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 skills add leonardoeverling-spec/governed-context --skill blind-evalgit clone --depth 1 https://github.com/leonardoeverling-spec/governed-contextWrote 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/leonardoeverling-spec/governed-context/blind-eval)<a href="https://agentmods.dev/skills/leonardoeverling-spec/governed-context/blind-eval"><img src="https://agentmods.dev/badge/skills/leonardoeverling-spec/governed-context/blind-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 5.1 | $0.00092 | $0.01459 |
| Opus 5 | $0.00046 | $0.00730 |
| Sonnet 5 | $0.00018 | $0.00292 |
| Haiku 4.5 | $0.00009 | $0.00146 |
Grade A, and why
blind-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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blind evaluation protocol
Roles.
- OPERATOR (usually the agent): prepares material, runs arms, records judgments as scribe.
- JUDGE: a human, or a clean model instance with no access to preparation material. Whoever prepared or operated does not judge.
- PRINCIPAL: the human the result is for. Present at unblinding; never unblind alone.
Who judged is study data: human/model, creator/external, and the bias they declared before seeing anything.
Study layout
Create one directory per study and keep everything in it:
study-<name>/
preregistration.md frozen after Phase 1
index.md every collection, with status
collections/<id>/ the material judges see (clean, no badges)
mapping/<id>.private.json label -> arm; nobody opens until Phase 4
judgments/ append-only records written by the recorder
analysis.md written in Phase 4 only
Phase 1 — Pre-registration (before any material exists)
Write preregistration.md, then it freezes: corrections are new files that reference the old
one, never edits.
- The question, in conditional form that accepts any outcome ("under which conditions does X improve, worsen or not change Y") — never "prove X is better".
- Arms and exactly what each receives; parity of model, budget and conditions.
- Observed metadata (effective model id, versions) — a requested alias is never proof.
- Judgment dimensions + a closed flag list you write for this study. The list from the founding study — invented facts, unsupported grandiosity, irrelevant personalization, leakage, ignored constraints — is an example, not a standard.
- Proportional reading grid: what EACH possible outcome licenses you to conclude — including defeat and instability ("variance across replicas is the main finding" is a legitimate result).
- ALL post-unblinding analyses, fixed now. A stopping rule. After this the protocol is frozen: new analysis ideas become proposals for the NEXT study, never amendments to this one.
What ships with it
1 file 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.
- 6d ago First seen · 116 lines · 92 tokens per session scan A 1664befd6b2e
blind-eval is a skill published in the GitHub repository leonardoeverling-spec/governed-context (0 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,459 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 2026-08-31.
Other skills, from other repositories
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
golden-dataset
Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.
omega-memory
Persistent memory for AI coding agents. Teaches agents how to use OMEGA's MCP tools for storing decisions, querying context, coordinating multi-agent workflows, and resuming tasks across sessions.
omega-memory
Persistent memory for AI coding agents. Semantic search, auto-capture, checkpoint/resume across sessions.
core
This skill should be used when the user says "capture memory", "save to memory", "remember this", or trigger phrases like: "I've decided", "let's use", "we're going with", "I learned", "turns out", "TIL", "discovered", "I'm stuck", "blocked by", "always use", "never do", "convention is". Also triggers on recall…
baton
A project handoff system for letting different AI coding tools continue the same work while sharing tasks, decisions, verification results, and project memory through Git.