Lcf Strain Life — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Lcf Strain Life (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
An AI-agent-native toolkit for fatigue analysis of materials. It is a Python library plus an MCP server, so AI agents can run the whole analysis by calling tools.
Provide your own strain-controlled fatigue test data and get the standardized reduction, fitted material constants, life predictions, and plots. Results are reproducible and are saved for recall.
Why this exists: plenty of fatigue software exists, but none is built for AI agents to drive directly. The agent-native design over MCP is the point. Every capability is reachable through tools an agent can call.
Convention: all analysis uses true stress and true strain. Engineering input is converted at ingestion. The fatigue exponentsbandcare negative throughout.
| Stage | What happens |
|---|---|
| Ingest and normalize | raw time, strain, force plus parameters become true stress-strain |
| Cycle reduction | peak and valley per cycle, half-life cycle, cycles-to-failure N_f |
| Per-cycle metrics | stress amplitude, plastic strain amplitude, mean stress, T/C ratio, hysteresis energy |
| Strain-life fits | Basquin, Coffin-Manson, Ramberg-Osgood, transition life |
| Mean stress | Morrow, modified Morrow, SWT, Walker corrections |
| Save and recall | results persisted per test or material, recalled without recomputation |
The toolkit is general purpose and material agnostic. It focuses on strain-life and per-cycle evolution, which the established stress-based high-cycle libraries such as pyLife, py-fatigue, and fatpack do not cover. It is input compatible with their pandas data shapes.
python -m venv .venv
.venv\Scripts\activate # Windows
pip install -e ".[mcp,dev]"Requires Python 3.11 or newer.
import lcf
# fit strain-life constants from per-test reduced data, here SAE 1137
fit = lcf.fit_strain_life(
total_strain_amp=[0.009, 0.007, 0.005, 0.003, 0.002, 0.00175],
stress_amp=[553, 522, 464, 405, 350, 319], # MPa, half-life
reversals=[4234, 7398, 14768, 77104, 437498, 3327958],
E=208000, # MPa
min_plastic_strain=5e-4, # exclude near-runout points from the plastic branch
)
print(fit.coffin_manson.eps_f, fit.coffin_manson.c) # about 1.11, -0.62
print(fit.basquin.sigma_f, fit.basquin.b) # about 1073 MPa, -0.084
print(fit.transition_reversals) # about 22,000 reversalslcf-mcp # runs the stdio MCP server
# or
python -m lcfRegister with Claude Code or Claude Desktop over stdio:
{ "mcpServers": {
"lcf": { "command": "lcf-mcp" } } }The full documentation site is at https://dfieser.github.io/lcf-strain-life/, with the Scientific reference, Usage, Theory, and API reference as tabs.
mkdocs serve after pip install -e ".[docs]".This toolkit is built to be driven by AI agents over MCP.
src/lcf/ core library and MCP server
tests/ unit tests including golden-value validation, SAE 1137
docs/reference/ equations, physics, symbol tables
docs/design/ workflow and research-derived implementation reference
docs/decisions/ ADRs, the decision logMIT. See LICENSE.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.