Agent skill

Experiment Engine

by vibeeval in vibeeval/vibecosystem

Otonom deney dongusu. An agent skill from vibeeval/vibecosystem.

MITAuto-check passedTesting & QA

Install Experiment Engine

skills CLI
$ npx skills add vibeeval/vibecosystem --skill experiment-engine -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vibeeval/vibecosystem experiment-engine --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/experiment-engine .claude/skills/experiment-engine && rm -rf skills-src

Use ~/.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/

Facts

Skill name
experiment-engine
GitHub stars
531
Token cost
~1.6k tokens
SKILL.md length
103 words
Files
1
Skills in repo
144
Repo updated
First seen
Licence
MIT

At a glance

Otonom deney dongusu. An agent skill from vibeeval/vibecosystem.

  • Testing & QA work in your project
  • SKILL.md covers Core Loop, Kullanim Alanlari, Deney Protokolu and Deney Sablonu, plus 2 more sections
  • Calls git, npx and curl

What it does

Experiment Engine is an agent skill from vibeeval/vibecosystem. Otonom deney dongusu. Kod degisikligi yap, olc, karsilastir, kabul et veya geri al. Metrik bazli karar verme ile performans, boyut veya kalite optimizasyonu. Tek basina veya agent ile kullan.

Its SKILL.md is about 1.6k 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 Testing & QA. The repository describes itself as: AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. The licence is MIT.

When your agent uses it

  • Testing & QA work in your project

Example prompts

  • “/experiment-engine”

Requirements

  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit 3b763b1. 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:

    • git
    • npx
    • curl
    • claude

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, npx and curl, 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

Experiment Engine loads about 1.6k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 103 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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 vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 103 words, ~1,578 tokens.

Download SKILL.mdSave it as .claude/skills/experiment-engine/SKILL.md (or your agent's skills folder).
name
experiment-engine
description
Otonom deney dongusu. Kod degisikligi yap, olc, karsilastir, kabul et veya geri al. Metrik bazli karar verme ile performans, boyut veya kalite optimizasyonu. Tek basina veya agent ile kullan.

Not / Overlap: Bu skill experiment-loop ile ayni otonom deney dongusu kavramini kapsar (bu dosya TR, experiment-loop EN). Yeni isler icin experiment-loop tercih edilir; v4.0 da birlestirilecek.

Experiment Engine

Bir hedef belirle, sistematik olarak deneyler yap, sadece iyilestirenleri tut.

Core Loop

HEDEF BELIRLE
  └─ "API response time'i %20 dusur"
  └─ "Bundle size'i 500KB'nin altina getir"
  └─ "Test coverage'i %90'a cikar"

BASELINE OLC
  └─ Mevcut metrigi kaydet (ornek: 340ms, 720KB, %78)

DENEY DONGUSU (N kez tekrarla):
  ┌─────────────────────────────────────┐
  │ 1. MODIFY  - Tek degisiklik yap    │
  │ 2. VERIFY  - Metrigi olc           │
  │ 3. COMPARE - Baseline ile kiyasla  │
  │ 4. DECIDE  - Kabul / Reddet        │
  │    ├─ Iyilesti → COMMIT + yeni     │
  │    │             baseline           │
  │    └─ Kotulesti → ROLLBACK         │
  └─────────────────────────────────────┘

RAPOR OLUSTUR
  └─ N deney, X kabul, Y red, final metrik

Kullanim Alanlari

Performans Optimizasyonu
bash
# Hedef: API response time < 200ms
# Baseline: 340ms

# Deney 1: Database query'ye index ekle
git stash  # mevcut durumu kaydet
# ... index ekle ...
curl -w "%{time_total}" http://localhost:3000/api/users  # 280ms
# 340ms -> 280ms = IYILESTI → COMMIT

# Deney 2: Response'u cache'le
# ... Redis cache ekle ...
curl -w "%{time_total}" http://localhost:3000/api/users  # 45ms
# 280ms -> 45ms = IYILESTI → COMMIT

# Deney 3: JSON serializer degistir
# ... fast-json-stringify ekle ...
curl -w "%{time_total}" http://localhost:3000/api/users  # 42ms
# 45ms -> 42ms = MINIMAL IYILESME → REDDET (karmasiklik artmaya degmez)

# Sonuc: 340ms -> 45ms (%87 iyilesme), 2/3 deney kabul edildi
Bundle Size Azaltma
bash
# Hedef: < 500KB
# Baseline olc
BASELINE=$(npx next build 2>&1 | grep "First Load JS" | awk '{print $4}')

# Deney dongusu
experiments=(
  "lodash yerine lodash-es"
  "moment yerine dayjs"
  "tree-shaking acik mi kontrol"
  "dynamic import lazy component'ler"
  "image optimize (next/image)"
)

for exp in "${experiments[@]}"; do
  echo "=== Deney: $exp ==="
  # degisiklik yap...
  NEW=$(npx next build 2>&1 | grep "First Load JS" | awk '{print $4}')
  if [ "$NEW" -lt "$BASELINE" ]; then
    echo "KABUL: $BASELINE -> $NEW"
    BASELINE=$NEW
    git add -A && git stash  # kaydet
  else
    echo "RED: $NEW >= $BASELINE"
    git checkout .  # geri al
  fi
done
Test Coverage Artirma
bash
# Hedef: %90 coverage
# Baseline
BASELINE=$(npx jest --coverage --silent 2>&1 | grep "All files" | awk '{print $4}')

# Her dosya icin test yaz, coverage'i olc
for file in $(find src -name "*.ts" -not -name "*.test.*"); do
  echo "=== Test: $file ==="
  # test yaz...
  NEW=$(npx jest --coverage --silent 2>&1 | grep "All files" | awk '{print $4}')
  if (( $(echo "$NEW > $BASELINE" | bc -l) )); then
    echo "KABUL: %$BASELINE -> %$NEW"
    BASELINE=$NEW
  fi
done

Deney Protokolu

Tek Degisiklik Kurali
YANLIS: Ayni anda 3 sey degistirip "daha hizli oldu" demek
  → Hangi degisiklik etkili oldugunu bilemezsin

DOGRU: Her seferinde TEK degisiklik yap
  → Neyin ise yaradigini kesin bilirsin
Rollback Stratejisi
bash
# Yontem 1: git stash (basit)
git stash         # deney oncesi
# ... deney ...
git stash pop     # basarisizsa geri al

# Yontem 2: git worktree (izole)
git worktree add /tmp/experiment-1 -b exp/perf-test
cd /tmp/experiment-1
# ... deney ...
# basarisizsa worktree'yi sil

# Yontem 3: checkpoint (karmasik deneyler)
git add -A && git commit -m "checkpoint: pre-experiment"
# ... deney ...
# basarisizsa: git reset --hard HEAD~1
Metrik Toplama
typescript
interface ExperimentResult {
  id: string
  description: string
  baseline: number
  result: number
  improvement: number  // yuzde
  accepted: boolean
  duration: number     // saniye
  timestamp: string
}

// Deney raporu
interface ExperimentReport {
  goal: string
  metric: string
  baselineValue: number
  finalValue: number
  totalExperiments: number
  accepted: number
  rejected: number
  totalImprovement: number  // yuzde
  experiments: ExperimentResult[]
}

Deney Sablonu

markdown
# Deney Raporu: [Hedef]

## Ozet
- Hedef: [metrik] < [esik]
- Baseline: [baslangic degeri]
- Final: [son deger]
- Iyilesme: [yuzde]
- Deneyler: [kabul]/[toplam]

## Deneyler

| # | Aciklama | Onceki | Sonraki | Degisim | Karar |
|---|----------|-------:|--------:|--------:|-------|
| 1 | Index ekle | 340ms | 280ms | -18% | KABUL |
| 2 | Redis cache | 280ms | 45ms | -84% | KABUL |
| 3 | JSON serializer | 45ms | 42ms | -7% | RED |

## Ogrenim
- En etkili: Redis cache (-84%)
- Degmez: JSON serializer degisimi (karmasiklik > kazanim)

Otomatik Deney Modu

bash
# experiment-loop.sh
# Kullanim: ./experiment-loop.sh "response_time" "200" "ms" 10

METRIC=$1       # olculecek metrik
TARGET=$2       # hedef deger
UNIT=$3         # birim
MAX_ROUNDS=$4   # max deney sayisi

ROUND=0
BASELINE=$(measure_$METRIC)

while [ $ROUND -lt $MAX_ROUNDS ]; do
  ROUND=$((ROUND + 1))

  # Claude'a optimize ettir
  claude -p "Optimize $METRIC. Current: ${BASELINE}${UNIT}. Target: <${TARGET}${UNIT}. Make ONE small change." --no-input

  # Olc
  NEW=$(measure_$METRIC)

  if [ "$NEW" -lt "$BASELINE" ]; then
    echo "Round $ROUND: KABUL ($BASELINE -> $NEW)"
    BASELINE=$NEW
    git add -A && git commit -m "experiment: $METRIC improved to ${NEW}${UNIT}"
  else
    echo "Round $ROUND: RED ($NEW >= $BASELINE)"
    git checkout .
  fi

  # Hedefe ulastik mi?
  if [ "$BASELINE" -le "$TARGET" ]; then
    echo "HEDEF ULASILDI: ${BASELINE}${UNIT} <= ${TARGET}${UNIT}"
    break
  fi
done

vibecosystem Entegrasyonu

  • profiler agent: Performans deneylerinde metrik toplama
  • nitro agent: Optimization deneylerini yonetme
  • tdd-guide agent: Coverage deneylerinde test yazma
  • verifier agent: Her deney sonrasi build/test dogrulama
  • self-learner agent: Basarili deneyleri pattern olarak kaydet
  • experiment-loop skill: Bu skill'in mevcut complementary'si

© vibeeval, 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 skills/experiment-engine of vibeeval/vibecosystem.

Open the folder on GitHubat commit 3b763b1

Compare with similar skills

Experiment Engine 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.

Experiment Engine compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Experiment Engine this skillvibeeval/vibecosystem531—~1.6kAutomated safety check: PassMIT
Web Application Testinganthropics/skills180k51 repos~966Automated safety check: PassApache-2.0
Diagnosing Bugsfossasia/eventyay-interpretation1.6k31 repos~2.1kAutomated safety check: PassApache-2.0
TDDfossasia/eventyay-interpretation1.6k28 repos~1.1kAutomated safety check: PassApache-2.0
TDD WorkflowhellangleZ/burn-in-cceverywhere-ralph11211 repos~2.4kAutomated safety check: PassNone
TDDsanity-io/sanity6.4k20 repos~1kAutomated safety check: PassMIT

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Categories

Questions about Experiment Engine

What does Experiment Engine do?

Otonom deney dongusu. An agent skill from vibeeval/vibecosystem. Experiment Engine is an agent skill from vibeeval/vibecosystem. Otonom deney dongusu.

When should I use Experiment Engine?

Experiment Engine fits situations like: testing & QA work in your project.

How do I install Experiment Engine in Claude Code?

Run `npx skills add vibeeval/vibecosystem --skill experiment-engine -a claude-code`. Or copy the skill folder (skills/experiment-engine in vibeeval/vibecosystem) into .claude/skills/experiment-engine in your project. Claude Code loads it when a task matches its description.

How do I install Experiment Engine in Codex?

Run `npx skills add vibeeval/vibecosystem --skill experiment-engine -a codex`. Or copy the skill folder (skills/experiment-engine in vibeeval/vibecosystem) into .agents/skills/experiment-engine in your project. Codex loads it when a task matches its description.

Can I use Experiment Engine 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 vibeeval/vibecosystem --skill experiment-engine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/experiment-engine, .gemini/skills/experiment-engine, .github/skills/experiment-engine and .opencode/skills/experiment-engine in your project.

What does Experiment Engine need to run?

Going by SKILL.md and its folder, Experiment Engine needs the command-line tools its instructions call (git, npx, curl and claude). Our summary lists: Node.js.

Does Experiment Engine access the network?

SKILL.md contains no URLs. Its commands use git, npx and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Experiment Engine 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 Experiment Engine use?

Experiment Engine 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 Experiment Engine use?

About 1.6k tokens (SKILL.md is roughly 6.3k 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 Experiment Engine?

Skills that share tags, products or a category with Experiment Engine: Web Application Testing (anthropics/skills, 180k stars), Diagnosing Bugs (fossasia/eventyay-interpretation, 1.6k stars), TDD (fossasia/eventyay-interpretation, 1.6k stars) and TDD Workflow (hellangleZ/burn-in-cceverywhere-ralph, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Experiment Engine?

vibeeval (a GitHub user) maintains it in vibeeval/vibecosystem, which has 531 GitHub stars. The repository holds 144 skills in this directory. The repository was last updated on August 8, 2026.

Source: vibeeval/vibecosystem on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.