Session logs document agent work sessions and must comply with RFC 2119 requirements:

• MUST: Required for compliance (blocking failures)
• SHOULD: Recommended practices (warnings)
• MAY: Optional enhancements

See .agents/SESSION-PROTOCOL.md for full specification.

=== Session Validation ===
File: /home/runner/work/ai-agents/ai-agents/.agents/sessions/2026-04-27-session-1760-1798-grill-me-requirements-interview.json

[FAIL] Validation errors:

• Incomplete MUST: sessionEnd.serenaMemoryUpdated

This validation uses deterministic script analysis instead of AI:

• ✅ Zero tokens consumed (previously 300K-900K per debug cycle)
• ✅ Instant feedback - see exact failures in this summary
• ✅ No artifact downloads needed to diagnose issues
• ✅ 10x-100x faster debugging

Powered by validate_session_json.py

This validation ensures your implementation matches the specifications:

• Requirements Traceability: Verifies PR changes map to spec requirements
• Implementation Completeness: Checks all requirements are addressed

Requirements Coverage Matrix

Summary

• Total Requirements: 15
• Covered: 14 (93%)
• Partially Covered: 1 (7%)
• Not Covered: 0 (0%)

Gaps

1. REQ-011 (Option C partial): Issue recommended "Option C — Both" where the skill is created AND analyst agent gets behavior wired into its default flow. The PR creates the skill and wires it to /spec, but does not modify the analyst agent (analyst.agent.md) to invoke the skill as part of its standard workflow. The /spec integration achieves the primary goal; analyst enhancement could be a follow-up.

[!TIP]
VERDICT: PASS
All core requirements from Issue #1798 are covered. The implementation delivers the grill-me skill with design tree traversal, recommended-answer discipline, codebase-first lookup, structured PRD output, and /spec integration. The partial gap (analyst agent enhancement) is an optional enhancement per Option C, not a blocking requirement. The PR explicitly chose a minimal-blast-radius approach by wiring via /spec only.

Acceptance Criteria Checklist

Based on Issue #1798, the acceptance criteria are:

• AC1: Grill-me pattern implementation - SATISFIED

  • Evidence: SKILL.md lines 15-17 implement adversarial interview behavior; lines 43-50 define the Method with "walk the design tree branch by branch"

• AC2: Design tree traversal (Brooks' concept) - SATISFIED

  • Evidence: SKILL.md lines 44-47 define depth-first branch traversal; lines 63-71 enumerate 7 branches (user stories, data model, integrations, failure modes, security, observability, scope boundaries)

• AC3: "For each question, provide your recommended answer" - SATISFIED

  • Evidence: SKILL.md line 46: "For every question, propose your recommended answer. Cite the source: code path, ADR, prior art, or stated assumption."

• AC4: Codebase-first principle - SATISFIED

  • Evidence: SKILL.md line 47: "If the codebase can answer it, answer it. Grep before you ask. Cite the file and line."

• AC5: Structured PRD output - SATISFIED

  • Evidence: SKILL.md lines 92-94 define output format with Problem, User stories, Data model, Integrations, Failure modes, Security, Observability, Acceptance criteria, Out of scope, Deferred, Open questions

• AC6: /spec integration - SATISFIED

  • Evidence: spec.md line 16 inserts Skill(skill="requirements-interview") as step 2, before complexity classification

• AC7: Reusable skill (Option C recommendation) - SATISFIED

  • Evidence: SKILL.md line 7 user-invocable: true; triggers in lines 8-12 allow standalone invocation by orchestrator, architect, analyst

• AC8: Resolve dependencies between decisions - SATISFIED

  • Evidence: SKILL.md line 45: "Resolve dependencies before siblings. A storage decision constrains the consistency model; ask the storage question first."

• AC9: Tests guard the prompt contract - SATISFIED

  • Evidence: test_skill_contract.py provides 10 tests covering frontmatter, required sections, grill-me reference, recommended-answer discipline, codebase-first principle

Missing Functionality

None identified. All explicit requirements from Issue #1798 are addressed.

Edge Cases Not Covered

1. Analyst agent enhancement (Option A) - Not implemented. Issue recommended Option C (skill + analyst integration). The skill is standalone; analyst agent file was not modified. This is acceptable since Option C states "analyst agent invokes it as part of its standard workflow" and /spec already invokes the skill before delegating to analyst.

2. Interview transcript persistence - SKILL.md specifies output at .agents/specs/interviews/INTERVIEW-<slug>.md but no directory or template exists. This is acceptable for a prompt-only skill; the agent creates the file on invocation.

Implementation Quality

• Completeness: 9/9 acceptance criteria satisfied (100%)
• Quality: Implementation exceeds minimum requirements. Branch checklist covers 7 areas vs. 4 in the issue. Anti-patterns section guards against common failure modes. Quality gates define completion criteria.

[!TIP]
VERDICT: PASS
Implementation satisfies all 9 acceptance criteria from Issue #1798. The skill correctly implements the grill-me pattern with design tree traversal, recommended-answer discipline, and codebase-first lookup. Integration into /spec as step 2 ensures alignment-before-generation. Test coverage guards the prompt contract.

This PR was reviewed by six AI agents in parallel, analyzing different aspects of the changes:

• Security Agent: Scans for vulnerabilities, secrets exposure, and security anti-patterns
• QA Agent: Evaluates test coverage, error handling, and code quality
• Analyst Agent: Assesses code quality, impact analysis, and maintainability
• Architect Agent: Reviews design patterns, system boundaries, and architectural concerns
• DevOps Agent: Evaluates CI/CD, build pipelines, and infrastructure changes
• Roadmap Agent: Assesses strategic alignment, feature scope, and user value

Now I have reviewed all the changed files. Let me compile my security analysis.

Security Review: PR #1812

PR Type Classification

Findings

Analysis Summary

PROMPT Files (SKILL.md, spec.md)

• [PASS] No dynamic code execution patterns
• [PASS] allowed-tools declares least-privilege scope: Read, Glob, Grep, Write (no shell/bash)
• [PASS] No user input directly passed to system prompts without structure
• [PASS] Tool invocations use typed delegation (Task(subagent_type=...), Skill(skill=...))
• [PASS] No external URL fetching or arbitrary code execution

CODE File (test_skill_contract.py)

• [PASS] Read-only test file; reads local SKILL.md only
• [PASS] No subprocess calls or shell execution
• [PASS] No network operations
• [PASS] Uses standard pytest patterns with Path.read_text()
• [PASS] Imports from internal validation module only

CONFIG Files (JSON)

• [PASS] No secrets or credentials present
• [PASS] marketplace.json increments skill count only (66)
• [PASS] Session logs contain no sensitive data (timestamps, filenames, protocol compliance)

DOCS File (Serena memory)

• [PASS] Documentation only; no executable content
• [PASS] External URLs are reference links, not fetched

Recommendations

None required. The PR follows security best practices:

1. Skill declares minimal allowed-tools (no shell access)
2. Test file is isolated and read-only
3. No hardcoded secrets or credentials
4. No injection vectors in prompt templates

Verdict

VERDICT: PASS
MESSAGE: No security vulnerabilities found. Skill uses least-privilege tool scope, no shell access, no secrets exposure.

{
  "verdict": "PASS",
  "message": "No security vulnerabilities found. Skill uses least-privilege tool scope, no shell access, no secrets exposure.",
  "agent": "security",
  "timestamp": "2026-04-29T03:42:29.442Z",
  "findings": []
}

Now I have a complete picture of the PR. Let me produce my QA review verdict.

QA Review: PR #1812

PR TYPE: MIXED

Test Coverage Assessment

Quality Concerns

Regression Risk Assessment

• Risk Level: Low
• Affected Components: /spec command workflow, new skill (no existing functionality modified beyond spec.md)
• Breaking Changes: None. /spec gains new step 2 behavior but existing usage patterns preserved.
• Required Testing: Contract tests validate SKILL.md structure; pytest 7362 passed confirms no regression.

Code Quality Verification

Pre-Executed Test Results

• pytest: 7362 passed, 3 skipped, 43 warnings in 44.17s
• Status: [PASS]
• Skill-specific tests: 10/10 contract tests in test_skill_contract.py passed (confirmed in PR description)

Fail-Safe Pattern Verification

Test-Implementation Alignment

Coverage: 10/10 acceptance criteria covered (100%)

VERDICT: PASS
MESSAGE: New skill with comprehensive contract tests (10/10) and pytest suite (7362 passed); no blocking issues.

PR TYPE: MIXED (CODE + DOCS + CONFIG + SESSION ARTIFACTS)

EVIDENCE:
- Tests found: 10 contract tests for 1 new skill (test_skill_contract.py)
- Test execution: PASS (7362 passed, 3 skipped, 43 warnings in 44.17s)
- Edge cases: Covered (null/empty fields, pattern mismatches, length limits, XML injection)
- Error handling: Tested (frontmatter parsing validation)
- Blocking issues: 0

{
  "verdict": "PASS",
  "message": "New skill with comprehensive contract tests (10/10) and pytest suite (7362 passed); no blocking issues.",
  "agent": "qa",
  "timestamp": "2026-04-29T03:43:38.154Z",
  "findings": [
    {
      "severity": "low",
      "category": "code-quality",
      "description": "Test imports private validator patterns (_NAME_PATTERN, _XML_TAG_PATTERN)",
      "location": "test_skill_contract.py:22-28",
      "recommendation": "Documented as deferred in Serena memory; track for follow-up refactor"
    },
    {
      "severity": "low",
      "category": "test-coverage",
      "description": "DESCRIPTION_MAX=1024 differs from 200-char memory guideline for short descriptions",
      "location": "test_skill_contract.py:34",
      "recommendation": "Acceptable; validates against canonical validator maximum, not style preference"
    }
  ]
}

Now I have enough context to perform the analysis. Let me compile the findings.

Analysis: PR #1812 - Requirements Interview Skill (grill-me pattern)

Code Quality Score

Overall: 4.5/5

Impact Assessment

• Scope: Module-wide (touches /spec command, adds new skill, updates marketplace count)
• Risk Level: Low
• Affected Components:
  • .claude/commands/spec.md (step 2 and step 6 modified)
  • .claude/skills/requirements-interview/ (new skill)
  • .claude-plugin/marketplace.json (count bump: 66 skills)
  • .serena/memories/skills/ (new memory entry)
  • .agents/sessions/ and .agents/memory/episodes/ (session artifacts)

Findings

Recommendations

1. Future PR: Promote validator private constants (_NAME_PATTERN, _XML_TAG_PATTERN) to public exports and import them in tests to eliminate duplication (already tracked in memory as deferred work).
2. Consider: Shortening the frontmatter description to under 200 chars per steering guidance, though not blocking.
3. Session log: Fix episode outcome to "success" since validations passed and commits landed.

Verdict

The skill is well-structured, follows established patterns, and includes 10 contract tests. The /spec integration correctly wires the skill at step 2 and hands the PRD to spec-generator at step 6. Marketplace count is updated. Tests import from the canonical validator to prevent drift on core validation logic.

The test constant duplication is a known trade-off documented in the Serena memory entry and PR notes. No architectural violations or blocking issues.

VERDICT: PASS
MESSAGE: Clean skill implementation with comprehensive tests. Minor maintainability debt (test constant duplication) is tracked for follow-up.

{
  "verdict": "PASS",
  "message": "Clean skill implementation with comprehensive tests. Minor maintainability debt (test constant duplication) is tracked for follow-up.",
  "agent": "analyst",
  "timestamp": "2026-04-29T03:42:34.235Z",
  "findings": [
    {
      "severity": "low",
      "category": "maintainability",
      "description": "Test file duplicates VALID_MODEL_ALIASES, _NAME_PATTERN, _XML_TAG_PATTERN constants from validator. Future drift risk if validator changes.",
      "location": "tests/test_skill_contract.py:22-28",
      "recommendation": "Future PR: promote validator private constants to public exports and import them in tests."
    },
    {
      "severity": "low",
      "category": "documentation",
      "description": "Description length (289 chars) exceeds 200-char best practice from steering guidance, though under 1024-char hard limit.",
      "location": "SKILL.md:4",
      "recommendation": "Consider shortening description in future iteration."
    },
    {
      "severity": "low",
      "category": "consistency",
      "description": "Session log episode outcome marked 'failure' but metrics show 8 commits and all validations passed.",
      "location": "episode-2026-04-27-session-1761.json:5",
      "recommendation": "Fix episode outcome to 'success'."
    },
    {
      "severity": "low",
      "category": "documentation",
      "description": "Analyst-agent integration deferred. Documented in PR notes and Serena memory.",
      "location": "PR description",
      "recommendation": "Track in separate issue per PR notes."
    }
  ]
}

Now I have enough context to perform the architectural review.

Architect Review: PR #1812 — requirements-interview skill

Design Quality Assessment

Overall Design Score: 4.6/5

Architectural Concerns

Breaking Change Assessment

• Breaking Changes: No
• Impact Scope: None
• Migration Required: No
• Migration Path: N/A

Technical Debt Analysis

• Debt Added: Low (test imports from canonical validator reduce duplication)
• Debt Reduced: Medium (structured PRD eliminates ad-hoc requirements gathering)
• Net Impact: Improved

ADR Assessment

• ADR Required: No
• Decisions Identified: None requiring new ADR. The skill pattern is established (ADR-030). EARS syntax is documented (ADR-032). /spec wiring is a feature enhancement, not an architectural change.
• Existing ADR: ADR-030 (skills-pattern-superiority), ADR-032 (ears-requirements-syntax)
• Recommendation: N/A

Recommendations

1. Shorten frontmatter description to comply with 200-char guideline. Example: Adversarial requirements interview using the grill-me pattern. Walks the design tree, proposes answers, greps first, emits structured PRD.
2. Split memory file into follow-up PR per repository memory guidance (Serena memory changes separate from skill changes).

Verdict

VERDICT: PASS
MESSAGE: Well-structured skill with clear boundaries. Two low-severity findings are non-blocking.

{
  "verdict": "PASS",
  "message": "Well-structured skill with clear boundaries. Two low-severity findings are non-blocking.",
  "agent": "architect",
  "timestamp": "2026-04-29T03:42:25.869Z",
  "findings": [
    {
      "severity": "low",
      "category": "design-pattern",
      "description": "Frontmatter description exceeds 200-char steering guideline (340 chars)",
      "location": ".claude/skills/requirements-interview/SKILL.md:4",
      "recommendation": "Shorten to under 200 chars per .agents/steering/claude-skills.md"
    },
    {
      "severity": "low",
      "category": "boundaries",
      "description": "Serena memory file bundled with skill PR",
      "location": ".serena/memories/skills/skills-requirements-interview-pattern.md",
      "recommendation": "Move memory changes to separate PR per repository guidance"
    }
  ]
}

Let me check the local repository state and files mentioned in the PR description.

Based on my review, I now have a complete picture of this PR. Let me compile the DevOps review.

DevOps Review: PR #1812

PR Scope Detection

Assessment: This PR contains no workflow files, no shell scripts, no GitHub Actions changes. It is primarily a skill addition (documentation + tests) with a marketplace count bump.

Pipeline Impact Assessment

CI/CD Quality Checks

Findings

Template Assessment

• PR Template: Adequate (PR follows template, includes test plan)
• Issue Templates: Not modified
• Template Issues: None

Automation Opportunities

This PR follows existing patterns. The skill tests use the existing pytest infrastructure. The marketplace count validation script (build/scripts/validate_marketplace_counts.py) already validates skill counts in CI.

Recommendations

1. No DevOps changes required for this PR.
2. The test file imports from scripts.validation.skill_frontmatter, which is already in the existing test infrastructure.

Verdict

VERDICT: PASS
MESSAGE: DOCS/CONFIG-only PR with no CI/CD, workflow, or infrastructure changes. New pytest tests integrate with existing infrastructure. Marketplace count properly bumped.

{
  "verdict": "PASS",
  "message": "DOCS/CONFIG-only PR with no CI/CD, workflow, or infrastructure changes. New pytest tests integrate with existing infrastructure.",
  "agent": "devops",
  "timestamp": "2026-04-29T03:42:26.166Z",
  "findings": [
    {
      "severity": "low",
      "category": "automation",
      "description": "Marketplace skill count bumped from 65 to 66",
      "location": ".claude-plugin/marketplace.json:21",
      "recommendation": "Verified correct; count matches skill directory enumeration"
    }
  ]
}

I have reviewed the key files. Now I'll provide the strategic alignment assessment.

Roadmap Review: PR #1812

Strategic Alignment Assessment

Feature Completeness

• Scope Assessment: Right-sized
• Ship Ready: Yes
• MVP Complete: Yes
• Enhancement Opportunities:
  • Analyst agent auto-invocation (explicitly deferred, tracked)
  • Session collision fix (explicitly deferred, tracked)

Impact Analysis

RICE Score (Estimated)

KANO Classification

Must-Be (becoming): Requirements validation is expected in mature agent systems. Absence creates dissatisfaction when implementation diverges from intent. This skill converts what was an implicit expectation into an explicit gate.

Concerns

Recommendations

1. Ship as-is. The skill addresses a documented pain point with quantified impact and minimal implementation cost.
2. Track the deferred analyst-agent integration in a follow-up issue to complete Option C from feat(agents): Add adversarial requirements interview to /spec command (grill-me pattern) #1798.
3. Monitor adoption: Track /spec invocations pre/post to validate the 50% review time claim.

Verdict

VERDICT: PASS
MESSAGE: High-impact, right-sized skill that closes the alignment-before-generation gap. Research-backed, minimal cost, strong integration with /spec workflow.

{
  "verdict": "PASS",
  "message": "High-impact requirements-interview skill closes alignment-before-generation gap with research-backed methodology and minimal implementation cost",
  "agent": "roadmap",
  "timestamp": "2026-04-29T03:42:30.789Z",
  "findings": [
    {
      "severity": "low",
      "category": "scope",
      "description": "Serena memory file included in skill PR",
      "location": ".serena/memories/skills/skills-requirements-interview-pattern.md",
      "recommendation": "Repository convention prefers memory changes in separate PRs. Acceptable for this PR but note for future."
    },
    {
      "severity": "low",
      "category": "documentation",
      "description": "Deferred work items properly tracked",
      "location": "PR description Notes section",
      "recommendation": "Create follow-up issue for analyst-agent auto-invocation to complete #1798 Option C."
    }
  ]
}

Walkthrough

This PR introduces a mandatory adversarial requirements-interview step into the /spec workflow that generates a structured PRD before proceeding to complexity classification and specification generation. It adds a new reusable requirements-interview skill that walks a design tree and records decisions with EARS acceptance criteria, along with validation tests and updated workflow documentation.

Changes

Sequence Diagram(s)

sequenceDiagram
    actor User
    participant Analyst as Analyst Agent<br/>(spec command)
    participant Interview as requirements-interview<br/>skill
    participant Codebase as Codebase
    participant SpecGen as spec-generator<br/>stage

    User->>Analyst: /spec [feature request]
    Analyst->>Interview: invoke (grill-me pattern)
    
    Interview->>Interview: restate & confirm problem
    Interview->>Interview: construct design tree
    
    loop for each branch (depth-first)
        Interview->>Interview: walk to leaf decision
        Interview->>Codebase: grep/explore for answer
        alt answer found in codebase
            Codebase-->>Interview: evidence
            Interview->>Interview: recommend answer from code
        else unknown answer
            Interview->>User: ask decision question
            User-->>Interview: provide answer
        end
        Interview->>Interview: record (CONFIRMED|OVERRIDDEN|DEFERRED|OUT_OF_SCOPE)
    end
    
    Interview->>Interview: finalize PRD (user stories,<br/>data model, integrations,<br/>failure modes, EARS criteria)
    Interview-->>SpecGen: structured PRD (unchanged)
    
    SpecGen->>SpecGen: compute complexity tier<br/>(driven by PRD)
    SpecGen->>SpecGen: conditional CVA analysis
    SpecGen->>SpecGen: formalize REQ/DESIGN/TASK<br/>artifacts
    SpecGen-->>Analyst: durable artifacts
    Analyst-->>User: spec output

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~25 minutes

Suggested reviewers

• rjmurillo

❌ Failed checks (1 warning)

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}