analyze-spectral-linearity

analyze-spectral-linearity is a skill for Claude Code, Codex from simra-tech/OpenADA. It costs 80 tokens per session (1,767 once invoked), scanned A, original, MIT.

A method for measuring frequency-domain signal behavior, including harmonics, unwanted tones and distortion. A spectrum shows how much of each frequency is present in a sampled signal.

In plain words
What is it for?
Use it to set up FFT measurements, check coherent sampling, measure harmonic distortion and spurious-free dynamic range, and compare linearity before and after a change.
Why use it?
It helps separate trustworthy measurements from misleading single-number results caused by sampling, windowing or aliasing problems.

Skill for Claude CodeCodex

Part of the openada plugin — 11 skills shipped together

Install

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.

agentmods
npx agentmods add skills/simra-tech/openada/analyze-spectral-linearity
Any agent
npx skills add simra-tech/OpenADA --skill analyze-spectral-linearity
Clone the repo
git clone --depth 1 https://github.com/simra-tech/OpenADA

Made for: Claude Code, Codex.

Or install openada, the plugin that ships this one along with the rest of its 11 skills.

Wrote 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.

agentmods badge for analyze-spectral-linearity

README.md
[![agentmods](https://agentmods.dev/badge/skills/simra-tech/openada/analyze-spectral-linearity.svg)](https://agentmods.dev/skills/simra-tech/openada/analyze-spectral-linearity)
Your own site
<a href="https://agentmods.dev/skills/simra-tech/openada/analyze-spectral-linearity"><img src="https://agentmods.dev/badge/skills/simra-tech/openada/analyze-spectral-linearity.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,767 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.01767
Opus 5 $0.00040 $0.00883
Sonnet 5 $0.00016 $0.00353
Haiku 4.5 $0.00008 $0.00177

Measured 4d ago against content hash 7bb8276a378f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyze-spectral-linearity 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 4d 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.

skills/analyze-spectral-linearity/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze Spectral Linearity

Treat each spectral metric as a simulation result derived from identified waveform evidence, not as a simulator side effect. Use $openada:openada for semantic operations and normalized results; keep the exact testbench and native waveform artifact authoritative.

Preserve the contract layers

  • Establish waveform evidence with openada.operation/circuit.simulate/v1alpha2 and openada.assertion/simulation.evidence.valid/v1alpha1.
  • Bind exact native vectors to a real sampled record with openada.operation/result.series.extract/v1alpha1 and openada.assertion/series.extraction.valid/v1alpha1.
  • Extract an implemented coherent single-tone ratio with openada.operation/result.spectral.measure/v1alpha1 and openada.assertion/spectral.measurement.valid/v1alpha1.
  • Use ordinary time-domain scalar algorithms through openada.operation/result.measure/v1alpha2 and openada.assertion/measurement.valid/v1alpha1.
  • Evaluate declared limits with openada.operation/specification.evaluate/v1alpha1 and openada.assertion/specification.satisfied/v1alpha1.

Inspect installed capability records and parameter schemas before forming a request. Use openada profile show openada.operation/result.spectral.measure/v1alpha1 for the packaged closed schema. The implemented spectral alpha covers SNR, SINAD, THD, and SFDR only for its exact coherent rectangular method. A different window, noncoherent record, sine fit, PSD, averaging, SNDR alias, ENOB, jitter, or phase-noise question remains not evaluated — capability unavailable. Never present an ad hoc script or backend expression as a result.spectral.measure result.

Read references/standards-and-methods.md when a metric is connected to an ADC, DAC, waveform recorder, pulse, jitter, or IEEE standard.

Freeze provenance and signal meaning

Record:

  • exact testbench, configuration, corner, temperature, mode, stimulus, clock, driver, native product/version, and run/result identity;
  • native waveform artifact identity and the requested transient interval;
  • source signal names and whether the analyzed signal is differential, common-mode, single-ended, or a declared mathematical combination;
  • fundamental or clock source and how its frequency was established;
  • requested metric, units, included/excluded content, and specification source.

Read the full file on GitHub · 165 lines

Files

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.

Changes

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.

  1. 4d ago First seen · 165 lines · 80 tokens per session scan A 7bb8276a378f

Subscribe to this mod's changes

analyze-spectral-linearity is a skill published in the GitHub repository simra-tech/OpenADA (24 stars, last pushed 22d ago), licensed MIT. It adds 80 tokens to every session and 1,767 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens

auditing-subgroup-fairness

Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairnessreport. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to…

maziyarpanahi/openmed · 148 tokens

mixed-precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

aiming-lab/AutoResearchClaw · 25 tokens

tooluniverse-drug-research

Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory…

mims-harvard/ToolUniverse · 71 tokens