Borrowing it
Nothing to install: this file belongs to jacob-dietle/context-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jacob-dietle/context-os/main/.claude/skills/eval-loop/SKILL.mdgit clone --depth 1 https://github.com/jacob-dietle/context-osWrote 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/jacob-dietle/context-os/eval-loop)<a href="https://agentmods.dev/skills/jacob-dietle/context-os/eval-loop"><img src="https://agentmods.dev/badge/skills/jacob-dietle/context-os/eval-loop/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/skills/jacob-dietle/context-os/eval-loop"><img src="https://agentmods.dev/badge/skills/jacob-dietle/context-os/eval-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00150 | $0.04905 |
| Opus 5.5 | $0.00060 | $0.01962 |
| Sonnet 5.5 | $0.00030 | $0.00981 |
| Haiku 4.5 | $0.00015 | $0.00490 |
Grade A, and why
eval-loop 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 2mo 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 — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Loop
Generalized quality iteration loop for any dimension — UX, data architecture, code quality, feature completeness, content tone. Traces specific symptoms to structural root causes, defines measurable targets, and iterates with automated backpressure until targets pass.
Meta-Principle: "Don't fix the symptom, fix the class — but verify by measuring the symptom."
Scope-Principle (added): "This loop verifies presence, shape, and content quality. It does NOT verify predictive validity. Scoring/classification problems require statistical gates this skill does not provide — route them to eval-driven-scoring."
When to Use This Skill
Apply this skill when:
- A specific quality complaint surfaces ("I can't click the contact", "this email sounds robotic")
- A feature needs a quality bar before shipping ("what would 10/10 look like?")
- A class of problems keeps recurring (data gaps, UX friction, architectural debt)
- Quality needs to improve but the path from current → target is unclear
- Multiple dimensions (UX + data + code) need coordinated improvement
Do NOT use for:
- Initial architecture design (use
/specification-driven-development) - LLM scoring / classification / predictive ranking — use
/eval-driven-scoring. This is a HARD route, not a suggestion. - Simple bugs with obvious fixes (just fix them)
- Performance optimization (different discipline — profile first)
Relationship to eval-driven-scoring: This skill is the generalized eval loop for deterministic quality (presence, shape, tone, content). eval-driven-scoring is the specialized sibling for predictive quality (classification, ranking). They are NOT interchangeable — predictive problems need holdouts, base rates, and discriminative ratios that this skill does not enforce.
Step 0: Classify the Problem Type (MANDATORY — DO FIRST)
Before Step 1, classify the quality problem. Routing wrong here wastes work and can produce confidently wrong scoring models.
What ships with it
3 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.
- 2mo ago First seen · 458 lines · 150 tokens per session scan A a1ac29e48d3c
eval-loop is a skill published in the GitHub repository jacob-dietle/context-os (115 stars, last pushed 2mo ago), licensed MIT. It adds 150 tokens to every session and 4,905 once invoked, about $0.0006 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 2026-08-30.
eval-loop compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| eval-loopjacob-dietle/context-os | Skill | 115 | 2mo ago | MIT | A | 150 | Claude Code |
| systematic-debuggingopen-metadata/OpenMetadata | Skill | 16k | yesterday | Apache-2.0 | A | 37 | Claude Code |
| log-error-digestzebbern/claude-code-guide | Skill | 4.7k | 3d ago | MIT | A | 71 | Claude Code, Codex |
| cwv-optimizeradobe/skills | Skill | 319 | yesterday | Apache-2.0 | A | 112 | Claude Code |
| ci-fixersipyourdrink-ltd/bernstein | Skill | 1.2k | yesterday | Apache-2.0 | A | 15 | Claude Code, Codex |
| incident-response-incident-responsermyndharis/antigravity-skills | Skill | 1.9k | 23d ago | MIT | A | 36 | Claude Code |
| triaging-web-analytics-supportPostHog/posthog | Skill | 41k | yesterday | none | A | 151 | Claude Code, Codex |