Compliance Drift Evals
ucsandman/DashClaw
Set up compliance exports, drift detection, evaluations, scoring, and learning analytics
Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi
Install Eval Harness
$ npx skills add affaan-m/ECC --skill eval-harness -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC eval-harness --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/tr/skills/eval-harness .claude/skills/eval-harness && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .claude/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harnessType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add affaan-m/ECC --skill eval-harness -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC eval-harness --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/docs/tr/skills/eval-harness .agents/skills/eval-harness && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .agents/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill eval-harness -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC eval-harness --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/docs/tr/skills/eval-harness .cursor/skills/eval-harness && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .cursor/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/affaan-m/ECC.git --path docs/tr/skills/eval-harness--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add affaan-m/ECC --skill eval-harness -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC eval-harness --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/docs/tr/skills/eval-harness .gemini/skills/eval-harness && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .gemini/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install affaan-m/ECC eval-harnessInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add affaan-m/ECC --skill eval-harness -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/docs/tr/skills/eval-harness .github/skills/eval-harness && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .github/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill eval-harness -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC eval-harness --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/docs/tr/skills/eval-harness .opencode/skills/eval-harness && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "eval-harness" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/tr/skills/eval-harness into .opencode/skills/eval-harness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eval-harness", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
eval-harness- GitHub stars
- 277k
- Used in
- 1 other repo
- Token cost
- ~1.7k tokens
- SKILL.md length
- 396 words
- Files
- 1
- Skills in repo
- 683
- Repo updated
- First seen
- Licence
- MIT
At a glance
Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi
- Works in 7 steps: Code-Based Grader → Model-Based Grader → Human Grader → …
- Tasks that involve LLM evaluation
- SKILL.md covers Ne Zaman Aktifleştirmeli, Felsefe, Eval Tipleri and Grader Tipleri, plus 7 more sections
- Calls npm
What it does
Eval Harness is an agent skill from affaan-m/ECC. Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering LLM evaluation. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.
When your agent uses it
- Tasks that involve LLM evaluation
Example prompts
- “/eval-harness”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
npm
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Eval Harness loads about 1.7k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 396 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 396 words, ~1,743 tokens.
Download SKILL.mdSave it as
.claude/skills/eval-harness/SKILL.md (or your agent's skills folder).- name
- eval-harness
- description
- Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi
- origin
- ECC
- tools
- Read, Write, Edit, Bash, Grep, Glob
Eval Harness Skill
Claude Code oturumları için eval-driven development (EDD) ilkelerini uygulayan formal değerlendirme çerçevesi.
Ne Zaman Aktifleştirmeli
- AI destekli iş akışları için eval-driven development (EDD) kurarken
- Claude Code görev tamamlama için geçti/kaldı kriterleri tanımlarken
- pass@k metrikleriyle agent güvenilirliğini ölçerken
- Prompt veya agent değişiklikleri için regresyon test paketleri oluştururken
- Model versiyonları arasında agent performansını benchmark ederken
Felsefe
Eval-Driven Development, eval'ları "AI geliştirmenin birim testleri" olarak ele alır:
- İmplementasyondan ÖNCE beklenen davranışı tanımla
- Geliştirme sırasında eval'ları sürekli çalıştır
- Her değişiklikle regresyonları izle
- Güvenilirlik ölçümü için pass@k metriklerini kullan
Eval Tipleri
Capability Eval'ları
Claude'un daha önce yapamadığı bir şeyi yapıp yapamadığını test et:
[CAPABILITY EVAL: feature-name]
Görev: Claude'un başarması gereken şeyin açıklaması
Başarı Kriterleri:
- [ ] Kriter 1
- [ ] Kriter 2
- [ ] Kriter 3
Beklenen Çıktı: Beklenen sonucun açıklamasıRegression Eval'ları
Değişikliklerin mevcut fonksiyonaliteyi bozmadığından emin ol:
[REGRESSION EVAL: feature-name]
Baseline: SHA veya checkpoint adı
Testler:
- existing-test-1: PASS/FAIL
- existing-test-2: PASS/FAIL
- existing-test-3: PASS/FAIL
Sonuç: X/Y geçti (önceden Y/Y)Grader Tipleri
1. Code-Based Grader
Kod kullanarak deterministik kontroller:
# Dosyanın beklenen pattern içerip içermediğini kontrol et
grep -q "export function handleAuth" src/auth.ts && echo "PASS" || echo "FAIL"
# Testlerin geçip geçmediğini kontrol et
npm test -- --testPathPattern="auth" && echo "PASS" || echo "FAIL"
# Build'in başarılı olup olmadığını kontrol et
npm run build && echo "PASS" || echo "FAIL"2. Model-Based Grader
Açık uçlu çıktıları değerlendirmek için Claude kullan:
[MODEL GRADER PROMPT]
Aşağıdaki kod değişikliğini değerlendir:
1. Belirtilen sorunu çözüyor mu?
2. İyi yapılandırılmış mı?
3. Edge case'ler işleniyor mu?
4. Hata işleme uygun mu?
Puan: 1-5 (1=kötü, 5=mükemmel)
Gerekçe: [açıklama]3. Human Grader
Manuel inceleme için işaretle:
[HUMAN REVIEW REQUIRED]
Değişiklik: Neyin değiştiğinin açıklaması
Sebep: Neden insan incelemesi gerekli
Risk Seviyesi: DÜŞÜK/ORTA/YÜKSEKMetrikler
pass@k
"k denemede en az bir başarı"
- pass@1: İlk deneme başarı oranı
- pass@3: 3 denemede başarı
- Tipik hedef: pass@3 > %90
pass^k
"Tüm k denemeler başarılı"
- Güvenilirlik için daha yüksek çıta
- pass^3: Ardışık 3 başarı
- Kritik yollar için kullan
Eval İş Akışı
1. Tanımla (Kodlamadan Önce)
## EVAL DEFINITION: feature-xyz
### Capability Eval'ları
1. Yeni kullanıcı hesabı oluşturabilir
2. Email formatını doğrulayabilir
3. Şifreyi güvenli şekilde hash'leyebilir
### Regression Eval'ları
1. Mevcut login hala çalışıyor
2. Oturum yönetimi değişmedi
3. Logout akışı sağlam
### Başarı Metrikleri
- capability eval'lar için pass@3 > %90
- regression eval'lar için pass^3 = %1002. Uygula
Tanımlanan eval'ları geçmek için kod yaz.
3. Değerlendir
# Capability eval'ları çalıştır
[Her capability eval'ı çalıştır, PASS/FAIL kaydet]
# Regression eval'ları çalıştır
npm test -- --testPathPattern="existing"
# Rapor oluştur4. Rapor
EVAL REPORT: feature-xyz
========================
Capability Eval'ları:
create-user: PASS (pass@1)
validate-email: PASS (pass@2)
hash-password: PASS (pass@1)
Genel: 3/3 geçti
Regression Eval'ları:
login-flow: PASS
session-mgmt: PASS
logout-flow: PASS
Genel: 3/3 geçti
Metrikler:
pass@1: %67 (2/3)
pass@3: %100 (3/3)
Durum: İNCELEMEYE HAZIREntegrasyon Kalıpları
İmplementasyondan Önce
/eval define feature-name.claude/evals/feature-name.md konumunda eval tanım dosyası oluşturur
İmplementasyon Sırasında
/eval check feature-nameMevcut eval'ları çalıştırır ve durumu raporlar
İmplementasyondan Sonra
/eval report feature-nameTam eval raporu oluşturur
Eval Depolama
Eval'ları projede sakla:
.claude/
evals/
feature-xyz.md # Eval tanımı
feature-xyz.log # Eval çalıştırma geçmişi
baseline.json # Regression baseline'larıShow full SKILL.md (174 more words)Show less
En İyi Uygulamalar
- Kodlamadan ÖNCE eval'ları tanımla - Başarı kriterleri hakkında net düşünmeyi zorlar
- Eval'ları sık çalıştır - Regresyonları erken yakala
- pass@k'yı zaman içinde izle - Güvenilirlik trendlerini gözle
- Mümkün olduğunda code grader kullan - Deterministik > olasılıksal
- Güvenlik için insan incelemesi - Güvenlik kontrollerini asla tam otomatikleştirme
- Eval'ları hızlı tut - Yavaş eval'lar çalıştırılmaz
- Eval'ları kodla versiyonla - Eval'lar birinci sınıf artifact'lardır
Örnek: Kimlik Doğrulama Ekleme
## EVAL: add-authentication
### Faz 1: Tanımla (10 dk)
Capability Eval'ları:
- [ ] Kullanıcı email/şifre ile kayıt olabilir
- [ ] Kullanıcı geçerli kimlik bilgileriyle giriş yapabilir
- [ ] Geçersiz kimlik bilgileri uygun hatayla reddedilir
- [ ] Oturumlar sayfa yeniden yüklemelerinde kalıcıdır
- [ ] Logout oturumu temizler
Regression Eval'ları:
- [ ] Halka açık rotalar hala erişilebilir
- [ ] API yanıtları değişmedi
- [ ] Veritabanı şeması uyumlu
### Faz 2: Uygula (değişir)
[Kod yaz]
### Faz 3: Değerlendir
Çalıştır: /eval check add-authentication
### Faz 4: Raporla
EVAL REPORT: add-authentication
==============================
Capability: 5/5 geçti (pass@3: %100)
Regression: 3/3 geçti (pass^3: %100)
Durum: YAYINLAProduct Eval'ları (v1.8)
Davranış kalitesi sadece birim testlerle yakalanamadığında product eval'ları kullan.
Grader Tipleri
- Code grader (deterministik assertion'lar)
- Rule grader (regex/şema kısıtlamaları)
- Model grader (LLM-as-judge rubric)
- Human grader (belirsiz çıktılar için manuel karar)
pass@k Kılavuzu
pass@1: doğrudan güvenilirlikpass@3: kontrollü yeniden denemeler altında pratik güvenilirlikpass^3: kararlılık testi (3 çalıştırmanın tümü geçmeli)
Önerilen eşikler:
- Capability eval'ları: pass@3 >= 0.90
- Regression eval'ları: yayın-kritik yollar için pass^3 = 1.00
Eval Anti-Kalıpları
- Prompt'ları bilinen eval örneklerine overfitting yapmak
- Sadece mutlu-yol çıktılarını ölçmek
- Geçme oranlarını kovalamken maliyet ve gecikme kaymasını görmezden gelmek
- Yayın kapılarında kararsız grader'lara izin vermek
Minimal Eval Artifact Düzeni
.claude/evals/<feature>.mdtanımı.claude/evals/<feature>.logçalıştırma geçmişidocs/releases/<version>/eval-summary.mdyayın snapshot'ı
© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in docs/tr/skills/eval-harness of affaan-m/ECC.
Open the folder on GitHubat commit 2d515e4
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Eval Harness next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Eval Harness this skillaffaan-m/ECC | 277k | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Compliance Drift Evalsucsandman/DashClaw | 311 | — | ~1.8k | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Looperksimback/looper | 710 | — | ~2.7k | Automated safety check: Notes | MIT | |
| Agent Eval Engineeringlangchain-ai/langchain-skills | 1.3k | — | ~4k | Automated safety check: Pass | MIT |
Similar skills
LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
- Official
Hugging Face Local Model Evals
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
Looper
ksimback/looper
Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council.
- Official
Agent Eval Engineering
langchain-ai/langchain-skills
Builds agent evaluations in stages: inspect the repository and traces, agree a Task Spec with you, then build, audit and run a Harbor task with an independent verifier.
Quality Flywheel
GoogleCloudPlatform/vertex-ai-samples
Evaluate and improve GenAI models and agents using the Google GenAI Evaluation SDK.
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Questions about Eval Harness
What does Eval Harness do?
Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi. Eval Harness is an agent skill from affaan-m/ECC.
When should I use Eval Harness?
Eval Harness fits situations like: tasks that involve LLM evaluation.
How do I install Eval Harness in Claude Code?
Run `npx skills add affaan-m/ECC --skill eval-harness -a claude-code`. Or copy the skill folder (docs/tr/skills/eval-harness in affaan-m/ECC) into .claude/skills/eval-harness in your project. Claude Code loads it when a task matches its description.
How do I install Eval Harness in Codex?
Run `npx skills add affaan-m/ECC --skill eval-harness -a codex`. Or copy the skill folder (docs/tr/skills/eval-harness in affaan-m/ECC) into .agents/skills/eval-harness in your project. Codex loads it when a task matches its description.
Can I use Eval Harness in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill eval-harness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eval-harness, .gemini/skills/eval-harness, .github/skills/eval-harness and .opencode/skills/eval-harness in your project.
What does Eval Harness need to run?
Going by SKILL.md and its folder, Eval Harness needs the command-line tools its instructions call (npm).
Does Eval Harness access the network?
SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Eval Harness safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Eval Harness use?
Eval Harness is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Eval Harness use?
About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Eval Harness?
Skills that share tags, products or a category with Eval Harness: Compliance Drift Evals (ucsandman/DashClaw, 311 stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars) and Looper (ksimback/looper, 710 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Eval Harness?
affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.
Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.