---
id: gh-ai-research-explore
name: "ai-research-explore"
url: https://skills.yangsir.net/skill/gh-ai-research-explore
author: lllllllama
domain: science
tags: ["deep-learning", "research-workflow", "experimentation", "reproducibility", "ai-research"]
install_count: 330800
rating: 4.90 (1 reviews)
github: https://github.com/lllllllama/rigorpilot-skills/tree/main/skills/ai-research-explore
---

# ai-research-explore

> 此技能旨在为深度学习研究提供严谨且可复现的探索流程。它帮助研究人员在既定任务、数据集和基准上，进行候选方案的探索，确保实验可审计、结果可比较，并生成高质量的探索性输出，从而加速有意义的创新研究。

**Stats**: 330,800 installs · 4.9/5 (1 reviews)

## Before / After 对比

### 提升深度学习研究探索效率与严谨性

**Before**:

在没有这个技能之前，研究人员在深度学习探索性研究中，常因缺乏系统性流程而面临实验设置不一致、结果难以比较、复现困难等痛点。手动追踪变体、文档记录和确保公平比较耗时耗力，严重拖慢了创新周期。

**After**:

此技能提供结构化的深度学习研究探索流程，确保实验可审计、结果可比较、输出受控。它显著减少了手动追踪和文档工作，让研究人员能更专注于想法和分析，在保持科学严谨性的同时，加速发现有意义的新研究候选。

| Metric | Before | After | Change |
|---|---|---|---|
| 确保实验可比性和可复现性的时间 | 8小时 | 1小时 | -88% |

## Readme

# ai-research-explore

## Purpose

Use this as the Rigor Explore compatible skill slug after the researcher
explicitly authorizes candidate-only work on top of a durable
`current_research` anchor. The installed slug remains `ai-research-explore` for
compatibility. Rigor Explore is for meaningful and potentially novel deep
learning research candidates while preserving scientific rigor, comparability,
reproducibility, and auditable collaboration. Novelty and significance remain
hypotheses before literature contrast, ablation evidence, and fair comparison.
The skill does not promise autonomous discovery, global benchmark completeness,
novelty proof, or trusted reproduction success.

Start from the shared operating principles in
`../../references/agent-operating-principles.md`, then load
`../../references/research-rigor-principles.md` for research claims and
`../../references/deep-learning-experiment-principles.md` when experiment
details affect comparability or reproducibility.

## Fit

Use this skill only when the request has both:

- Explicit exploration authorization such as candidate-only work, isolated
  branch or worktree, sweep, several variants, or exploratory ranking.
- A durable `current_research` context such as a branch, commit, checkpoint,
  run record, or already-trained local model state.

Keep narrow code-only requests on `explore-code`. Keep narrow run-only requests
on `explore-run`. Keep passive repository analysis on `analyze-project`. Keep
README-first reproduction on `ai-research-reproduction`.

## Research Rhythm

Use a two-loop rhythm:

- Outer loop: understand the repository, freeze task/dataset/evaluation/budget,
  preserve user ideas, map sources, gate ideas, and decide whether the next
  experiment is worth running.
- Inner loop: make one bounded candidate change or run, smoke-check it, collect
  evidence, rank it against the current anchor, and either stop or return to the
  outer loop with the new evidence.

This rhythm is a guide, not a rigid autonomous loop. Stop at explicit blockers,
unclear scientific meaning, exhausted budget, missing anchor/evaluation, or a
human checkpoint.

## Workflow

1. Confirm `current_research` and explicit explore-lane authorization.
2. Accept either legacy `variant_spec` or higher-level `research_campaign`.
3. In campaign mode, freeze the task, dataset, benchmark, evaluation source,
   SOTA reference, and budget before candidate work.
4. Build only the repo-understanding artifacts needed for the current campaign,
   usually through `analyze-project`.
5. Run bounded, cache-first source lookup when source support matters; prefer
   local curated literature such as Zotero if available, then seed sources,
   repo-local locators, public locators, or optional web lookup. Treat lookup as
   source resolution, not an open-ended literature search.
6. Preserve researcher-provided ideas, optionally add a small bounded set of
   single-variable seed ideas, and rank ideas with explicit gates and score
   breakdowns.
7. Prefer one clear candidate at a time. Use `explore-code` for bounded code
   adaptation and `explore-run` for short-cycle trials or sweeps.
8. Use `minimal-run-and-audit` or `run-train` only when the exploratory plan
   requires real execution evidence.
9. Write candidate-only outputs to `analysis_outputs/`, `sources/`, and
   `explore_outputs/` as appropriate; never present exploratory gains as trusted
   reproduction success. Include `SCIENTIFIC_CHANGELOG.md` and
   `COMPARABILITY_REPORT.md` for candidate scientific meaning and comparison
   boundaries.

## Ranking and Evidence

- Before execution, prioritize candidates by expected gain, cost, success
  likelihood, patch surface, dependency drag, evaluation risk, and rollback
  ease.
- After execution, rank by real evidence first: command status, observed
  metrics, artifacts, changed paths, smoke results, and reproducibility notes.
- Keep researcher-provided `evaluation_source` and `sota_reference` frozen for
  the campaign; do not claim they are globally complete.
- If the top ideas are too close or the implementation cannot be decomposed into
  auditable units, stop for a checkpoint instead of silently choosing.

## Campaign Inputs

`research_campaign` is preferred for Rigor Explore campaigns, but it should
stay minimal. The durable core is:

- `current_research`
- `task_family`
- `dataset`
- `benchmark`
- `evaluation_source`
- `sota_reference`
- `compute_budget`

Use `candidate_ideas`, `variant_spec`, `research_lookup`, `idea_policy`,
`idea_generation`, `source_constraints`, `feasibility_policy`, `baseline_gate`,
and `execution_policy` as optional guidance, not as fields the agent must fill
for every campaign. See `references/research-campaign-spec.md` for the advanced
schema and artifact expectations.

## Reference Loading

- Load `references/ai-research-explore-policy.md` for lane safety and candidate
  semantics.
- Load `references/research-campaign-spec.md` only when a campaign file is
  present or the user asks for Rigor Explore campaign governance.
- Load `../../references/explore-variant-spec.md` for run-level variant matrix
  details.
- Load `../../references/research-rigor-principles.md` before making novelty,
  contribution, SOTA, or comparability statements.
- Load `../../references/deep-learning-experiment-principles.md` when training,
  evaluation, baseline, ablation, metric, checkpoint, or dataset details matter.
- Use `scripts/orchestrate_explore.py` and `scripts/write_outputs.py` for the
  existing deterministic artifact workflow.



---
*Source: https://skills.yangsir.net/skill/gh-ai-research-explore*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-ai-research-explore/markdown*