skill-forge-eval

An evaluation pipeline for testing Claude Code skills, which are reusable instructions for a coding assistant. It checks whether a skill activates when it should, follows its workflow correctly, and produces acceptable results.

In plain words
What is it for?
Use it to create or load evaluation cases, run skill tests, check assertions, and produce metadata, grading results, comparisons, and feedback reports.
Why use it?
It replaces informal testing with defined prompts, expected outcomes, grading, and feedback about what needs improvement.

Skill for Claude CodeCodex

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 skills/agricidaniel/skill-forge/skill-forge-eval
Any agent
npx skills add AgriciDaniel/skill-forge --skill skill-forge-eval
Clone the repo
git clone --depth 1 https://github.com/AgriciDaniel/skill-forge

Made for: Claude Code, Codex.

Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,697 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 $0.00095 $0.01697
Opus 5 $0.00048 $0.00848
Sonnet 5 $0.00019 $0.00339
Haiku 4.5 $0.00010 $0.00170

Measured 2d ago against content hash 8f124e2ad95d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

skill-forge-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 2d 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.

skills/skill-forge-eval/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill Evaluation Pipeline

Run structured evaluations against Claude Code skills to verify triggering, correctness, and quality using a multi-agent pipeline.

Process

Step 1: Define Eval Set

Accept eval definitions from:

  • Path to eval set JSON: evals/evals.json or user-specified file
  • Inline prompts: User provides eval queries directly
  • Auto-generated: Generate from skill description (see Step 1b)

Eval set JSON schema:

{
  "skill_name": "my-skill",
  "skill_path": "./my-skill",
  "evals": [
    {
      "eval_id": 0,
      "eval_name": "descriptive-name",
      "prompt": "The user's task prompt",
      "input_files": [],
      "assertions": [
        {
          "name": "output-has-score",
          "check": "Output contains a numeric score between 0-100",
          "weight": 1.0
        }
      ],
      "should_trigger": true
    }
  ]
}
Step 1b: Auto-Generate Eval Set

If no eval set exists, generate one:

  1. Read the skill's SKILL.md description and instructions
  2. Run python scripts/generate_eval_set.py <skill-path> to produce a starter set
  3. Present the generated set to the user for review and editing
  4. User approves or modifies before proceeding

Step 2: Set Up Workspace

Create the eval workspace outside the skill directory to avoid confusing eval artifacts with skill files. Use a sibling directory or a dedicated location:

eval-workspace/
  iteration-1/
    eval-0/
      eval_metadata.json        # Assertions and config for this eval
      with_skill/
        outputs/                # Skill execution outputs
        timing.json             # Token count + duration
        grading.json            # Assertion results + evidence
      baseline/
        outputs/
        timing.json
        grading.json
    eval-1/
      eval_metadata.json
      with_skill/
        outputs/
        timing.json
        grading.json
      baseline/
        outputs/
        timing.json
        grading.json
    benchmark.json              # Aggregated metrics
    benchmark.md                # Human-readable report

Read the full file on GitHub · 233 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. 2d ago First seen · 233 lines · 95 tokens per session scan A 8f124e2ad95d

Subscribe to this mod's changes

skill-forge-eval is a skill published in the GitHub repository AgriciDaniel/skill-forge (166 stars, last pushed 4mo ago), licensed MIT. It adds 95 tokens to every session and 1,697 once invoked, about $0.0005 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 2026-08-30.

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