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---
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name: document-triage
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description: Scope a document research task, prioritize sources, and define an evidence-driven reading plan
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license: MIT
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compatibility: opencode
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metadata:
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audience: analysts
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workflow: research
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stage: triage
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---
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## What I do
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- Define the research objective in precise terms
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- Break the task into answerable sub-questions
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- Identify which documents are likely primary, secondary, or low-value
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- Propose a reading order based on expected relevance
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- Highlight likely evidence types such as requirements, decisions, risks, assumptions, and timelines
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- Create a focused plan before deep reading begins
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## When to use me
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Use this skill at the start of a document-based research task.
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Examples:
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- You have a folder of policies, specs, meeting notes, or reports and need a systematic approach
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- You need to answer a question using internal documentation
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- You need to map a large corpus before extracting evidence
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- You want to avoid reading everything in full
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## Inputs I expect
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Provide as many of these as available:
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- Research goal or question
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- List of documents, filenames, or brief descriptions
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- Any constraints such as deadline, topic boundaries, time period, or required output
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- Any known "must-cover" documents
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- Preferred output format if already defined
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## Workflow
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1. Restate the research objective in one sentence
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2. Convert the objective into focused sub-questions
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3. Classify available documents by likely value:
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- Primary source
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- Supporting source
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- Background source
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- Likely irrelevant
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4. Identify expected signal in each source:
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- Requirements
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- Decisions
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- Responsibilities
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- Risks
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- Dates or milestones
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- Definitions
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- Exceptions
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5. Recommend an efficient reading order
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6. Define extraction criteria for the next stage
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7. Flag ambiguities, missing sources, and review risks
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## Output format
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Produce these sections:
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### Research Objective
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A single concise statement of the exact question to answer.
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### Sub-questions
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A numbered list of focused questions the research should resolve.
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### Source Triage
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For each document:
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- Document name
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- Classification
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- Why it matters
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- What to look for
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### Reading Plan
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A prioritized reading sequence with rationale.
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### Extraction Criteria
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A checklist of evidence worth capturing in the next phase.
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### Risks and Gaps
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Explicitly list missing documents, unclear scope, and likely blind spots.
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## Quality bar
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- Be specific, not generic
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- Prefer primary documents over commentary
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- Do not assume a document is authoritative unless the user says so
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- Separate fact-finding from interpretation
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- Explicitly note what cannot be determined yet
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## Do not
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- Do not summarize the documents in depth at this stage
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- Do not invent document contents
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- Do not claim conclusions before evidence extraction
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- Do not merge assumptions with facts
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## Hand-off to companion skills
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After this skill, use:
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- `evidence-extraction` to pull structured findings
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- `research-synthesis` to produce a final answer, brief, matrix, or report
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---
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name: evidence-extraction
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description: Extract structured evidence from documents with traceability, quotations, and confidence markers
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license: MIT
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compatibility: opencode
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metadata:
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audience: analysts
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workflow: research
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stage: extraction
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---
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## What I do
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- Read documents and extract evidence relevant to a defined research question
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- Separate direct evidence from interpretation
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- Capture exact quotations or precise paraphrases
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- Record document references for every extracted item
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- Structure findings so they can be audited and synthesized later
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- Mark uncertain or ambiguous findings for review
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## When to use me
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Use this after the research task has been scoped and prioritized.
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Examples:
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- You need requirements, decisions, obligations, controls, or action items extracted from documents
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- You need a traceable evidence table
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- You need to compare what multiple documents say about the same issue
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- You need defensible notes before writing a summary or conclusion
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## Inputs I expect
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Provide:
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- The research objective or sub-question
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- One or more documents or excerpts
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- Optional triage output from `document-triage`
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- Optional extraction schema if the project already has one
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## Core extraction rules
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For every finding, preserve:
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- Finding ID
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- Topic or theme
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- Evidence type
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- Source document
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- Section, heading, page, or paragraph reference if available
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- Extracted text
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- Short analyst note
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- Confidence level
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- Review flag where needed
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Evidence types may include:
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- Requirement
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- Decision
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- Risk
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- Assumption
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- Responsibility
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- Constraint
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- Definition
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- Date or milestone
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- Dependency
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- Open issue
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## Workflow
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1. Read the objective and extraction criteria
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2. Scan the document for relevant passages
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3. Extract only evidence tied to the research question
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4. Distinguish:
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- Direct statement
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- Implied interpretation
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- Missing information
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5. Attach traceable references for each item
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6. Normalize wording without losing meaning
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7. Flag ambiguity, contradiction, or incomplete support
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## Output format
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Use a structured table like this:
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| # | Topic | Evidence Type | Description | Document Reference | Extracted Evidence | Analyst Note | Confidence | Review Needed |
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|---|---|----|-----------|-----|--------------------|------|----|---------|------|---------|-------|
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Where:
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- **Description** is concise and factual
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- **Document Reference** is precise enough to locate the source again
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- **Extracted Evidence** is a quote or faithful paraphrase
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- **Analyst Note** explains relevance without overstating certainty
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- **Confidence** is one of: High, Medium, Low
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- **Review Needed** is `Yes` when ambiguity remains
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## Comparison mode
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If multiple documents cover the same topic, add a second table:
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| Topic | Source A | Source B | Agreement | Conflict | Notes |
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|------|----------|----------|----------|----------|----|
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Use this to identify contradictions, differences in wording, or missing alignment.
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## Quality bar
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- Every claim must be traceable to a source
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- Keep extraction atomic: one row per distinct finding
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- Preserve important qualifiers such as "must", "should", "may", "unless", and exceptions
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- Mark uncertainty explicitly instead of smoothing it over
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- Prefer exact citations over memory-based summaries
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## Do not
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- Do not write the final conclusion here
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- Do not collapse multiple findings into one vague row
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- Do not omit document references
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- Do not present interpretation as if it were a quote
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- Do not silently resolve contradictions
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## Hand-off to companion skills
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After this skill, use:
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- `research-synthesis` to turn extracted evidence into a conclusion, report, briefing, matrix, or recommendation
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---
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name: research-synthesis
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description: Synthesize extracted document evidence into a clear, traceable conclusion, brief, or decision-ready report
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license: MIT
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compatibility: opencode
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metadata:
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audience: analysts
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workflow: research
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stage: synthesis
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---
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## What I do
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- Synthesize extracted evidence into a coherent answer
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- Build conclusions that remain traceable to source material
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- Identify consensus, conflicts, gaps, and unresolved questions
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- Produce outputs such as briefs, summaries, comparison matrices, decision memos, and research reports
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- Keep factual findings separate from recommendations or interpretation
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## When to use me
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Use this after evidence has been extracted and structured.
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Examples:
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- You need a final answer to a document-based research question
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- You need an executive summary grounded in source material
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- You need a position paper or internal brief
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- You need a gap analysis or comparison across documents
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- You need a recommendation with explicit evidence support
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## Inputs I expect
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Provide:
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- Research objective
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- Extracted evidence table or notes
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- Optional comparison table
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- Desired output type, such as:
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- summary
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- brief
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- memo
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- report
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- gap analysis
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- comparison matrix
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- recommendation
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## Workflow
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1. Restate the question being answered
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2. Group evidence by theme or sub-question
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3. Identify:
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- Supported conclusions
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- Partial support
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- Contradictions
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- Missing evidence
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4. Draft findings with explicit traceability
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5. Separate:
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- Facts from documents
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- Interpretation
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- Recommendations
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6. Produce a final structured output fit for the requested audience
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7. End with known limitations and review points
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## Output modes
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### A) Research Brief
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Use for concise decision support.
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Structure:
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- Question
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- Short answer
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- Key findings
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- Conflicting evidence
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- Gaps
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- Recommended next step
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### B) Research Report
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Use for fuller analytical output.
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Structure:
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- Objective
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- Scope
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- Method
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- Findings by theme
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- Evidence-backed conclusion
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- Known gaps and limitations
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- Appendix with source references
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### C) Gap Analysis
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Use when comparing required versus documented state.
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Structure:
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- Assessment topic
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- Evidence found
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- Gap
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- Impact
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- Confidence
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- Review needed
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## Traceability rule
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For every substantive conclusion, include a source anchor such as:
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- document name
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- section or page
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- extracted finding number if available
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If traceability is weak, say so explicitly.
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## Quality bar
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- Conclusions must follow from the extracted evidence
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- Contradictions must be surfaced, not hidden
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- Distinguish "document says" from "my recommendation"
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- State limits of the available corpus
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- Prefer precise wording over polished vagueness
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## Do not
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- Do not introduce facts that were not extracted
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- Do not overclaim certainty
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- Do not bury disagreements between documents
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- Do not produce recommendations without showing the evidence basis
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- Do not omit limitations
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## Companion skill sequence
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Recommended sequence:
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1. `document-triage`
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2. `evidence-extraction`
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3. `research-synthesis`
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Use all three together for rigorous document-based research with clear traceability.
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Reference in New Issue
Block a user