/assess

name: assess
license: MIT
compatibility: "Claude Code 2.1.277+. Requires memory MCP server."
description: "Rates code, designs or approaches 0-10 across quality dimensions with pros, cons and fixes. Use when evaluating or comparing options. Not for fixing or building (use implement or fix-issue), PR review (use review-pr) or running tests (use verify)."
user-invocable: true
allowed-tools: "AskUserQuestion Read Write Grep Glob Agent Workflow TaskCreate TaskUpdate TaskList ToolSearch mcp__memory__search_nodes Bash"
skills: [code-review-playbook, quality-gates, architecture-decision-record, memory, chain-patterns]
argument-hint: "[code-path-or-topic] [--render=markdown|json-render|both] [--effort=low|medium|high|xhigh]"
effort: high
model: sonnet
hooks:
  PreToolUse:
    - matcher: "Read"
      hooks:
        - type: command
          command: "${CLAUDE_PLUGIN_ROOT}/hooks/bin/run-hook.mjs skill/assessment-baseline-loader"
          once: true
metadata:
  category: document-asset-creation
  mcp-server: memory
  version: "1.9.0"
  author: "OrchestKit"
  complexity: "high"
  tags: "assessment, evaluation, quality, comparison, pros-cons, rating"
triggers:
  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

Host-neutral workflow. Invoke by skill name (`assess`). Claude Code slash routing, YAML hook loaders, and `.claude/chain` live in `references/claude-code.md`.

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

🎯 Quick Start

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

Effort levels

| Effort | Behavior | |---|---| | `low` / `medium` | Subset of dimensions, faster turnaround | | `high` (default) | All seven dimensions with pros/cons | | `xhigh` | All seven dimensions + one additional assessor pass focused on uncertainty/caveats; emits `confidence` per dimension |

> `xhigh` silently falls back to `high` on a model that does not implement it: no error, no log line. `doctor` Category 14 reports this, and only when it can positively prove the active model lacks the tier.

---

Argument Resolution

Step 0: resolve a pronoun target first

`$ARGUMENTS` is often not a path. Load `Read("../../shared/rules/target-resolution.md")` and follow its inline half: a bare pronoun or deictic (`them`, `this`, `the above`) or an empty target is read back to the nearest concrete subject in the conversation, announced in one line, and assessed. Ask only when the conversation is empty.

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", "fable"
        TARGET = TARGET.replace(token, "").strip()

Pass `MODEL_OVERRIDE` to all Agent() calls via `model=MODEL_OVERRIDE` when set. Accepts symbolic names (`opus`, `sonnet`, `haiku`, `fable` on harnesses whose Agent tool lists it; note fable is premium API spend after 2026-07-12) or full IDs (`claude-opus-5-5`) per CC 2.1.74.

> **Switching to Opus via `/model` (CC 2.1.144+):** `/model` now changes the model for the current session only, so picking Opus for an assess run no longer persists past it. Press `d` in the picker only to set a default for new sessions.

Effort detection

`$CLAUDE_EFFORT` is the primary signal: Claude Code sets it from `/effort` or the model picker. A `--effort=` token in `$ARGUMENTS` is the explicit override.

# Read env first, then check explicit override
EFFORT = os.environ.get("CLAUDE_EFFORT")  # "low" | "medium" | "high" | "xhigh" | None
for token in "$ARGUMENTS".split():
    if token.startswith("--effort="):
        EFFORT = token.split("=", 1)[1]   # explicit override wins
        TARGET = TARGET.replace(token, "").strip()
EFFORT = EFFORT or "high"  # default when neither is set

Use `EFFORT` to gate dimension count, agent count, and the optional `xhigh` uncertainty pass — see "Effort levels" table above. `doctor` Category 14 reports a provably unsupported `xhigh` request.

---

STEP -1: MCP Probe + Resume Check

> Load: `Read("../chain-patterns/references/mcp-detection.md")`

# 1. Probe MCP servers (once at skill start)
# memory may be absent from the user's .mcp.json; probe so the memory steps below are skipped, not failed:
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

| Phase | Handoff File | Contents | |-------|-------------|----------| | 0 | `00-intent.json` | Dimensions, target, mode | | 1 | `01-baseline.json` | Initial codebase scan results | | 2 | `02-evaluation.json` | Per-dimension scores + evidence | | 3 | `03-report.json` | Final report, grade, recommendations |

---

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)",