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 kh6570/HAbedini-qspice-mcp --skill qspice-power-electronicsgit clone --depth 1 https://github.com/kh6570/HAbedini-qspice-mcpWrote 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/kh6570/habedini-qspice-mcp/qspice-power-electronics)<a href="https://agentmods.dev/skills/kh6570/habedini-qspice-mcp/qspice-power-electronics"><img src="https://agentmods.dev/badge/skills/kh6570/habedini-qspice-mcp/qspice-power-electronics/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/kh6570/habedini-qspice-mcp/qspice-power-electronics"><img src="https://agentmods.dev/badge/skills/kh6570/habedini-qspice-mcp/qspice-power-electronics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.01071 |
| Opus 5 | $0.00028 | $0.00535 |
| Sonnet 5 | $0.00011 | $0.00214 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
qspice-power-electronics 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QSpice: Power Electronics
A workflow for switching converters (buck, boost, SMPS): get a working transient, then characterize efficiency, transient response, and control-loop stability.
When to use
- Designing or analyzing a DC-DC converter or other switching power stage.
- You need efficiency, output ripple, load-step response, or loop-gain / phase-margin numbers — not just "does it run."
When NOT to use
- The run won't converge or stops early →
qspice-convergence-debuggingfirst. - Generic single-node waveform reads/metrics →
qspice-waveform-analysis.
Workflow
- Get a circuit.
materialize_reference_circuitfor a bundled starting point (e.g.buck_converter_cpp), or author your own.inspect_schematicto confirm topology;check_schematicfor a conservative ERC pass (floating nets, single-pin nets, missing ground) before spending sim time. - Transient first.
prepare_transientto stage a.trandirective long enough to reach steady state plus the events you care about, thenrun_simulation. - Characterize the output.
read_waveformon the switching node and output;measure_step_responsefor rise time / overshoot / settling on a load or reference step;measure_efficiencyfrom input/output power traces. - Assess the loop. For a switched (non-averaged) SMPS prefer
.bode:prepare_bode_analysison the settled circuit,run_simulation, thenmeasure_stability_margins. For small-signal/averaged models useprepare_loop_gain_analysis(Tian or Middlebrook injection) +prepare_ac. - Verify suspicious Bode points.
.bodeuses aggressive numerical methods and can carry artifacts (spectral leakage, aperture diffraction). Spot-check the crossover withprepare_meas(kind="fra", ...)— it stages.meas <name> fra <freq> <input> <output>, the most reliable frequency-domain measurement QSpice offers — then re-run andread_measures.
Closed-loop .bode details
- Injection point: the perturbing source goes between the SMPS output and the top of the feedback divider (low impedance driving high impedance).
- Settling first: run
prepare_transient+run_simulationto learn how long the supply takes to settle; pass that assettling_timetoprepare_bode_analysis. No settling acceleration is attempted by QSpice. - Reference node: if the feedback reference is not at AC ground, pass
reference_node(stages.options boderef=<node>alongside.bode). - Amplitude shaping: perturbation amplitude rises away from the geometric
mean of the sweep by default. Tune with
bode_amplitude_frequency,bode_low_power, andbode_high_power(.options bodeampfreq/bodelopow/bodehipow), or setbode_amplitude_frequency="0"for constant amplitude. - Low FSTART is expensive: each decade down multiplies simulation time.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 86 lines · 55 tokens per session scan A c6151f7f9138
qspice-power-electronics is a skill published in the GitHub repository kh6570/HAbedini-qspice-mcp (5 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 1,071 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 2026-08-31.
Other skills, from other repositories
neuroskill-bci
Use live BCI cognitive and mood state from NeuroSkill.
ruview-advanced-sensing
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection…
ruview-applications
Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…
pylabrobot
Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.