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/jacob-dietle/context-os/eval-loopnpx skills add jacob-dietle/context-os --skill eval-loopgit clone --depth 1 https://github.com/jacob-dietle/context-osWhat 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 | $0.00150 | $0.04905 |
| Opus 5 | $0.00075 | $0.02452 |
| Sonnet 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 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 — 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.
- 3d 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 (108 stars, last pushed 20d ago), licensed MIT. It adds 150 tokens to every session and 4,905 once invoked, about $0.0007 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 2026-08-30.
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