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madebyarismadebyaris/spec-kit-command-cursor/.cursor/skills/sdd-research/SKILL.md

sdd-research

Skill198 starsChanged 5 months agoMITView on GitHub

Pattern investigation and technical research before specification. Use when technical approach is unclear, exploring existing solutions, or analyzing codebase patterns. Supports deep research mode for thorough external investigation.

1 other project uses a version of this.

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  • Other versions1 project uses a version of this.

    Word for word · 0

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    Wrote their own version · 1

    1. mariano-aguero/spec-driven-development-skill★ 11same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository6 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 6 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Before /specify or /plan commands

    line 15
  2. Very specific:

    Run scripts/scan-patterns.sh to auto-detect project stack before manual exploration.

    line 40
  3. Very specific:

    Use WebSearch to survey the solution space (e.g.

    line 52
  4. Very specific:

    Use WebFetch to read official docs, getting-started guides, and API references for each candidate

    line 57
  5. Distinctive:

    Search for "[candidate] vs [candidate]" comparisons, benchmarks, and post-mortems

    line 62
  6. Distinctive:

    Search for "[candidate] production issues" or "[candidate] limitations"

    line 63
  7. Distinctive:

    Look for community size indicators: GitHub stars, npm weekly downloads, Stack Overflow activity

    line 64
  8. Distinctive:

    Check compatibility with the project's detected stack (from Phase 1)

    line 67
  9. Distinctive:

    Search for "[candidate] + [framework]" integration guides

    line 68
  10. Distinctive:

    Use the ask question tool when research reveals multiple valid approaches

    line 131

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

SDD Research Skill

Investigate codebase patterns and external solutions to inform specification and planning. Supports two modes: standard (codebase-focused) and deep (comprehensive external investigation).

When to Use

  • Technical approach is unclear
  • Need to understand existing patterns
  • Evaluating solution options
  • Before /specify or /plan commands
  • Deep research: New domain, unfamiliar technology, high-stakes architectural decision, or when standard research yields insufficient clarity

Research Modes

Standard Research (default)

Quick internal + surface external analysis. Good for well-understood domains where the codebase already has relevant patterns.

Deep Research

Multi-pass external investigation using web search and documentation fetching. Use when:

  • Entering an unfamiliar technology domain
  • Comparing multiple complex solutions (e.g. auth providers, database engines, deployment platforms)
  • The decision has high cost-of-reversal (architecture, data model, vendor lock-in)
  • Standard research leaves too many unknowns

Trigger: User requests deep research explicitly, or the agent detects high uncertainty after Phase 1.

Research Protocol

Phase 1: Codebase Analysis

  1. Existing patterns — how similar problems are solved
  2. Reusable components — what can be leveraged
  3. Conventions — naming, structure, architecture patterns
  4. Dependencies — libraries/frameworks in use

Run scripts/scan-patterns.sh to auto-detect project stack before manual exploration.

Phase 2: External Solutions (Standard)

  1. Best practices — industry standards for this problem
  2. Library options — available tools and tradeoffs
  3. Architecture patterns — applicable design patterns

Phase 2-Deep: Deep External Research (when deep mode is active)

Perform iterative, multi-pass investigation:

Pass 1 — Landscape scan:

  • Use WebSearch to survey the solution space (e.g. "best [technology] for [use case] 2026")
  • Identify the top 3-5 candidates from search results
  • Note official documentation URLs for each candidate

Pass 2 — Documentation deep-dive:

  • Use WebFetch to read official docs, getting-started guides, and API references for each candidate
  • Extract: API surface, pricing model, limits, supported platforms, migration path
  • Note version numbers and last-updated dates (reject stale/abandoned projects)

Pass 3 — Real-world validation:

  • Search for "[candidate] vs [candidate]" comparisons, benchmarks, and post-mortems
  • Search for "[candidate] production issues" or "[candidate] limitations"
  • Look for community size indicators: GitHub stars, npm weekly downloads, Stack Overflow activity

Pass 4 — Integration feasibility:

  • Check compatibility with the project's detected stack (from Phase 1)
  • Search for "[candidate] + [framework]" integration guides
  • Identify required changes to existing architecture

Deep research output additions:

  • Source URLs for all claims (linked in the research doc)
  • Confidence level per finding (High / Medium / Low — based on source quality)
  • "Last verified" date for each external fact

Phase 3: Synthesis

  1. Compare options — pros/cons matrix with weighted criteria
  2. Recommend approach — based on findings, with confidence level
  3. Flag risks — technical concerns and unknowns
  4. Deep research only: Include source bibliography and confidence assessment

Output Format

# Research: [Topic]

## Summary
[1-2 sentence overview]
**Research mode:** Standard | Deep
**Confidence:** High | Medium | Low

## Codebase Analysis
### Existing Patterns
| Pattern | Location | Relevance |

### Reusable Components
- [component]: [how to leverage]

## External Solutions
### Option 1: [Name]
- **Pros**: | **Cons**: | **Effort**:
- **Source**: [URL] (deep research only)

## Comparison Matrix
| Criteria | Weight | Option 1 | Option 2 |

## Recommendation
[Recommended approach with rationale]
**Confidence:** [High/Medium/Low] — [why]

## Risks & Unknowns
- [risk]: [mitigation]

## Sources (deep research only)
- [URL]: [what was learned]

References

  • references/patterns.md — Common architectural patterns
  • references/deep-research-guide.md — Deep research methodology, search strategies, and source evaluation criteria

Scripts

  • scripts/scan-patterns.sh [project-root] — Auto-detect frameworks, languages, testing tools, and project structure conventions

Integration

  • Findings feed into /specify and sdd-planner subagent
  • Can be invoked by sdd-explorer for deeper analysis
  • Use the ask question tool when research reveals multiple valid approaches
  • Deep research mode uses WebSearch and WebFetch tools extensively — ensure sandbox allows outbound access

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