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AgentWorld supports a repository-local skill marketplace under `skills/`.
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Each skill is a reusable package of execution guidance that can be loaded by one operator node without forcing the same behavior onto every other node in the graph.
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## Structure
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Each skill usually lives in its own folder:
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```text
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skills/
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└── skill-name/
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├── SKILL.md
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├── references/
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├── scripts/
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└── assets/
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```
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Only `SKILL.md` is required. The other folders are optional.
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## Included Skills
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| Skill | Purpose |
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| --- | --- |
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|`research-paper-search`| paper discovery, identifier collection, source triage |
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|`literature-synthesis`| theme extraction, contradiction mapping, gap analysis |
The runtime injects the selected skill names into the operator request, so the operator can build prompts and execution context for that specific node.
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## Notes
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- Skills are public project assets and should stay in English
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- Skills should be specific enough to be reusable, but not so narrow that they only fit one task
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- If a skill needs code or references, keep them inside the skill folder
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- Private working notes belong in ignored local files, not in `skills/`
description: Use when an operator must verify citation accuracy, bibliography consistency, metadata quality, and whether claims are actually supported by the cited sources.
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---
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# Citation Audit
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## When to Use This Skill
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Use this skill when the node needs to:
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- verify that citations support the written claim
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- check metadata such as title, authors, venue, year, and DOI
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- detect missing or duplicate references
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- clean up a bibliography before release
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- review whether a writeup overstates what a cited paper proves
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## Workflow
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1. Check whether every major claim has a citation.
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2. For each citation, confirm that the source actually supports the stated claim.
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3. Normalize key metadata fields.
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4. Remove duplicates and weak citations.
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5. Flag unsupported or overstated claims.
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## Expected Outputs
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- a citation issue list
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- corrected metadata suggestions
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- unsupported claim warnings
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- a cleaned bibliography checklist
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## Quality Rules
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- citation presence is not enough; support must be real
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- prefer primary papers over derivative references
description: Use when an operator needs to convert a research objective into an executable plan with deliverables, checkpoints, dependencies, and validation criteria.
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---
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# Experiment Planning
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## When to Use This Skill
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Use this skill when the node needs to:
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- turn a vague research goal into a concrete plan
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- define inputs, outputs, metrics, and success criteria
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- break work into stages for planner, coder, and reviewer nodes
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- identify risky steps before execution begins
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- decide what evidence is needed to claim success
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## Workflow
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1. Rewrite the objective as a measurable question.
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2. List required inputs, tools, datasets, and dependencies.
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3. Break the task into stages with expected deliverables.
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4. Add validation gates for each stage.
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5. Mark high-risk assumptions and fallback paths.
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## Expected Outputs
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- a staged execution plan
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- a deliverable checklist
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- a risk register
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- validation checkpoints
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## Quality Rules
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- plans should be executable, not aspirational
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- every stage should produce an inspectable artifact
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- note blockers early instead of hiding them in later steps
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- define what counts as success before running the work
description: Use when an operator needs to find relevant papers, databases, identifiers, benchmark references, or primary-source evidence before planning or implementation.
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---
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# Research Paper Search
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## When to Use This Skill
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Use this skill when the node needs to:
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- find primary papers for a topic
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- identify canonical baselines or benchmark papers
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- collect DOI, arXiv, PMID, or project URLs
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- narrow a broad problem into a tractable evidence set
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- distinguish primary sources from commentary or summaries
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## Workflow
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1. Start from the task objective and convert it into 2-4 search queries.
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2. Prefer primary sources: papers, official datasets, benchmark repos, and technical documentation.
description: Use when an operator must review outputs for unsupported conclusions, weak evidence, missing ablations, broken assumptions, or incomplete reporting.
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---
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# Result Audit
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## When to Use This Skill
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Use this skill when the node needs to:
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- review a report, notebook, or artifact bundle
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- detect unsupported conclusions
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- check whether the evidence matches the claimed contribution
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- flag missing baselines, controls, or ablations
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- judge whether the final output is ready for handoff or publication
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## Workflow
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1. Read the stated claim before the evidence.
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2. Map each important claim to the supporting artifact or result.
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3. Check for missing comparisons, controls, or caveats.
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4. Identify where the output overstates certainty.
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5. Return findings as prioritized review items.
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## Expected Outputs
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- a severity-ranked review list
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- unsupported claim flags
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- missing evidence or baseline flags
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- final release recommendation
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## Quality Rules
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- focus on evidence, not writing style
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- do not accept "looks plausible" as support
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- call out missing baselines explicitly
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- separate hard failures from optional improvements
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