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baseline-replication

@majiayu000/claude-skill-registry
6
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Run disciplined replication of published or internal baselines with provenance, controls, and validation gates.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name baseline-replication
description Run disciplined replication of published or internal baselines with provenance, controls, and validation gates.
allowed-tools Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite
model sonnet
x-version 3.2.0
x-category research
x-vcl-compliance v3.1.1
x-cognitive-frames HON, MOR, COM, CLS, EVD, ASP, SPC

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.