Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use when evaluating code, designs, architectures, or comparing alternative approaches.
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Assesses and rates quality 0-10 across multiple dimensions (correctness, maintainability, security, performance, testability, simplicity) with pros/cons analysis. Compares against project conventions and prior decisions from memory. Produces structured evaluation reports with actionable improvement suggestions. Use when evaluating code, designs, architectures, or comparing alternative approaches.
{"keywords":["assess","asses","rate","evaluate","grade","score","compare","how good","how bad","red flags","trade-offs","pros and cons","good enough"],"examples":["rate this code from 0 to 10","is this approach good enough for production?","evaluate the trade-offs between Redis vs Postgres"],"anti-triggers":["fix","implement","build","test","commit","review pr","explore"]}
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
Effort levels (CC 2.1.111+ adds xhigh)
Effort
Behavior
low / medium
Subset of dimensions, faster turnaround
high (default)
All six dimensions with pros/cons
xhigh (Opus 4.7 only)
All six dimensions + one additional assessor pass focused on uncertainty/caveats; emits confidence per dimension
xhigh silently falls back to high on non-Opus-4.7 models. /ork:doctor warns when xhigh is used without Opus 4.7.
Argument Resolution
TARGET = "$ARGUMENTS"# Full argument string, e.g., "backend/app/services/auth.py"# $ARGUMENTS[0] is the first token (CC 2.1.59 indexed access)# Model override detection (CC 2.1.72)
MODEL_OVERRIDE = Nonefor token in"$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.
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
Question
How It's Answered
"Is this good?"
Quality score 0-10 with reasoning
"What are the trade-offs?"
Structured pros/cons list
"Should we change this?"
Improvement suggestions with effort
"What are the alternatives?"
Comparison with scores
"Where should we focus?"
Prioritized recommendations
Workflow Overview
Phase
Activities
Output
1. Target Understanding
Read code/design, identify scope
Context summary
1.5. Scope Discovery
Build bounded file list
Scoped file list
2. Quality Rating
7-dimension scoring (0-10)
Scores with reasoning
3. Pros/Cons Analysis
Strengths and weaknesses
Balanced evaluation
4. Alternative Comparison
Score alternatives
Comparison matrix
5. Improvement Suggestions
Actionable recommendations
Prioritized list
6. Effort Estimation
Time and complexity estimates
Effort breakdown
7. Assessment Report
Compile findings
Final report
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("${CLAUDE_SKILL_DIR}/references/scope-discovery.md") for the full file discovery, limit application (MAX 30 files), and sampling priority logic. Always include the scoped file list in every agent prompt.
Progressive Output (CC 2.1.76)
Output results incrementally as each evaluation phase completes:
After Phase
Show User
1. Target Understanding
Scope summary, file list, context
1.5. Scope Discovery
Bounded file list (max 30 files)
2. Quality Rating
Each dimension's score as the evaluating agent returns
3. Pros/Cons
Balanced evaluation summary
For Phase 2 parallel agents, show each dimension's score as soon as the evaluating agent returns — don't wait for all 4 agents. If any dimension scores below 4/10, flag it immediately as a priority concern requiring user attention.
Phase 2: Quality Rating (7 Dimensions)
Rate each dimension 0-10 with weighted composite score. Load Read("${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("${CLAUDE_SKILL_DIR}/references/improvement-prioritization.md")
Opus 4.7 is materially better than 4.6 at honestly reporting its own limits. When xhigh effort is active, enrich each dimension's rating with a confidence level and a list of caveats — things the model couldn't verify, assumptions it relied on, or cases it didn't test.
Output schema per dimension (JSON):
{"dimension":"security","score":7.2,"confidence":"medium",// "low" | "medium" | "high""caveats":["Didn't execute the SQL queries against a real DB to confirm parameterization","Assumed NODE_ENV=production in deployment; didn't verify CI config","Reviewed 12 of 15 handlers; remaining 3 deferred by scope filter"],"evidence":["src/api/auth.ts:42","src/middleware/guard.ts:88"]}
Rules:
Do not use confidence as an auto-gate. It's a signal for the human reader, not a pass/fail threshold.
caveats must be specific. "Didn't check X" with file paths beats "uncertainty about security".
If a caveat is cheap to resolve, resolve it instead of recording it. Caveats are for things that genuinely can't be verified within the skill's scope (e.g., production runtime behavior, future input patterns).
Composite score still computes from score only — not weighted by confidence — to keep the number comparable across runs.
Grade Interpretation
Load Read("${CLAUDE_PLUGIN_ROOT}/skills/quality-gates/references/unified-scoring-framework.md") for grade thresholds and scoring criteria.
