Agent skill
research-ideation
Guides research ideation through a 5-step goal-driven workflow: define long-term goal, build literature tree (novelty + challenge-insight), select a problem (well-established solution check), design a solution (cross-domain transfer + decomposition), validate and iterate. Also covers structured paper reading (3 depth levels). Use when: user wants to find a research direction, brainstorm ideas, build field vision, do a literature review, evaluate idea novelty, or read papers systematically. Do NOT use for comparing/ranking existing ideas (use idea-tournament) or planning a paper (use paper-planning).
Install this agent skill to your Project
npx add-skill https://github.com/EvoScientist/EvoSkills/tree/main/skills/research-ideation
Metadata
Additional technical details for this skill
- tags
-
core research ideation literature
- author
- EvoScientist
- version
- 1.0.0
SKILL.md
Research Ideation
A goal-driven workflow for finding important research problems, designing novel solutions, and building deep field understanding through structured paper reading.
When to Use This Skill
- User wants to find a research direction or brainstorm research ideas
- User needs to do a literature review or map a research field
- User asks about evaluating whether an idea is novel or worth pursuing
- User wants to read papers more effectively or build a systematic reading habit
- User mentions "research ideation", "find a problem", "literature tree", "novelty check", "paper reading"
Goal-Driven Research Workflow
Follow these five steps in order. Each step builds on the previous one.
Step 1: Define a Long-Term Research Goal
Start with a goal that has both scientific and practical value. The goal should be ambitious enough to sustain multiple papers, but concrete enough to guide daily decisions.
Ask: "What is the ultimate form of this research direction? What would the world look like if this problem were fully solved?"
Step 2: Build a Literature Tree
Map the field by constructing two complementary trees:
- Novelty tree: Classify existing work by milestone tasks, representative pipelines, and novel modules. This reveals WHERE the field has gaps.
- Challenge-insight tree: Collect technical challenges and the insights/techniques that address them. This reveals WHICH problems lack good solutions.
See references/literature-tree.md for the full construction method and four types of novelty.
Step 3: Select a Problem
Find tasks with genuine research space. The key question: "Is this problem worth solving, or has a well-established solution already claimed this territory?"
Use the well-established solution check (4 levels) to decide whether to proceed or switch problems. Actively seek new failure cases rather than improving on known benchmarks.
See references/problem-selection.md for the full selection framework.
Step 4: Design a Solution
Novel techniques are creative combinations of existing methods, not simple concatenations. Use two design patterns:
- Cross-domain transfer: Find papers in completely different domains that solve a technically similar problem, then adapt their solution.
- Problem decomposition: Break the problem into sub-problems, solve each via cross-domain transfer, then combine.
See references/solution-design.md for the full design methodology and knowledge distillation pipeline.
Step 5: Validate and Iterate
Run experiments on representative data. Use results to refine your understanding. If the approach fails, return to Step 3 or Step 4 with updated knowledge from the failure.
Output artifacts: Research direction summary (problem statement, proposed approach, novelty claim, key risks) — this becomes the input to idea-tournament or paper-planning.
See the experiment-craft skill for systematic debugging when experiments don't work as expected.
Counterintuitive Ideation Rules
Prioritize these rules before regular ideation:
- Problem selection matters more than solution design: Choosing WHAT to solve has more impact than HOW you solve it. A great solution to an unimportant problem is still unimportant.
- Pursue new failure cases, not incremental improvements: Don't improve a technique on its original setting. Find new settings where it breaks — new failure cases on new data are contributions even if the technique itself isn't novel.
- If a well-established solution exists, switch problems: Solving an already-solved problem wastes time regardless of your angle. Improvement space is too small.
- Technology is creative combination, not concatenation: Novel techniques combine existing methods in non-obvious ways. Simple A-to-B pipelines are not contributions — if direct concatenation worked, the problem would have no technical challenge.
- When a breakthrough tool appears, apply it to YOUR roadmap: Don't improve the tool itself on its original benchmarks. Use it to solve YOUR milestone tasks — this produces high-impact work because you combine the tool's power with your domain expertise.
- A paper without real contribution wastes your time: Even if accepted, it doesn't advance the field or earn respect. Do work that genuinely moves the needle.
Structured Paper Reading
Turn reading into structured Q&A using a paper parsing tree. Three levels of depth:
| Level | Goal | What You Can Do After |
|---|---|---|
| 1. Technical | Understand all details and terminology | Reproduce the method; explain each component |
| 2. Analytical | Know what problem it solves and why this approach | Explain the paper's motivation and design choices |
| 3. Contextual | Know its position in the literature tree | Update your field map; generate new research questions |
Write a structured summary for every paper you read. Use the template at assets/paper-summary-template.md.
See references/paper-reading.md for the full reading methodology and habit-building guidance.
Handoff to Idea Tournament or Planning
When you have a research direction but want to explore multiple concrete approaches, pass to idea-tournament for tree-structured generation and Elo ranking before planning.
When ideation is complete — you have a problem, a proposed solution approach, and supporting literature — pass these artifacts to paper-planning:
| Artifact | Source Step | Used By |
|---|---|---|
| Research goal and scope | Step 1 | Story design (task definition) |
| Literature tree (novelty + challenge-insight) | Step 2 | Related work mapping, novelty claims |
| Problem statement and motivation | Step 3 | Introduction motivation paragraphs |
| Solution sketch and design rationale | Step 4 | Method section planning |
| Key failure cases to address | Step 3 | Experiment planning (stress tests) |
| Relevant prior work and their limitations | Step 2 | Baseline selection, comparison design |
Reference Navigation
| Topic | Reference File | When to Use |
|---|---|---|
| Literature tree construction | literature-tree.md | Mapping a research field |
| Problem selection | problem-selection.md | Evaluating whether a problem is worth solving |
| Solution design | solution-design.md | Designing a novel approach |
| Paper reading methodology | paper-reading.md | Reading papers effectively |
| Paper summary template | paper-summary-template.md | Writing structured paper notes |
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
paper-writing
Guides writing academic papers section by section using an 11-step workflow with LaTeX templates and counterintuitive writing tactics. Covers Abstract, Introduction, Method, Experiments, Related Work, Conclusion, and Supplementary. Use when: user asks to write or draft a paper section, needs LaTeX templates, wants to improve academic writing quality, optimize novelty framing, or mentions 'write introduction', 'draft method', 'paper writing'. Do NOT use for pre-submission review (use paper-review), experiment execution (use experiment-pipeline), or paper planning/story design (use paper-planning).
evo-memory
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).
paper-navigator
End-to-end academic paper workflow: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), read with structured analysis (3-level strategy), and organize into literature maps or reports. Use when: finding papers, reading a paper, related work, literature survey, citation analysis, research trends, SOTA results, datasets, or literature reports. Do NOT use for writing a literature review section (use paper-writing), comparing research ideas (use idea-tournament), or planning paper structure (use paper-planning).
paper-review
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: 5-aspect checklist (contribution sufficiency, writing clarity, results quality, testing completeness, method design), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust, promote limitations, attack novelty), reverse-outlining, and figure/table quality checks. Use when: user wants to self-review or self-check their own paper draft before submission, stress-test their claims, prepare for reviewer criticism, or mentions 'self-review', 'check my draft', 'is my paper ready'. Do NOT use for writing a peer review of someone else's paper, and do NOT use after receiving actual reviews (use paper-rebuttal instead).
experiment-craft
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
experiment-pipeline
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
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