assess（assess 搜索）

Assess

Comprehensive assessment skill for answering "is this good?" with structured evaluation, scoring, and actionable recommendations.

Quick Start

/ork:assess backend/app/services/auth.py
/ork:assess our caching strategy
/ork:assess --model=opus the current database schema
/ork:assess frontend/src/components/Dashboard

Argument Resolution

TARGET = "$ARGUMENTS"  # Full argument string, e.g., "backend/app/services/auth.py"
# $ARGUMENTS[0] is the first token (CC 2.1.59 indexed access)

# Model override detection (CC 2.1.72)
MODEL_OVERRIDE = None
for token in "$ARGUMENTS".split():
    if token.startswith("--model="):
        MODEL_OVERRIDE = token.split("=", 1)[1]  # "opus", "sonnet", "haiku"
        TARGET = TARGET.replace(token, "").strip()

Pass MODEL_OVERRIDE to all Agent() calls via model=MODEL_OVERRIDE when set. Accepts symbolic names (opus, sonnet, haiku) or full IDs (claude-opus-4-6) per CC 2.1.74.

STEP -1: MCP Probe + Resume Check

Load: Read("${CLAUDE_PLUGIN_ROOT}/skills/chain-patterns/references/mcp-detection.md")

# 1. Probe MCP servers (once at skill start)
ToolSearch(query="select:mcp__memory__search_nodes")

# 2. Store capabilities
Write(".claude/chain/capabilities.json", {
  "memory": probe_memory.found,
  "skill": "assess",
  "timestamp": now()
})

# 3. Check for resume
state = Read(".claude/chain/state.json")  # may not exist
if state.skill == "assess" and state.status == "in_progress":
    last_handoff = Read(f".claude/chain/{state.last_handoff}")

Phase Handoffs

STEP 0: Verify User Intent with AskUserQuestion

BEFORE creating tasks, clarify assessment dimensions:

AskUserQuestion(
  questions=[{
    "question": "What dimensions to assess?",
    "header": "Dimensions",
    "options": [
      {"label": "Full assessment (Recommended)", "description": "All dimensions: quality, maintainability, security, performance", "markdown": "```\nFull Assessment (7 phases)\n──────────────────────────\n  Dimensions scored 0-10:\n  ┌─────────────────────────────┐\n  │ Correctness      ████████░░ │\n  │ Maintainability  ██████░░░░ │\n  │ Security         █████████░ │\n  │ Performance      ███████░░░ │\n  │ Testability      ██████░░░░ │\n  │ Architecture     ████████░░ │\n  │ Documentation    █████░░░░░ │\n  └─────────────────────────────┘\n  + Pros/cons + alternatives\n  + Effort estimates + report\n  Agents: 4 parallel evaluators\n```"},
      {"label": "Code quality only", "description": "Readability, complexity, best practices", "markdown": "```\nCode Quality Focus\n──────────────────\n  Dimensions scored 0-10:\n  ┌─────────────────────────────┐\n  │ Correctness      ████████░░ │\n  │ Maintainability  ██████░░░░ │\n  │ Testability      ██████░░░░ │\n  └─────────────────────────────┘\n  Skip: security, performance\n  Agents: 1 code-quality-reviewer\n  Output: Score + best practice gaps\n```"},
      {"label": "Security focus", "description": "Vulnerabilities, attack surface, compliance", "markdown": "```\nSecurity Focus\n──────────────\n  ┌──────────────────────────┐\n  │ OWASP Top 10 check       │\n  │ Dependency CVE scan       │\n  │ Auth/AuthZ flow review    │\n  │ Data flow tracing         │\n  │ Secrets detection         │\n  └──────────────────────────┘\n  Agent: security-auditor\n  Output: Vuln list + severity\n          + remediation steps\n```"},
      {"label": "Quick score", "description": "Just give me a 0-10 score with brief notes", "markdown": "```\nQuick Score\n───────────\n  Single pass, ~2 min:\n\n  Read target ──▶ Score ──▶ Done\n                  7.2/10\n\n  Output:\n  ├── Composite score (0-10)\n  ├── Grade (A-F)\n  ├── 3 strengths\n  └── 3 improvements\n  No agents, no deep analysis\n```"}
    ],
    "multiSelect": false
  }]
)

Based on answer, adjust workflow:

• Full assessment: All 7 phases, parallel agents
• Code quality only: Skip security and performance phases
• Security focus: Prioritize security-auditor agent
• Quick score: Single pass, brief output

STEP 0b: Select Orchestration Mode

Load details: Read("${CLAUDE_SKILL_DIR}/references/orchestration-mode.md") for env var check logic, Agent Teams vs Task Tool comparison, and mode selection rules.

Task Management (CC 2.1.16)

# 1. Create main task IMMEDIATELY
TaskCreate(
  subject="Assess: {target}",
  description="Comprehensive evaluation with quality scores and recommendations",
  activeForm="Assessing {target}"
)

# 2. Create subtasks for each assessment phase
TaskCreate(subject="Understand target and gather context", activeForm="Understanding target")   # id=2
TaskCreate(subject="Discover scope and build file list", activeForm="Discovering scope")        # id=3
TaskCreate(subject="Rate quality across 7 dimensions", activeForm="Rating quality")             # id=4
TaskCreate(subject="Analyze pros and cons", activeForm="Analyzing pros/cons")                   # id=5
TaskCreate(subject="Compare alternatives", activeForm="Comparing alternatives")                 # id=6
TaskCreate(subject="Generate improvement suggestions", activeForm="Generating suggestions")     # id=7
TaskCreate(subject="Compile assessment report", activeForm="Compiling report")                  # id=8

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Scope needs target understanding
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Rating needs scoped file list
TaskUpdate(taskId="5", addBlockedBy=["4"])  # Pros/cons needs quality scores
TaskUpdate(taskId="6", addBlockedBy=["4"])  # Alternatives need quality scores
TaskUpdate(taskId="7", addBlockedBy=["5", "6"])  # Suggestions need analysis
TaskUpdate(taskId="8", addBlockedBy=["7"])  # Report needs suggestions

# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2")  # Verify blockedBy is empty

# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

What This Skill Answers

Workflow Overview

Phase 1: Target Understanding

Identify what's being assessed and gather context:

# PARALLEL - Gather context
Read(file_path="$ARGUMENTS[0]")  # If file path
Grep(pattern="$ARGUMENTS[0]", output_mode="files_with_matches")
mcp__memory__search_nodes(query="$ARGUMENTS[0]")  # Past decisions

Phase 1.5: Scope Discovery

Load Read("${CLAUDE_SKILL_DIR}/references/scope-discovery.md") for the full file discovery, limit application (MAX 30 files), and sampling priority logic. Always include the scoped file list in every agent prompt.

Progressive Output (CC 2.1.76)

Output results incrementally as each evaluation phase completes:

For Phase 2 parallel agents, show each dimension's score as soon as the evaluating agent returns — don't wait for all 4 agents. If any dimension scores below 4/10, flag it immediately as a priority concern requiring user attention.

Phase 2: Quality Rating (7 Dimensions)

Rate each dimension 0-10 with weighted composite score. Load Read("${CLAUDE_PLUGIN_ROOT}/skills/quality-gates/references/unified-scoring-framework.md") for dimensions, weights, grade interpretation, and per-dimension criteria. Load Read("${CLAUDE_SKILL_DIR}/references/quality-model.md") for assess-specific overrides.

Load Read("${CLAUDE_SKILL_DIR}/references/agent-spawn-definitions.md") for Task Tool mode spawn patterns and Agent Teams alternative.

Composite Score: Weighted average of all 7 dimensions (see quality-model.md).

Phases 3-7: Analysis, Comparison & Report

Load Read("${CLAUDE_SKILL_DIR}/references/phase-templates.md") for output templates for pros/cons, alternatives, improvements, effort, and the final report.

See also: Read("${CLAUDE_SKILL_DIR}/references/alternative-analysis.md") | Read("${CLAUDE_SKILL_DIR}/references/improvement-prioritization.md")

Grade Interpretation

Load Read("${CLAUDE_PLUGIN_ROOT}/skills/quality-gates/references/unified-scoring-framework.md") for grade thresholds and scoring criteria.

Key Decisions

Rules Quick Reference

Related Skills

• ork:verify - Post-implementation verification
• ork:code-review-playbook - Code review patterns
• ork:quality-gates - Task complexity assessment, gate patterns

Version: 1.4.0 (March 2026) — Added progressive output for incremental evaluation results