/assess

name: assess
license: MIT
compatibility: "Claude Code 2.1.251+. Requires memory MCP server."
description: "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."
context: fork
# user-typed commands stay interactive; CC >= 2.1.218 backgrounds forks by default (#3093)
background: false
version: 1.8.0
author: OrchestKit
tags: [assessment, evaluation, quality, comparison, pros-cons, rating]
user-invocable: true
allowed-tools: [AskUserQuestion, Read, Write, Grep, Glob, Agent, 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]"
complexity: high
persuasion-type: guidance
effort: high
model: sonnet
hooks:
  PreToolUse:
    - matcher: "Read"
      command: "${CLAUDE_PLUGIN_ROOT}/hooks/bin/run-hook.mjs skill/assessment-baseline-loader"
      once: true
metadata:
  category: document-asset-creation
  mcp-server: memory
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 (CC 2.1.111+ adds `xhigh`)

| Effort | Behavior | |---|---| | `low` / `medium` | Subset of dimensions, faster turnaround | | `high` (default) | All six dimensions with pros/cons | | `xhigh` | All six 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 conversational reference first

`$ARGUMENTS` is often not a path. For a bare pronoun or deictic (`them`, `this`, `that`, `these`, `they`, `same`, `the above`, `the last one`, `what we just did`) or an empty target after flags are stripped, the subject is in the conversation. Read back for the NEAREST concrete one (a file just discussed, a diff or PR just opened, a component just investigated) and announce the resolution in one line, so a wrong guess costs a correction rather than a turn: *"Reading 'them' as the 3 pretool guards we just probed; say otherwise and I'll switch."*

**Refusing is the bug, not the safe option.** Asking "what does this refer to?" when the previous turn named the subject burns a round-trip re-deriving what is already on screen. Measured 2026-08-28: the operator sent `assess them throguhly` one message after "bug in orchestkit hooks", mid-investigation of `pretool/bash/dangerous-command-blocker`, and this skill replied that "them" had "no antecedent anywhere in this conversation". It had two.

Ask only when the conversation is genuinely empty (a fresh session opening with a bare pronoun). Every other case: resolve and announce.

> Not unique to this skill: `verify`, `cover`, `fix-issue`, `review-pr` and `implement` all > read `$ARGUMENTS` as a literal path or topic, and no skill mentions resolving a reference. > Tracked separately; this one fixes its own door.

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-4-8`) 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 (CC 2.1.120+)

`$CLAUDE_EFFORT` is the primary signal. CC 2.1.120 sets this env var from `/effort` or the model picker. `--effort=` token in `$ARGUMENTS` is the explicit override fallback (also covers older CC).

# Read env first (CC 2.1.120+), 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 CC < 2.1.120 and no flag

Use `EFFORT` to gate dimension count, agent count, and the optional `xhigh` uncertainty pass — see "Effort leve