eval

eval is a command for Claude Code from SeongwoongCho/adaptive-harness. It costs 11 tokens per session (649 once invoked), scanned A, original, MIT.

A command for manually evaluating a completed coding task or a selected file change. It uses recent session evidence or the files named in the command.

In plain words
What is it for?
Evaluate the latest task, inspect a specific file or diff, and report when no evaluation context is available.
Why use it?
It provides a way to review results when automatic evaluation did not run or when a second evaluation is needed.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the adaptive-harness plugin — 4 skills, 4 commands, 29 agents, 5 hooks shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/seongwoongcho/adaptive-harness/eval
Clone the repo
git clone --depth 1 https://github.com/SeongwoongCho/adaptive-harness

Made for: Claude Code.

Or install adaptive-harness, the plugin that ships this one along with the rest of its 4 skills, 4 commands, 29 agents, 5 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for eval

README.md
[![agentmods](https://agentmods.dev/badge/commands/seongwoongcho/adaptive-harness/eval.svg)](https://agentmods.dev/commands/seongwoongcho/adaptive-harness/eval)
Your own site
<a href="https://agentmods.dev/commands/seongwoongcho/adaptive-harness/eval"><img src="https://agentmods.dev/badge/commands/seongwoongcho/adaptive-harness/eval.svg" alt="Measured on agentmods" height="20"></a>
Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 649 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00011 $0.00649
Opus 5 $0.00005 $0.00324
Sonnet 5 $0.00002 $0.00130
Haiku 4.5 $0.00001 $0.00065

Measured 6d ago against content hash 209ce7bdfb06, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/eval.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

adaptive-harness-eval

Manually trigger evaluation of a task result. Use this to re-evaluate the last completed task, evaluate a specific change, or run evaluation when auto-evaluation did not fire.

Parsing Arguments

  • --last — (default) Evaluate the most recent task result in this session
  • --file=path — Evaluate a specific file or diff (e.g., --file=src/auth.py)
  • No argument — same as --last

Execution Steps

Step 1: Identify What to Evaluate

If --last or no argument:

Find the most recent evaluation context in this session:

  1. Check .adaptive-harness/sessions/{session_id}/ for the latest evidence files
  2. Read .adaptive-harness/sessions/{session_id}/evidence/ — sort by timestamp, take most recent
  3. If no evidence files exist, check if the last conversation turn produced code changes

If no evaluation context found:

No recent task result found to evaluate.

To evaluate a specific file: /adaptive-harness:eval --file=path/to/file
To run a task first: /adaptive-harness:run <task description>

If --file=path:

Read the specified file(s) to use as evaluation input. Accept glob patterns (e.g., --file=src/**/*.py).

Step 2: Collect Evidence

Read all evidence files from .adaptive-harness/sessions/{session_id}/evidence/ sorted by timestamp:

Read(".adaptive-harness/sessions/{session_id}/evidence/")

Also collect any git diff if available:

git diff --stat HEAD 2>/dev/null || echo "no git"
git diff HEAD 2>/dev/null | head -200

Step 3: Spawn Evaluator Agent

Task(
  subagent_type="adaptive-harness:evaluator",
  prompt="Manually evaluate this task result.\n\nEvaluation target: {description of what's being evaluated}\n\nEvidence:\n{evidence_summary}\n\nGit diff (if available):\n{diff_output}"
)

Step 4: Display Results

Show evaluation scores and write to state:

Manual evaluation complete.

Overall score: {score} ({PASS|FAIL})

Dimension scores:
  correctness:     {score}  — {brief_reasoning}
  completeness:    {score}  — {brief_reasoning}
  quality:         {score}  — {brief_reasoning}
  robustness:      {score}  — {brief_reasoning}
  clarity:         {score}  — {brief_reasoning}
  verifiability:   {score}  — {brief_reasoning}

Quality gate: {PASSED|FAILED — reason}

Suggestions:
  {improvement_suggestions}

Read the full file on GitHub · 86 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 86 lines · 11 tokens per session scan A 209ce7bdfb06

Subscribe to this mod's changes

eval is a command published in the GitHub repository SeongwoongCho/adaptive-harness (8 stars, last pushed 5mo ago), licensed MIT. It adds 11 tokens to every session and 649 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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