ensure those are present.

Credentials

The skill requests no environment variables, credentials, or config paths. The analyze.py file operates on user-supplied data files only. There is no code that reads unrelated environment variables or credential files.

Persistence & Privilege

always is false and disable-model-invocation is false (normal). The skill does not request persistent system privileges or modify other skills' configurations. No evidence of self-enabling or system-wide changes in the provided files.

Scan Findings in Context

[no_findings] expected: Static pre-scan reported no injection signals. This aligns with the code, which performs local numeric processing and file parsing and contains no obvious network calls or subprocess execution patterns in the visible portion.

Assessment

This skill appears to be an offline AFM force-curve analysis tool and does not request credentials or network access, which is good. Before installing or running it: 1) note that SKILL.md shows a CLI name (afm-force-curve-analyzer) but the package includes only analyze.py and no install wrapper — you may need to manually run analyze.py or create an entrypoint; 2) ensure the required Python scientific packages (numpy, pandas, scipy, lmfit, matplotlib) are installed in a controlled environment (virtualenv / conda), since the skill does not provide an installer; 3) inspect the full analyze.py file yourself (especially portions truncated in the bundle) for any reads of unexpected system paths or network I/O before running; 4) run the tool on non-sensitive sample data first to confirm behavior and outputs; 5) be aware the parsers use heuristic detection and may mis-parse some vendor formats (.nwi handling appears referenced in SKILL.md but a specific parser was not obvious), so verify parsed data for correctness. If you want higher assurance, ask the owner for an install script or a packaged release that declares dependencies and provides a proper CLI entrypoint.

SKILL.md — AFM Force Curve Analyzer

Tool Name

afm-force-curve-analyzer — AFM nanoindentation & force spectroscopy data analyzer

When to Use

When analyzing AFM force-distance curves, nanoindentation data, adhesion maps, or force spectroscopy measured during electrochemical cycling (LSV/CV/CP/CA). Triggers on phrases like:

• "AFM force curve分析"
• "force spectroscopy"
• "nanoindentation"
• "adhesion force"
• "Young's modulus AFM"
• "Sneddon模型"

Analysis Capabilities

• Force-distance (F-D) curve import & preprocessing (baseline subtraction, tip radius correction)
• Young's modulus extraction via Sneddon model (cone/pyramid/spherical indenters)
• Adhesion force (F_ad) & pull-off work calculation
• Multiple indentation models: Hertz, Sneddon (cone/pyramid), JKR, DMT
• Electrochemical potential correlation: modulus/adhesion vs. applied potential
• Deformation recovery analysis (creep/viscoelastic relaxation)
• Force map 2D visualization (adhesion, modulus, deformation)
• Multi-curve statistical comparison (fresh vs. cycled catalyst)
• Built-in reference database: NiOOH/γ-NiOOH/FeOOH/IrO₂/RuO₂/TiO₂/LiCoO₂/Si/Pt/Au

Input Formats

• CSV/XLSX ( Asylum MFP-3D / Bruker Nanoscope / JPK / NT-MDT / Keysight / Park )
• TXT (force curve single file, force map grid CSV)
• NWI (Nanonis)
• SXM (NT-MDT)

Output

• PNG multi-panel dashboard (F-D curves + statistics + potential correlation + 2D map)
• CSV with all extracted parameters
• JSON with metadata
• Markdown summary report

Key Calculations

• Sneddon model: E = (π/2) × F / (δ² × tan α) for conical indenter
• Adhesion work: W_ad = ∫F_ad dδ
• Reduced modulus: 1/E_r = (1−ν²)/E + (1−ν_tip²)/E_tip
• Standard indenters: ν_sample=0.3 (NiOOH), ν_tip=0.17 (SiN), E_tip=97 GPa (SiN)

Usage

afm-force-curve-analyzer --input data.csv --indenter conical --tip-radius 20e-9 \
  --poisson 0.3 --output ./results --format png,csv,json,md

Author

Labclaw 🦎 — built 2026-04-17