agentic-search

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Run a local-first research workflow by planning sub-queries, iterating retrieval, validating sources, and producing traceable evidence-backed conclusions.

0verL1nk By 0verL1nk schedule Updated 3/21/2026

name: agentic_search description: Run a local-first research workflow by planning sub-queries, iterating retrieval, validating sources, and producing traceable evidence-backed conclusions. metadata: level: advanced

Agentic Search Skill

When To Use

Use this skill when the user asks for:

  • multi-step research instead of a single lookup
  • external verification for incomplete document evidence
  • evidence-backed conclusions with explicit source traceability
  • gap-filling loops (find missing facts, then re-search)

Do not use this skill for:

  • direct extraction from a clearly scoped local document where one retrieval pass is enough
  • purely stylistic rewriting tasks

Workflow

  1. Define research objective and output contract
  • Convert user request into explicit deliverables: key question, expected depth, and evidence contract.
  • For project-document evidence, the contract is <evidence>chunk_id|p页码|o起止偏移</evidence>.
  1. Build query plan
  • Split into sub-queries by intent:
    • factual lookup
    • method comparison
    • recency update
    • risk/limitation check
  • Prefer local document retrieval first.
  1. Execute iterative retrieval
  • Run local/document search first.
  • If local evidence is insufficient, expand to scholarly search and then web search.
  • Track each round with: query, source type, key hit, confidence.
  1. Evaluate source quality
  • Score sources by credibility, relevance, recency, and cross-source consistency.
  • Downgrade claims that rely on a single weak source.
  1. Synthesize answer with traceability
  • Produce structured findings with explicit evidence anchors.
  • When citing project-document evidence in prose, use <evidence>chunk_id|p页码|o起止偏移</evidence> instead of bracket citations or free-form page references.
  • Separate confirmed facts, probable inferences, and unresolved gaps.

Runtime Contract

  • Return conclusions that are evidence-backed and audit-friendly.
  • For each key conclusion, attach at least one source anchor.
  • For current-project document anchors, use the exact <evidence>chunk_id|p页码|o起止偏移</evidence> format.
  • If evidence conflicts, explain conflict instead of forcing one conclusion.
  • If evidence is still insufficient, output explicit follow-up search actions.

Progressive References

Read additional references only when needed:

  • references/workflow_blueprint.md: full multi-round execution blueprint
  • references/source_quality_rubric.md: scoring and conflict-resolution rubric
  • references/output_schema.md: final structured output schema

Scripted Helpers

  • scripts/evidence_aggregator.py: normalize and merge heterogeneous evidence records
  • scripts/source_score.py: deterministic source scoring utility
Install via CLI
npx skills add https://github.com/0verL1nk/PaperSage --skill agentic-search
Repository Details
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