research

Use when needing to understand requirements before implementation. Gathers context from Jira, Confluence, codebase, and docs. Produces research document with confidence assessment.

Best use case

research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Use when needing to understand requirements before implementation. Gathers context from Jira, Confluence, codebase, and docs. Produces research document with confidence assessment.

Teams using research should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/research/SKILL.md --create-dirs "https://raw.githubusercontent.com/ferdiangunawan/rpi-stack/main/research/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/research/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How research Compares

Feature / AgentresearchStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Use when needing to understand requirements before implementation. Gathers context from Jira, Confluence, codebase, and docs. Produces research document with confidence assessment.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Research Skill

Conducts thorough research on requirements and codebase before implementation.

## When to Use

- Need to understand a Jira ticket or PRD before planning
- Exploring feasibility of a feature
- Gathering context about existing code patterns

## Agent Compatibility

- AskUserQuestion: use the tool in Claude Code; in Codex CLI, ask the user directly.
- Subagents/Task tool: use if available; otherwise run searches yourself in parallel.
- OUTPUT_DIR: `.claude/output` for Claude Code, `.codex/output` for Codex CLI.

---

## Phase 1: Input Gathering

**From Jira:**
```
Use mcp__atlassian__getJiraIssue to extract:
- Summary, Description, Acceptance Criteria, linked Confluence pages
```

**From Codebase (run in parallel when possible):**
```
Search 1: Similar features / patterns matching {feature keywords}
Search 2: Files likely affected by this feature (dependencies, related components)
Search 3 (complex features): Architecture for {related domain} — data flow, state management
```

After gathering:
1. Synthesize findings
2. Read AGENTS.md for project conventions
3. Identify existing components to reuse

---

## Phase 2: Requirement Analysis

For each requirement, identify:
- Type: functional / non-functional / constraint
- Priority: must-have / should-have / nice-to-have
- Complexity: low / medium / high
- Affected layers: presentation / application / domain / data

---

## Phase 3: Codebase Mapping

Map requirements to existing code:
- Similar features to reference
- Reusable components (widgets, services)
- API endpoints (existing vs. new needed)
- State management patterns in use

---

## Phase 4: Gap Analysis & Clarifications

Identify:
- Missing information or unclear requirements
- Edge cases with no specified behavior
- Technical unknowns or multiple valid approaches

**If there are ANY open questions, list them ALL and ask in a single batch before writing the output.**

> Do NOT write open questions and then answer them yourself with recommendations.
> Do NOT proceed with assumptions — ask.
> Document the user's answer before continuing.

Example:
```
Before I write the research document, I have a few questions:

1. [Requirement ambiguity] "{X}" could mean A or B — which is correct?
2. [Missing spec] The PRD doesn't say what should happen when {Y}. Options: A / B / skip for now?
3. [Technical choice] Implementing {Z} could use approach A (pros: X, cons: Y) or B (pros: Y, cons: X) — preference?
```

---

## Phase 5: Confidence Assessment

Rate confidence across dimensions (qualitative):

| Dimension | Confidence | Notes |
|-----------|------------|-------|
| Requirement Clarity | High / Medium / Low | |
| Codebase Understanding | High / Medium / Low | |
| Technical Feasibility | High / Medium / Low | |
| Scope Definition | High / Medium / Low | |
| Risk Identification | High / Medium / Low | |

**Overall:** High / Medium / Low — and whether to PROCEED / CLARIFY FURTHER / HALT.

---

## Output

Create `OUTPUT_DIR/research-{feature}.md`:

```markdown
# Research: {Feature Name}

## Metadata
- Date: {date}
- Source: {Jira/Confluence/Prompt}
- Confidence: {High / Medium / Low}

## Requirements Summary
{Parsed requirements with IDs: R1, R2, R3...}

## Codebase Analysis
{Related code, patterns to follow, reusable components}

## Technical Analysis
{Architecture impact, new code required, API needs}

## Clarified Questions
{Questions asked and user answers — or "None"}

## Risk Assessment
{Risks with likelihood/impact/mitigation}

## Confidence Assessment
{Per-dimension table, overall confidence, recommendation}

## Recommendation
{PROCEED / CLARIFY FURTHER / HALT}
```

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