How do I install the Baseline Replication skill?

Run npx skillmds add majiayu000/baseline-replication-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Baseline Replication safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Baseline Replication?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Baseline Replication free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Baseline Replication?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Baseline Replication by majiayu000 — Agent Skill

Name: Baseline Replication
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

STANDARD OPERATING PROCEDURE

Purpose

Reproduce baseline experiments faithfully before new development.
Capture every constraint (data, metrics, hardware, randomness) and document variances.
Produce artifacts that downstream skills can trust (configs, logs, checkpoints).

Trigger Conditions

Positive: requests to replicate a paper’s baseline, re-run internal baselines, validate claims prior to extension.
Negative: greenfield method design (use method-development), or pure prompt tuning (route to prompt-architect).

Guardrails

Structure-first delivery: SKILL + README + examples + references; stash configs/resources alongside outputs.
Constraint extraction in HARD / SOFT / INFERRED buckets with sources (paper, repo, maintainer notes).
Two-pass refinement: (1) structure and environment parity; (2) epistemic validation and variance explanation.
Confidence ceilings enforced; never overstate beyond observation/research limits.

Inputs

Original specification (paper section, repo, config files).
Target hardware/software stack and allowed variance.
Success criteria (metric thresholds, tolerances, reproducibility bounds).

Workflow

Scope & Constraints: Capture dataset versions, metrics, seeds, hardware, and tolerances; confirm INFERRED assumptions.
Environment Parity Plan: Mirror dependencies; document any substitutions and expected impact.
Reproduce Runs: Execute baseline with fixed seeds; log configs, hashes, and environment fingerprints.
Validate: Compare against claimed results with statistical checks; explain deviations.
Package Artifacts: Store configs, logs, checkpoints, and a replication report; update examples/references as needed.

Validation & Quality Gates

Environment parity documented; deviations justified.
Metrics within agreed tolerance or deviation analysis provided.
Replication report includes evidence, seeds, and reproducibility notes.
Confidence line item included with appropriate ceiling (observation 0.95, research 0.85).

Response Template

**Constraints**
- HARD: ...
- SOFT: ...
- INFERRED (confirm): ...

**Plan**
- Environment + run strategy.

**Results**
- Metric(s): ... vs. claim ...
- Variance analysis: ...

**Artifacts**
- Configs/logs: <paths>
- Checkpoints: <paths>

Confidence: 0.82 (ceiling: research 0.85) - based on replicated runs and logged evidence.

Confidence: 0.82 (ceiling: research 0.85) - Assumes replication gates and evidence checks completed.

---name: baseline-replication-2description: Run disciplined replication of published or internal baselines with provenance, controls, and validation gates.--- ## STANDARD OPERATING PROCEDURE ### Purpose - Reproduce baseline experiments faithfully before new development. - Capture every constraint (data, metrics, hardware, randomness) and document variances. - Produce artifacts that downstream skills can trust (configs, logs, checkpoints). ### Trigger Conditions - **Positive:** requests to replicate a paper’s baseline, re-run internal baselines, validate claims prior to extension. - **Negative:** greenfield method design (use `method-development`), or pure prompt tuning (route to `prompt-architect`). ### Guardrails - Structure-first delivery: SKILL + README + examples + references; stash configs/resources alongside outputs. - Constraint extraction in HARD / SOFT / INFERRED buckets with sources (paper, repo, maintainer notes). - Two-pass refinement: (1) structure and environment parity; (2) epistemic validation and variance explanation. - Confidence ceilings enforced; never overstate beyond observation/research limits. ### Inputs - Original specification (paper section, repo, config files). - Target hardware/software stack and allowed variance. - Success criteria (metric thresholds, tolerances, reproducibility bounds). ### Workflow 1. **Scope & Constraints**: Capture dataset versions, metrics, seeds, hardware, and tolerances; confirm INFERRED assumptions. 2. **Environment Parity Plan**: Mirror dependencies; document any substitutions and expected impact. 3. **Reproduce Runs**: Execute baseline with fixed seeds; log configs, hashes, and environment fingerprints. 4. **Validate**: Compare against claimed results with statistical checks; explain deviations. 5. **Package Artifacts**: Store configs, logs, checkpoints, and a replication report; update examples/references as needed. ### Validation & Quality Gates - Environment parity documented; deviations justified. - Metrics within agreed tolerance or deviation analysis provided. - Replication report includes evidence, seeds, and reproducibility notes. - Confidence line item included with appropriate ceiling (observation 0.95, research 0.85). ### Response Template ``` **Constraints** - HARD: ... - SOFT: ... - INFERRED (confirm): ... **Plan** - Environment + run strategy. **Results** - Metric(s): ... vs. claim ... - Variance analysis: ... **Artifacts** - Configs/logs: <paths> - Checkpoints: <paths> Confidence: 0.82 (ceiling: research 0.85) - based on replicated runs and logged evidence. ``` Confidence: 0.82 (ceiling: research 0.85) - Assumes replication gates and evidence checks completed.

Frequently asked questions