Spec-Driven Research

Capture and persist research findings across sessions in structured, queryable documents through a 7-phase workflow with structural anti-skip enforcement.

Context files are THE LAW: tech-stack.md, source-tree.md, dependencies.md, coding-standards.md, architecture-constraints.md, anti-patterns.md

If ambiguous or conflicts detected: HALT and use AskUserQuestion

Execution Model

This skill expands inline. After invocation, execute Phase 00 Initialization immediately. Do not wait passively, ask permission, or offer execution options.

Self-Check (if ANY box is true = VIOLATION):

• Stopping to ask about token budget
• Stopping to offer execution options
• Waiting passively for user to say "go"
• Asking "should I execute this?"
• Skipping a phase because it "seems simple"
• Combining multiple phases into one
• Summarizing instead of loading a reference file
• Skipping verification because "I already wrote the file"

IF any box checked: EXECUTION MODEL VIOLATION. Go directly to Phase 00 Initialization now.

Anti-Skip Enforcement Contract

This skill enforces 4 independent anti-skip layers. ALL FOUR must fail for a step to be skipped:

1. Per-phase reference loading - Each phase loads its reference files fresh via Read(). NOT consolidated. Prevents "already covered" rationalization.
2. Binary CLI gates - devforgeai-validate phase-check/phase-complete at phase boundaries. Cannot be forged by LLM.
3. Checkpoint-based state tracking - Phase completion verified by checking checkpoint JSON data keys and current_phase field.
4. Artifact verification - Checkpoint JSON existence checked via Glob(), research files verified on disk, index entries verified via Grep().

Execute-Verify-Record Pattern: Every mandatory step in every phase file has three parts:

• EXECUTE: The exact action to perform (Read, Write, AskUserQuestion, Task, WebSearch, WebFetch, Grep, Glob)
• VERIFY: How to confirm the action happened (file exists, content contains expected text, data key populated)
• RECORD: Update checkpoint JSON with captured data; call devforgeai-validate phase-record

Token Optimization Bias is PROHIBITED. Do not skip, compress, or shortcut any step. Every phase step exists because a previous failure proved it necessary.

Purpose

This skill serves as the knowledge persistence layer for DevForgeAI. It transforms ephemeral research (web searches, competitive analysis, technology evaluations) into persistent, structured documents that survive session restarts.

Core Philosophy

"Research Once, Reference Forever" - Capture research in structured documents that survive context window clears and are queryable and reusable.

"Evidence-Based Decision Making" - All recommendations cite research with bidirectional traceability to epics, stories, and ADRs.

"Knowledge Compounds Over Time" - Research index grows with each session, related research is cross-referenced, and staleness tracking ensures currency.

When to Use This Skill

Trigger Scenarios

• Competitive analysis (AWS Kiro, Cursor, etc.)
• Technology evaluation (Treelint, new libraries)
• Market research (developer frustrations, trends)
• Integration planning (external tools, APIs)
• Architecture research (patterns, best practices)
• User runs /research command
• User mentions research capture or knowledge persistence

When NOT to Use

• Documenting code implementation (use stories)
• Recording decisions (use ADRs)
• Tracking bugs/issues (use RCA)

Parameter Extraction

Extract from command arguments:

State Persistence

• Checkpoint: devforgeai/workflows/${RESEARCH_ID}-phase-state.json
• References: references/ (self-contained within this skill)
• Templates: assets/templates/ (self-contained within this skill)
• Research Output: devforgeai/specs/research/
• Research Index: devforgeai/specs/research/research-index.md

Phase 00: Initialization [INLINE - Bootstraps State]

This phase runs inline because it creates the state that all other phases depend on. It CANNOT be skipped.

Step 0.1: Parse Arguments

Extract arguments from command invocation. Determine mode:

IF args contain "--resume RESEARCH-NNN":
  mode = "resume"
  RESUME_ID = extract_id(args)
ELIF args contain "--search":
  mode = "search"
  SEARCH_QUERY = extract_query(args)
  GOTO Step 0.2 (Search Mode)
ELIF args contain "--list":
  mode = "list"
  CATEGORY_FILTER = args.get("--category", null)
  GOTO Step 0.3 (List Mode)
ELSE:
  mode = "new"
  TOPIC = extract_topic(args)

Step 0.2: Search Mode Branch [CONDITIONAL]

Triggered by: --search query

Read("src/claude/skills/spec-driven-research/references/search-list-modes.md")

Follow search mode procedure from reference file. After displaying results, EXIT skill. No further phases execute.

Step 0.3: List Mode Branch [CONDITIONAL]

Triggered by: --list or --category type

Read("src/claude/skills/spec-driven-research/references/search-list-modes.md")

Follow list mode procedure from reference file. After displaying table, EXIT skill. No further phases execute.

Step 0.4: Resume Detection

IF mode == "resume":
  checkpoint_file = Glob(pattern=f"devforgeai/workflows/{RESUME_ID}-phase-state.json")
  IF checkpoint_file found:
    Read the checkpoint file
    Restore state from checkpoint
    Set CURRENT_PHASE from checkpoint.progress.current_phase
    GOTO Phase Orchestration Loop at CURRENT_PHASE
  ELSE:
    # Resume by loading existing research doc
    research_files = Glob(pattern=f"devforgeai/specs/research/{RESUME_ID}-*.research.md")
    IF research_files found:
      Read the research document
      RESEARCH_ID = RESUME_ID
      mode = "expand"
      Continue to Step 0.5
    ELSE:
      HALT -- "Research {RESUME_ID} not found"

# Check for existing in-progress checkpoint (new mode)
existing_checkpoints = Glob(pattern="devforgeai/workflows/RESEARCH-*-phase-state.json")
IF existing_checkpoints with status "in_progress":
  AskUserQuestion:
    Question: "Found existing research session in progress. Resume or start fresh?"
    Header: "Resume"
    Options:
      - label: "Resume session"
        description: "Continue from last checkpoint"
      - label: "Start fresh"
        description: "Begin new research session"
  IF "Resume": Restore state, GOTO Phase Orchestration Loop at CURRENT_PHASE

Step 0.5: Generate Research ID

existing_files = Glob("devforgeai/specs/research/RESEARCH-*.research.md")

# Extract IDs (gap-aware)
ids = []
FOR each file in existing_files:
  match = regex("RESEARCH-(\d{3})", basename(file))
  IF match: ids.append(int(match.group(1)))

next_id = max(ids) + 1 IF ids ELSE 1
RESEARCH_ID = f"RESEARCH-{next_id:03d}"

Step 0.6: CLI Initialization

source .venv/bin/activate && devforgeai-validate phase-init ${RESEARCH_ID} --workflow=research --project-root=. 2>&1

Step 0.7: Create Initial Checkpoint

{
  "checkpoint_version": "1.0",
  "research_id": "RESEARCH-NNN",
  "workflow": "research",
  "created_at": "ISO 8601",
  "updated_at": "ISO 8601",
  "status": "in_progress",
  "input": {
    "mode": "new|resume|expand",
    "topic": null,
    "category": null,
    "questions": [],
    "resume_id": null
  },
  "progress": {
    "current_phase": 0,
    "phases_completed": [],
    "total_steps_completed": 0
  },
  "phases": {
    "01": { "status": "pending", "steps_completed": [] },
    "02": { "status": "pending", "steps_completed": [] },
    "03": { "status": "pending", "steps_completed": [] },
    "04": { "status": "pending", "steps_completed": [] },
    "05": { "status": "pending", "steps_completed": [] },
    "06": { "status": "pending", "steps_completed": [] }
  },
  "output": {
    "research_file_path": null,
    "assets_folder": null,
    "index_updated": false,
    "epics_linked": [],
    "adr_created": false,
    "error": null
  }
}

Write to devforgeai/workflows/${RESEARCH_ID}-phase-state.json

VERIFY: Glob(pattern="devforgeai/workflows/${RESEARCH_ID}-phase-state.json") IF not found: HALT -- "Initial checkpoint was NOT created."

Step 0.8: Display Session Banner

Display:
"------------------------------------------------------------
  DevForgeAI Research Session
------------------------------------------------------------

Research ID: ${RESEARCH_ID}
Mode: ${mode}
Topic: ${TOPIC || 'To be defined in Phase 01'}

Phases: 7 (Initialization > Topic Definition > Research Execution > Findings Synthesis > Documentation > Cross-Reference > Completion)
------------------------------------------------------------"

Set CURRENT_PHASE = 1.

Phase Orchestration Loop

FOR phase_num in range(CURRENT_PHASE, 7):  # Phases 01-06

    1. ENTRY GATE: devforgeai-validate phase-check ${RESEARCH_ID} --workflow=research --from={prev} --to={phase_id} --project-root=.
       IF exit != 0 AND exit != 127: HALT

    2. LOAD: Read(file_path="src/claude/skills/spec-driven-research/phases/{phase_files[phase_num]}")
       Load the phase file FRESH. Do NOT skip this step. Do NOT rely on memory of previous reads.

    3. REFERENCE: Read the phase's reference files as specified in the phase Contract section.
       References are in references/ (self-contained within this skill).
       Load ALL listed references. Do not skip any.

    4. EXECUTE: Follow EVERY step in the phase file using EXECUTE-VERIFY-RECORD triplets.

    5. EXIT GATE: devforgeai-validate phase-complete ${RESEARCH_ID} --workflow=research --phase={phase_id} --checkpoint-passed --project-root=.
       IF exit != 0 AND exit != 127: HALT

    6. CHECKPOINT: Update checkpoint JSON with phase completion.
       Write updated checkpoint to disk.
       Verify write via Glob().

Phase Table

Required Subagents Per Phase

All other phases: No subagents required. Direct tool calls (Read, Write, Glob, Grep, AskUserQuestion, WebSearch, WebFetch).

Deviation Protocol: Any skip requires explicit user consent via AskUserQuestion.

Workflow Completion Validation

completed_count = len(checkpoint.progress.phases_completed)
IF completed_count < 6:
    HALT "WORKFLOW INCOMPLETE - {completed_count}/6 phases completed"
IF completed_count == 6:
    Display "All 6 phases completed - Workflow validation passed"
    Update checkpoint status to "completed"

Success Criteria

Complete research generated with:

• Valid research ID (RESEARCH-NNN format)
• Research topic defined with category
• Research questions captured
• Findings with evidence and citations
• Recommendations ranked by priority
• Sources with credibility assessment
• Research document written to devforgeai/specs/research/
• Research index updated
• Cross-references created (if applicable)
• Checkpoint updated to "completed"

Reference Files Inventory

Load these on-demand during workflow execution:

Phase Files (7 files in phases/)

Reference Files (6 files in references/)

• parameter-extraction.md - Argument parsing, category code mapping
• research-workflow.md - Core workflow patterns, ID generation, slug generation, frontmatter schema
• citation-standards.md - Source citing formats, evidence quoting, confidence levels
• search-strategies.md - Fallback search strategies by category, search optimization tips
• internet-sleuth-delegation.md - Subagent contract, category-to-mode mapping, Task() template
• search-list-modes.md - Search and list mode handling (conditional branches in Phase 00)

Assets (2 templates in assets/templates/)

• research-template.md - Research document template (YAML + markdown)
• sleuth-report-template.md - Internet-sleuth 9-section report template with YAML frontmatter (absorbed from internet-sleuth-integration per ADR-045)

Sleuth Methodology References (5 files in references/sleuth-methodology/)

• research-principles.md - Core research principles, evidence standards, framework integration (always loaded by internet-sleuth)
• discovery-mode-methodology.md - Discovery mode workflow: feasibility assessment, alternatives identification (conditional)
• repository-archaeology-guide.md - GitHub mining: search strategy, quality scoring, pattern extraction (conditional)
• competitive-analysis-patterns.md - Market analysis: SWOT, positioning maps, pricing analysis (conditional)
• skill-coordination-patterns.md - Task invocation patterns for internet-sleuth coordination by other skills

Note: Sleuth methodology files absorbed from internet-sleuth-integration per ADR-045. Loaded by the internet-sleuth subagent via progressive disclosure.

Total: 7 phase files + 6 reference files + 5 sleuth methodology files + 2 templates = 20 files + SKILL.md = 21 files

Best Practices

1. Provide clear research topic - Specific topics yield better research than broad ones
2. Select accurate category - Category determines research strategy and internet-sleuth mode
3. Define research questions upfront - Questions guide the research focus
4. Review findings for accuracy - AI research requires human verification
5. Link to epics/ADRs when relevant - Bidirectional traceability strengthens the knowledge base

See phase-specific reference files for detailed procedures.