openags/auto-research · Archived

research-workflow

Dynamic research workflow management with self-reflection and backtracking

First seen Jul 9, 2026

Installation

$ npx skills add openags/auto-research --skill research-workflow

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More details

Agent compatibility

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Repository health

Stars 4
License LICENSE
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,219 B
  • docs SUMMARY.md 99 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Research Workflow Management

When managing a research project, follow this adaptive workflow:

Stage Progression (typical order, but flexible)

  1. Literature Review → Understand the field

- Dispatch: dispatch_agent(role="literature", task="...") - Expected output: Review notes in literature/notes/, BibTeX in references - Proceed when: Review covers key related work with cited papers

  1. Research Proposal → Define the research question

- Dispatch: dispatch_agent(role="proposer", task="...") - Expected output: Proposal document in proposal/ideas/ - Proceed when: Clear hypotheses, methodology, and expected outcomes

  1. Experiments → Validate the hypothesis

- Dispatch: dispatch_agent(role="experimenter", task="...") - Expected output: Code in experiments/code/, results in experiments/results/ - Proceed when: Code runs successfully and produces meaningful results - Common backtrack: If results don't support hypothesis → re-examine proposal

  1. Manuscript → Write the paper

- Dispatch: dispatch_agent(role="writer", task="...") - Expected output: LaTeX in manuscript/main.tex - Proceed when: All sections drafted with citations

  1. Peer Review → Quality check

- Dispatch: dispatch_agent(role="reviewer", task="...") - Expected output: Structured review with scores - Common backtrack: If scores < 6/10 → address specific feedback

Self-Reflection Protocol

After each agent completes, reflect on:

  • Quality: Is the output good enough for the next stage?
  • Consistency: Does it align with previous stages?
  • Completeness: Are there gaps that need filling?

If issues are found, you have three options:

  1. Fix: Dispatch the same agent with more specific instructions
  2. Backtrack: Go to an earlier stage to address root causes
  3. Consult: Use ask_user to get human guidance