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.
npx agentmods add skills/robcsaszar/ai-forge/ai-forge-evalnpx skills add robcsaszar/ai-forge --skill ai-forge-evalgit clone --depth 1 https://github.com/robcsaszar/ai-forgeWrote 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.
[](https://agentmods.dev/skills/robcsaszar/ai-forge/ai-forge-eval)<a href="https://agentmods.dev/skills/robcsaszar/ai-forge/ai-forge-eval"><img src="https://agentmods.dev/badge/skills/robcsaszar/ai-forge/ai-forge-eval.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00112 | $0.02754 |
| Opus 5 | $0.00056 | $0.01377 |
| Sonnet 5 | $0.00022 | $0.00551 |
| Haiku 4.5 | $0.00011 | $0.00275 |
Grade A, and why
ai-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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Forge Eval
Behavioral validation for skills and agents. Rubric scoring (ai-forge-judge) tells you if an artifact is well-written. Eval tells you if it works.
Works for SKILL.md (skills) and .agent.md (agents). Same 5-phase flow; Phase 2 setup differs by artifact type.
Phase 0 — Load or Write the Suite
MANDATORY — READ references/eval-suite.md for the on-disk format, the trigger protocol, and the assertion-discrimination table.
Check for evals/evals.json beside the artifact.
- Present — load it and skip to Phase 2. Add cases if coverage is thin; never silently replace existing ones, or the trend breaks.
- Absent — author it in Phase 1 and write it to
evals/evals.jsonbefore spawning anything.
A suite that lives only in this conversation cannot detect a regression next month. Persisting it is what makes the difference between an opinion and a test.
Phase 1 — Write Evals
Write 2–3 eval cases. Each eval is a realistic prompt plus 3–5 assertions.
Prefer prompts drawn from a baseline probe (ai-forge-create Phase 1b) over the artifact's own stated triggers — assertions written from the artifact can only confirm it does what it claims. Carry each capture into the scenario's baseline_failure field.
For skills: prompts that should activate the skill naturally. Assertions check skill-specific behaviors (e.g. "output includes a Phase 1 recap", "NEVER rule format has WHY and INSTEAD").
For agents: prompts covering the agent's stated scope. Assertions check observable behaviors — files created, tools called, tone constraints, scope limits (e.g. "did not modify files outside src/lib/", "opened a PR", "commit message starts with 'refactor:'").
Assertion rules:
- Checkable: the assayer can verify from the output alone
- Specific: "output includes 'Phase 1' header" beats "output is well-structured"
- Falsifiable: must be possible to fail
Record evals:
Eval 1: <prompt>
Expectations:
- <assertion 1>
- <assertion 2>
- <assertion 3>
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 196 lines · 112 tokens per session scan A 2a0434f9836e
ai-forge-eval is a skill published in the GitHub repository robcsaszar/ai-forge (0 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 2,754 once invoked, about $0.0006 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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