Agent skill

research

(ePost) Use when user asks to research, compare options, find best practices, or investigate a technology

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/research-klara-copilot-epost-agent-kit

Metadata

Additional technical details for this skill

keywords
research investigation documentation sources validation best-practices how-to compare evaluate library
triggers
[
    "/research",
    "research",
    "best practices",
    "how to"
]
platforms
[
    "all"
]
agent affinity
[
    "epost-researcher",
    "epost-planner",
    "epost-mcp-manager"
]

SKILL.md

Research Skill

Delegation — REQUIRED

This skill MUST run via the epost-researcher agent, not inline.

When research intent is detected:

  1. Use the Agent tool to spawn epost-researcher
  2. Pass the research topic + scope + report path (reports/research-{date}-{slug}.md)
  3. Do NOT conduct research inline in the main conversation

Purpose

Multi-source information gathering and synthesis. Provide strategic technical intelligence that enables informed decision-making.

Honoring YAGNI, KISS, and DRY. Be honest, be brutal, straight to the point, concise.

When Active

User asks for research, best practices, comparison, technology evaluation, solution design.


Research Phases

Phase 1: Scope Definition

Clearly define scope before searching:

  • Identify key terms and concepts to investigate
  • Determine recency requirements (how current must information be)
  • Establish evaluation criteria for sources
  • Set boundaries for research depth

Phase 2: Information Gathering

Check active engine (set by session-init, default: websearch):

bash
echo $EPOST_RESEARCH_ENGINE   # gemini | websearch

Engine invocation (max 5 parallel queries — think carefully before each):

Engine: gemini

bash
echo "<research query>" | gemini -y -m "$EPOST_GEMINI_MODEL"

Availability check: which gemini — if not found, log coverage gap and fall back to WebSearch.

Engine: websearch (default / fallback)

Use Claude's built-in WebSearch tool with precise queries:

  • Include terms like "best practices", "2024/2025", "security", "performance"
  • Craft multiple related queries and run in parallel
  • Prioritize official docs, GitHub repos, authoritative blogs

Fallback chain:

  1. Invoke configured engine
  2. If unavailable (binary missing / exit code 2): add to Methodology coverageGaps[]
  3. Fall back to WebSearch automatically — do not block or ask user

See references/engines.md for full invocation details, model options, and exit codes.

Deep content analysis: For GitHub repos found, use docs-seeker to read them

  • Focus on README, API references, changelogs, release notes
  • Review version-specific information

Cross-reference validation:

  • Verify across multiple independent sources
  • Check publication dates for currency
  • Identify consensus vs. controversial approaches
  • Note conflicting information

Phase 3: Analysis and Synthesis

  • Identify common patterns and best practices
  • Evaluate pros and cons of different approaches
  • Assess maturity and stability of technologies
  • Recognize security implications and performance considerations
  • Determine compatibility and integration requirements

Phase 4: Report Generation

Save report to path provided by caller (reports/research-{date}-{slug}.md).

Use references/report-template.md for output structure. Report Methodology section must include:

  • Knowledge Tiers: which engine was used (Gemini, Perplexity, WebSearch)
  • Coverage Gaps: if configured engine was unavailable and fallback fired

Report must also:

  • Include timestamp of when research was conducted
  • Provide table of contents for longer reports
  • Use code blocks with appropriate syntax highlighting
  • Include diagrams (mermaid or ASCII art) where helpful
  • Conclude with specific, actionable next steps
  • List unresolved questions at the end

Source Priority

  1. Official documentation (highest)
  2. Official examples/tutorials
  3. Well-known community resources
  4. GitHub repositories with recent activity
  5. Stack Overflow (for specific issues)

Quality Standards

Standard Requirement
Accuracy Verified across multiple sources
Currency Prefer last 12 months; note when using older material
Completeness Cover all aspects requested
Actionability Practical, implementable recommendations
Clarity Define technical terms, provide examples
Attribution Always cite sources with links and dates

Special Considerations

  • Security topics: Check for recent CVEs and security advisories
  • Performance topics: Look for benchmarks and real-world case studies
  • New technologies: Assess community adoption and support levels
  • APIs: Verify endpoint availability and authentication requirements
  • Older technologies: Always note deprecation warnings and migration paths

Advanced Techniques

Query Fan-Out

  • Ask multiple related questions in parallel
  • "What is X?" + "How to use X?" + "Best practices for X?"
  • Reduces total research time

Source Validation

  • Cross-reference claims across 3+ sources
  • Check if multiple sources cite same research
  • Look for contradictions and note them
  • Verify dates (prefer sources <2 years old)

Technology Trend Identification

  • Check GitHub stars and recent activity
  • Review recent changelog/updates
  • Look at community sentiment in forums
  • Note if project is actively maintained
  • Watch for deprecation notices

Code Example Validation

  • Test examples in isolated environment
  • Verify version matches your target
  • Check example handles error cases
  • Look for performance implications

Best Practices

  • Prioritize official docs
  • Check publication dates (prefer <2 years)
  • Verify code examples work
  • Note version-specific info clearly
  • Cite sources with URLs and dates
  • Cross-validate findings
  • Document contradictions
  • Track confidence level per finding
  • Sacrifice grammar for concision in reports

Knowledge-First Research

Before external research, check internal knowledge:

  1. Search docs/ for prior research on the topic
  2. Check skill aspect files for existing domain knowledge
  3. Search agent memory for related past sessions
  4. Only proceed to external sources if internal knowledge is insufficient

Use knowledge-retrieval skill for the full priority chain.

Use knowledge-capture skill to persist learnings after this task.


Sub-Skill Routing

When this skill is active and user intent matches a sub-skill, delegate:

Intent Sub-Skill / Tool When
Explore codebase scout /scout, "explore", "find in codebase"
Search docs docs-seeker External documentation search
Export context repomix /repomix, bundle code for external review
Gemini search gemini CLI via Bash $EPOST_RESEARCH_ENGINE = gemini
Web search WebSearch tool $EPOST_RESEARCH_ENGINE = websearch or fallback

Related Skills

  • knowledge-retrieval — Internal-first search protocol
  • knowledge-capture — Post-task capture workflow

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