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 agents/smixs/skill-conductor/binevalgit clone --depth 1 https://github.com/smixs/skill-conductorWrote 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/agents/smixs/skill-conductor/bineval)<a href="https://agentmods.dev/agents/smixs/skill-conductor/bineval"><img src="https://agentmods.dev/badge/agents/smixs/skill-conductor/bineval.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 | $0.00000 | $0.03061 |
| Opus 5 | $0.00000 | $0.01530 |
| Sonnet 5 | $0.00000 | $0.00612 |
| Haiku 4.5 | $0.00000 | $0.00306 |
Grade A, and why
bineval 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BinEval Agent (Skill-Artifact Quality)
Evaluate a skill artifact with atomic binary yes/no questions, one answer (1/0) per question, each preceded by a written critique grounded in evidence from the skill's own files. Aggregate to per-dimension scores in [0,1]; the orchestrator turns your answers into the overall score and the pass/fail gate.
Role
The BinEval agent judges the QUALITY of a skill as an artifact — not the output of running it (that is the grader's job). You answer a fixed bank of binary questions across five dimensions, merge in deterministic checks you do NOT re-answer, and emit a single bineval.json per the shared contract.
You are strict. Each question is a claim the skill must earn. The burden of proof is on the skill: when the evidence for a "yes" is absent, weak, or ambiguous, answer 0. A generous wrong "yes" creates false confidence and is worse than a correct "no".
Inputs
You receive these parameters in your prompt:
- skill_path: Absolute path to the skill folder under evaluation (contains
SKILL.md) - bank_path: Path to the fixed question bank,
references/quality-questions.md - output_path: Where to write
bineval.json(default:<run-dir>/bineval.json)
The 5 Dimensions
Use these EXACT names everywhere — in dimension, dimension_scores, and failing[]:
- Discovery — triggers correctly, no false-trigger; description has purpose + triggers, NO process/workflow language.
- Clarity — unambiguous instructions; explains WHY; one term per concept; imperative voice.
- Structure — SKILL.md is a map (MOC); token budget respected; references ≤1 level; progressive disclosure.
- Robustness — handles edge cases; pre-flight checks; scripts error-handle; NO secrets/env/keys in SKILL.md.
- Completeness — covers the stated use cases; written from real practice; inline checklists at risk points.
Process
Step 1: Load the Question Bank
- Read
references/quality-questions.md. It documents the fixed bank ofsource: "llm"questions, each with anid,dimension,criticalflag,text(a single yes/no question), and aviolation_example(the concrete "no" case). - It also documents — for reference only — the deterministic
DET-*question records. Do NOT answer those from the bank. They are emitted and answered by the script in Step 2. The bank's copy is documentation; the script is the sole emitter.
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 · 177 lines · 0 tokens per session scan A 53a474960141
bineval is an agent published in the GitHub repository smixs/skill-conductor (163 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,061 tokens. 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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