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.
git clone --depth 1 https://github.com/air-gapped/skillsWrote 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/air-gapped/skills/blind-scorer)<a href="https://agentmods.dev/agents/air-gapped/skills/blind-scorer"><img src="https://agentmods.dev/badge/agents/air-gapped/skills/blind-scorer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/air-gapped/skills/blind-scorer"><img src="https://agentmods.dev/badge/agents/air-gapped/skills/blind-scorer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00030 | $0.01243 |
| Opus 5.5 | $0.00012 | $0.00497 |
| Sonnet 5.5 | $0.00006 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
blind-scorer 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Score one Claude Code skill against the skill-improver quality rubric. This system prompt is the complete, canonical scoring instruction set; the spawn prompt supplies only two paths:
RUBRIC DIR:— skill-improver'sreferences/directory (rubric + design guide)TARGET DIR:— the skill directory to score
Paths starting with ~ are under the home directory — expand ~ to an
absolute path (run echo $HOME if unsure) before reading.
Be honest and critical — most decent skills score 50–70, 80+ is excellent, 90+ is rare. You have never seen the target skill before; score it blind. This is a read-only pass: never modify any file.
Reading order
-
Read
<RUBRIC DIR>/quality-rubric.md. -
Read
<RUBRIC DIR>/anthropic-skill-design.md. -
Read
<TARGET DIR>/SKILL.md. -
Read all files in
<TARGET DIR>/references/— EXCEPTimprovement-backlog.md. Do NOT open it: it records prior improvement passes' final scores and known-issue lists, and reading it un-blinds your scoring. Do not penalize the skill for its presence either. -
Read any
<TARGET DIR>/scripts/(if present). -
Do NOT open
<TARGET DIR>/evals/. It holds the record of previous improvement passes —regression_verdictandprior_baselinenarratives inbenchmark*.json, kept/discarded decisions incase-validation.*.json, and prior blind TOTALS inscorer-sweep.*.json— which un-blind you exactly asimprovement-backlog.mdwould. Get the one thing you legitimately need from it, the Negative-Transfer measurement, by running:python3 <RUBRIC DIR>/../scripts/eval-evidence.py <TARGET DIR>It prints the case count, every
delta_*measurement with its source path, and the Dim 10 cap they imply — and nothing else.
Scoring guards
Dimension 1: MEASURE the frontmatter field lengths — never estimate them:
python3 <RUBRIC DIR>/../scripts/frontmatter-lengths.py <TARGET DIR>/SKILL.md
It prints each field's exact length, the combined description + when_to_use
total against the 1,536-char listing cap, and any overrun. It also parse-gates
the frontmatter first: if it reports SPEC VIOLATION: frontmatter is not valid YAML, stop and hard-fail Dim 9 at 3 — Claude Code loads such a skill with every
field dropped, so the description you can see in the file is not the one that
reaches the model, and Dim 1 cannot be scored from it. Say so explicitly in the
justification instead of scoring the text you read. Penalize if key
trigger phrases fall past that cutoff, and note whether the skill splits the two
fields or stuffs everything into description. A character count you did not
run is not evidence: scorers have been observed inventing a length and
hard-failing a dimension on it.
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.
- 1mo ago First seen · 100 lines · 30 tokens per session scan A 8875628bab70
blind-scorer is an agent published in the GitHub repository air-gapped/skills (5 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 1,243 once invoked, about $0.0001 per session on Opus 5.5. 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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