Slash Command

/SKILL

Use this skill when tasked with replicating results from a research paper. Guides systematic reproduction of experiments, documentation of discrepancies, and validation of claims.

Install

npx claudepluginhub aviskaar/open-org --plugin code-replication

Prompt Preview

# Code Replication

Reproduce results from a research paper with methodical documentation.

## Replication Protocol

### Phase 1 — Paper Audit
Before writing any code:
- Extract every hyperparameter mentioned in the paper and appendix.
- Note any details that are ambiguous or missing.
- Check for an official codebase (GitHub, project page, supplementary materials).
- Identify the exact dataset split, preprocessing steps, and evaluation protocol.

Document findings in a `replication-notes.md` file.

### Phase 2 — Environment Setup
- Record exact package versions (use `pip freeze` or `conda e...

Command Content

Other plugins with /SKILL

/SKILL

security-compliance/

Guides implementation of defense-in-depth security architectures, compliance (SOC2, ISO27001, GDPR, HIPAA), threat modeling, risk assessments, SecOps, incident response, and SDLC security integration.

claude-forge

605

/SKILL

evaluating-llms-harness/

Evaluates LLMs on 60+ benchmarks (MMLU, HumanEval, GSM8K) using lm-eval harness. Provides CLI commands for HuggingFace/vLLM models, task lists, and evaluation checklists.

claude-forge

605

/SKILL

debugging-strategies/

Applies systematic debugging strategies to track down bugs, performance issues, and unexpected behavior using checklists, scientific method, and testing techniques.

claude-forge

605

/SKILL

summarize/

Summarizes content from URLs, local files, podcasts, and YouTube videos. Extracts transcripts with --extract-only flag. Supports AI models, lengths, and JSON output.

claude-forge

605

/SKILL

extract-errors/

Runs `yarn extract-errors` on React project to detect new error messages needing codes, reports them, and verifies existing codes are up to date.

claude-forge

605

/SKILL

dependency-upgrade/

Manages major dependency upgrades via compatibility analysis, staged rollouts with npm/yarn, and testing for frameworks like React.

claude-forge

605

Stats

Parent Repo Stars0

Parent Repo Forks0

Last CommitFeb 20, 2026

Actions

View Source View Plugin View on GitHub View README

Code Replication

Reproduce results from a research paper with methodical documentation.

Replication Protocol

Phase 1 — Paper Audit

Before writing any code:

Extract every hyperparameter mentioned in the paper and appendix.
Note any details that are ambiguous or missing.
Check for an official codebase (GitHub, project page, supplementary materials).
Identify the exact dataset split, preprocessing steps, and evaluation protocol.

Document findings in a replication-notes.md file.

Phase 2 — Environment Setup

Record exact package versions (use pip freeze or conda env export).
Prefer the same framework the paper used when possible.
Use a fixed random seed for all experiments. Document it.
Isolate the environment (virtualenv, conda, or container).

Phase 3 — Minimal Reproduction

Start with the smallest possible experiment:

Use the smallest dataset subset that exercises the full pipeline.
Confirm the training loop runs without errors before full-scale training.
Check intermediate outputs (loss curves, gradient norms) against any figures in the paper.

Phase 4 — Full Replication

Run the main experiment with the paper's reported settings.
Record all results in a structured log (seed, config, metric, timestamp).
Run at least 3 seeds; report mean ± std.

Phase 5 — Gap Analysis

Compare your results against the paper:

Metric	Paper	Reproduced	Δ
...	...	...	...

Categorize any gap:

Reproduced: within noise (< 0.5% relative difference)
Close: explainable by known ambiguities
Discrepancy: notable gap — document likely causes
Failed: results do not match — flag for investigation

Phase 6 — Documentation

Produce a REPLICATION.md with:

Paper citation
Environment details
Deviations from the paper (with justification)
Results table
Conclusion: successfully reproduced / partially reproduced / failed

Principles

Never alter evaluation code to match paper numbers — fix the model, not the metric.
Document every ambiguity you encountered and how you resolved it.
A failed replication is a valid and valuable research outcome.