research-idea-explorer

Category: Content & Multimedia | Uploader: jameslemon2002 | Downloads: 0 | Version: v1.0（Latest）

Use when the user wants cross-disciplinary research ideas generated as concise research cards with Title, Abstract, Design, Distinctiveness, and Significance rather than one-shot prompting.

Changelog: Source: GitHub https://github.com/jameslemon2002/research-idea-explorer

Directory Structure

Current level: tree/main/research_idea_explorer/assets/skills/research-idea-explorer/

📁 agents/
- 📄 openai.yaml 252 B
📁 references/
- 📄 retrieval.md 240 B
- 📄 schema.md 220 B
📄 SKILL.md 5.5 KB

SKILL.md

---
name: research-idea-explorer
description: Use when the user wants cross-disciplinary research ideas generated as concise research cards with Title, Abstract, Design, Distinctiveness, and Significance rather than one-shot prompting.
---

# Research Idea Explorer Skill

Use this skill when the task is to generate, refine, or compare research ideas across disciplines.

## Default policy

If the machine has the packaged CLI installed, prefer calling:

- `research-idea-explorer`
- or `rie`

Use the repo-local fallback `python -m research_idea_explorer.cli` only when the packaged command is unavailable but the repository is present in the workspace.

Default to `literature-first` for every non-pure-functional invocation.

That means:

- If the user asks to generate, refine, compare, extend, red-team, or continue research ideas, search literature first.
- Treat literature retrieval as the default prelude, not an optional extra, unless the user explicitly says not to use it.
- Keep `hybrid` retrieval as the default strategy unless the user explicitly prefers `lexical`, `embedding`, or `graph`.
- Only skip retrieval for pure utility actions such as:
  - formatting an existing result
  - inspecting `graph`, `summary`, or `neighbors`
  - writing `feedback`
  - explaining the output schema or command usage

The workflow is:

1. Start with a literature-guided single-pass divergence across multiple research moves. The built-in persona set remains an internal way to keep those moves orthogonal, but it should not dominate the user-facing framing.
2. Convert only the strongest brainstorm seeds into the shared internal schema:
   `Object`, `Puzzle`, `Claim`, `Contrast`, `Evidence`, `Scope`, `Stakes`.
3. Reject ideas that are obvious near-duplicates or amount to a thin "method + topic" pairing.
4. If the user asks to deepen, branch, compare, or continue from an accepted direction, run a second literature-guided mutation round around the retained frontier families.
5. Push each retained idea one step further by adding:
   data source, minimal runnable design, likely failure mode, and why the question matters now.
6. When available, use the persistent memory graph so the session does not keep revisiting the same research neighborhoods.

## Preference handling

When the user expresses stable preferences in conversation, carry them into the CLI call instead of treating them as disposable chat context.

Typical examples:

- preferred direction:
  `prefer causal identification`, `focus on policy relevance`
- constraints:
  `avoid survey`, `don't do LLM`, `less benchmark work`

Default behavior:

- Pass those signals through `--preference-note`.
- Use `--remember-preferences topic` when the preference is specific to the current topic.
- Use `--remember-preferences global` only when the user clearly wants the same preference to apply across topics.
- Reuse the same memory path so stored preferences can affect the next `ideas` run.

## CLI mapping

When you want the packaged command to do the work instead of manually re-creating the flow:

- generate ideas:
  `research-idea-explorer ideas --query "..."`
- inspect memory:
  `research-idea-explorer graph --memory ./data/memory/cli-memory.json`
- list ideas for feedback:
  `research-idea-explorer feedback --memory ./data/memory/cli-memory.json`
- accept or reject an idea:
  `research-idea-explorer feedback --memory ./data/memory/cli-memory.json --idea-id <idea-id> --decision accepted`
- generate ideas while storing the user's conversation preferences:
  `research-idea-explorer ideas --query "..." --preference-note "prefer causal identification, avoid survey" --remember-preferences topic`
- store preferences during feedback on a retained idea:
  `research-idea-explorer feedback --memory ./data/memory/cli-memory.json --idea-id <idea-id> --decision accepted --note "prefer causal identification, avoid survey" --remember-preferences topic`
- inspect stored preference profiles:
  `research-idea-explorer graph --memory ./data/memory/cli-memory.json --view preferences`

If only the repo-local version is available, the same commands can be run with `python -m research_idea_explorer.cli`.

## Guardrails

- Do not output only titles. Output brainstorm seeds first when useful, then concise research cards.
- Do not overuse `Blind spot` if a stronger puzzle such as `Conflict`, `Distortion`, or `Mechanism unknown` is available.
- Treat `Scale` and `Counterfactual` as part of contrast and scope unless the user explicitly wants them foregrounded.
- Search literature by default before proposing ideas, and use metadata to identify crowded areas before proposing ideas.
- Prefer diversity of research moves over superficial variation in wording.

## Diagram provenance

If you produce a diagram, distinguish clearly between:

- a computed graph generated from stored nodes and edges
- a literature-derived map synthesized from retrieved papers
- a conceptual sketch synthesized by the agent

Do not present a hand-authored conceptual diagram as an automatically generated network graph.

## Output shape

Each default research card should contain:

- `Title`
- `Abstract`
- `Design`
- `Distinctiveness`
- `Significance`

The richer internal schema can still be used when deeper analysis is needed, but the default user-facing card should stay concise.

## References

Read [references/schema.md](references/schema.md) when you need the field meanings, internal move set, or puzzle definitions.
Read [references/retrieval.md](references/retrieval.md) when literature retrieval is available and you need to decide between lexical, embedding, or graph expansion.

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