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Ieee methods

Skill TenWalk/ieee-skills/ieee-skills-codex/skills/ieee-methods

Claude and Codex adapted IEEE communications manuscript skills for JSAC, TWC, TCOM, WCL, and CL.

Install
npx -y skills add TenWalk/ieee-skills --skill ieee-methods

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One thing to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Write or audit IEEE communications Methods/System Model/Algorithm details for reproducibility: notation tables, simulation-parameter tables, solver settings, random seeds, channel/model assumptions, algorithm boxes, convergence statements, and complexity analysis. Use this when the user asks for "methods", "reproducibility checklist", "notation table", "complexity analysis", "algorithm box", "simulation parameters table", "复现性", "符号表", "复杂度", or wants a JSAC/TWC/TCOM/WCL/CL methods section checked for enough detail to reproduce. For drafting the broader paper argument use ieee-writing; for deciding which experiments to run use ieee-experiments.

SKILL.md

4.0 KB, as published. Nobody here has run it

IEEE Methods and Reproducibility

Use this skill to turn a method description into a reproducible IEEE communications methods block. It sits between ieee-writing and ieee-experiments: writing explains the idea; this skill makes the implementation, notation, and complexity auditable.

Core stance

  • Reproducible beats impressive. A reviewer should be able to rebuild the main curve from the system model, parameter table, algorithm box, and solver/training details.
  • Define every symbol at first use. If a variable appears in an equation, it needs a domain, dimension, unit when relevant, and role.
  • Separate assumptions from simplifications. Assumptions define the studied regime; simplifications need a boundary or robustness check.
  • Complexity claims need variables. State big-O in terms of antennas/users/subcarriers/RIS elements/iterations, not a vague "low complexity".
  • No hidden implementation. Solver version, stopping tolerance, Monte-Carlo realization count, train/test split, and randomization policy are methods content, not footnotes.

When to open extra files

FileOpen when
references/reproducibility-checklist.mdAuditing or writing system/channel setup, simulation-parameter tables, randomization, solvers, training data, hardware/testbed/ray-tracing settings, or reproducibility statements
references/notation-and-complexity.mdBuilding notation tables, algorithm boxes, convergence statements, or big-O complexity analysis

Use ieee-writing for full Section II/III prose, ieee-experiments for benchmark/metric design, ieee-figure for result plots, and ieee-latex for IEEEtran formatting and compilation.

Workflow

  1. Identify the method object: system model, problem formulation, algorithm, analysis, neural network, simulation protocol, or testbed/ray-tracing setup.
  2. Extract the reproducibility variables: topology, channel, signal model, optimization variables, metrics, solver/training setup, and random quantities.
  3. Build a notation table for nontrivial papers: symbol, size/domain, meaning, first equation.
  4. Write the method in signal flow order: input -> transformation/channel/optimization -> output -> metric. Avoid listing modules without explaining information flow.
  5. Add a parameter table for experiments and shared settings. Put repeated constants there, not scattered through prose.
  6. For algorithms, add an algorithm box with initialization, loop, stopping criterion, and returned variables. Label exact, approximate, and learned steps.
  7. State convergence and complexity honestly: KKT/stationary/global only when proved; complexity per iteration plus total iteration count when applicable.
  8. Flag gaps that prevent reproduction rather than filling them by guess.

Output format

  1. Methods text: polished IEEE prose or LaTeX for the requested method block.
  2. Reproducibility checklist: Item -> Present / missing / needs confirmation.
  3. Notation and complexity: notation table and complexity statement when relevant.
  4. Assumptions and boundaries: CSI/channel/scope simplifications that need support.
  5. Missing inputs: exact numbers, solver settings, data splits, or hardware details the author must provide.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.