Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vibeic/vibe-icnpx agentmods add skills/vibeic/vibe-ic/analog-hw-tuning-loopWrote 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/vibeic/vibe-ic/analog-hw-tuning-loop)<a href="https://agentmods.dev/skills/vibeic/vibe-ic/analog-hw-tuning-loop"><img src="https://agentmods.dev/badge/skills/vibeic/vibe-ic/analog-hw-tuning-loop/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/vibeic/vibe-ic/analog-hw-tuning-loop"><img src="https://agentmods.dev/badge/skills/vibeic/vibe-ic/analog-hw-tuning-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.01786 |
| Opus 5 | $0.00030 | $0.00893 |
| Sonnet 5 | $0.00012 | $0.00357 |
| Haiku 4.5 | $0.00006 | $0.00179 |
Grade A, and why
analog-hw-tuning-loop 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 11d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analog HW Tuning Loop
The master orchestrator for hardware-verified analog convergence. First converges in SPICE simulation, then validates on real hardware, iterating until all three sources agree: hardware measurement, SPICE prediction, and design spec.
When to use
- Step A9 of the analog track (hardware verification)
- When the user says "tune the LDO on hardware", "verify analog on bench"
- After
analog-sizing-loophas converged in simulation
Inputs
analog/<block>/spec.json— target specsanalog/<block>/sizing_final.json— SPICE-converged sizinganalog/<block>/corner_results.json— SPICE simulation results- Hardware setup: FPGA board, scope, breadboard/PCB
Two-phase convergence
Phase 1: SPICE convergence (delegates to analog-sizing-loop)
Run the simulation-only loop until all PVT corners pass. This phase is fully automatic with no hardware needed.
Phase 2: Hardware verification (max 3 iterations)
For each iteration:
1. analog-hw-testbench-gen → generate FPGA stimulus RTL
2. eda_fpga_compile → eda_fpga_program → program DE10-Lite
3. Prompt user: "Confirm the breadboard circuit has been built"
- Display component BOM derived from sizing_final.json
- Show wiring diagram from hw_test/README.md
4. analog-hw-measure → scope capture + ADC readings
5. Three-way comparison + verdict — enforced by `programs/analog_hil_three_way_verdict.py`
6. Iterate per the verdict; single-knob + iteration-cap discipline enforced by program (see below)
Three-way comparison logic (enforced by program)
The four-row decision table (SPICE-vs-Spec, HW-vs-Spec, HW-vs-SPICE-discrepancy)
→ {CONVERGED | CONVERGED_WARNING | MODEL_INACCURACY | BACK_TO_PHASE1} is a pure
lookup — enforced by programs/analog_hil_three_way_verdict.py (PASS on a
CONVERGED variant, FAIL on MODEL_INACCURACY / BACK_TO_PHASE1, SKIP on no data).
| SPICE vs Spec | HW vs Spec | HW vs SPICE | Verdict |
|---|---|---|---|
| PASS | PASS | <20% | CONVERGED — ideal |
| PASS | PASS | >=20% | CONVERGED_WARNING (model-accuracy) |
| PASS | FAIL | — | MODEL_INACCURACY — add margin, re-sim + re-measure |
| FAIL | — | — | BACK_TO_PHASE1 — should not reach Phase 2 |
What ships with it
2 files 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.
- 11d ago First seen · 146 lines · 60 tokens per session scan A 13a084d73b7e
analog-hw-tuning-loop is a skill published in the GitHub repository vibeic/vibe-ic (23 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 1,786 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 2026-08-30.
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