Clawpathy Autoresearch
ClawBio/ClawBio
Eval-driven skill tuning. An agent skill from ClawBio/ClawBio.
Configures and runs LLM evaluation using Promptfoo framework.
$ npx skills add daymade/claude-code-skills --skill promptfoo-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install daymade/claude-code-skills promptfoo-evaluation --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/daymade/claude-code-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/promptfoo-evaluation .claude/skills/promptfoo-evaluation && 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 "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .claude/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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/daymade/claude-code-skills/tree/main/promptfoo-evaluationType 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 daymade/claude-code-skills --skill promptfoo-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install daymade/claude-code-skills promptfoo-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/promptfoo-evaluation .agents/skills/promptfoo-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .agents/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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 daymade/claude-code-skills --skill promptfoo-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install daymade/claude-code-skills promptfoo-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/promptfoo-evaluation .cursor/skills/promptfoo-evaluation && 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 "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .cursor/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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/daymade/claude-code-skills.git --path promptfoo-evaluation--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 daymade/claude-code-skills --skill promptfoo-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install daymade/claude-code-skills promptfoo-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/promptfoo-evaluation .gemini/skills/promptfoo-evaluation && 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 "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .gemini/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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 daymade/claude-code-skills promptfoo-evaluationInstalls 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 daymade/claude-code-skills --skill promptfoo-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/promptfoo-evaluation .github/skills/promptfoo-evaluation && 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 "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .github/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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 daymade/claude-code-skills --skill promptfoo-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install daymade/claude-code-skills promptfoo-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/daymade/claude-code-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/promptfoo-evaluation .opencode/skills/promptfoo-evaluation && 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 "promptfoo-evaluation" agent skill from https://github.com/daymade/claude-code-skills/tree/main/promptfoo-evaluation into .opencode/skills/promptfoo-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "promptfoo-evaluation", 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.
promptfoo-evaluationConfigures and runs LLM evaluation using Promptfoo framework.
Promptfoo Evaluation is an agent skill from daymade/claude-code-skills. Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".
Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/promptfoo_api.md` and `scripts/metrics.py`).
It sits in AI & LLM Engineering, covering LLM evaluation, Quizzes and assessments and Prompt engineering. It works with Python and OpenAI. The repository describes itself as: Professional Claude Code skills marketplace featuring production-ready skills for enhanced development workflows. The licence is MIT.
Read from SKILL.md and the folder at commit 3c268d6. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
npxFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
promptfoo.devFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
ANTHROPIC_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Promptfoo Evaluation loads about 3k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 558 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.
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); the scripts in this folder are not scanned.
The full file from daymade/claude-code-skills at commit 3c268d6, republished under its MIT licence (© daymade). 558 words, ~3,022 tokens.
.claude/skills/promptfoo-evaluation/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.This skill provides guidance for configuring and running LLM evaluations using Promptfoo, an open-source CLI tool for testing and comparing LLM outputs.
# Initialize a new evaluation project
npx promptfoo@latest init
# Run evaluation
npx promptfoo@latest eval
# View results in browser
npx promptfoo@latest viewA typical Promptfoo project structure:
project/
├── promptfooconfig.yaml # Main configuration
├── prompts/
│ ├── system.md # System prompt
│ └── chat.json # Chat format prompt
├── tests/
│ └── cases.yaml # Test cases
└── scripts/
└── metrics.py # Custom Python assertions# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: "My LLM Evaluation"
# Prompts to test
prompts:
- file://prompts/system.md
- file://prompts/chat.json
# Models to compare
providers:
- id: anthropic:messages:claude-sonnet-4-6
label: Claude-Sonnet-4.6
- id: openai:gpt-4.1
label: GPT-4.1
# Test cases
tests: file://tests/cases.yaml
# Concurrency control (MUST be under commandLineOptions, NOT top-level)
commandLineOptions:
maxConcurrency: 2
# Default assertions for all tests
defaultTest:
assert:
- type: python
value: file://scripts/metrics.py:custom_assert
- type: llm-rubric
value: |
Evaluate the response quality on a 0-1 scale.
threshold: 0.7
# Output path
outputPath: results/eval-results.jsonYou are a helpful assistant.
Task: {{task}}
Context: {{context}}[
{"role": "system", "content": "{{system_prompt}}"},
{"role": "user", "content": "{{user_input}}"}
]Embed examples directly in prompt or use chat format with assistant messages:
[
{"role": "system", "content": "{{system_prompt}}"},
{"role": "user", "content": "Example input: {{example_input}}"},
{"role": "assistant", "content": "{{example_output}}"},
{"role": "user", "content": "Now process: {{actual_input}}"}
]- description: "Test case 1"
vars:
system_prompt: file://prompts/system.md
user_input: "Hello world"
# Load content from files
context: file://data/context.txt
assert:
- type: contains
value: "expected text"
- type: python
value: file://scripts/metrics.py:custom_check
threshold: 0.8Create a Python file for custom assertions (e.g., scripts/metrics.py):
def get_assert(output: str, context: dict) -> dict:
"""Default assertion function."""
vars_dict = context.get('vars', {})
# Access test variables
expected = vars_dict.get('expected', '')
# Return result
return {
"pass": expected in output,
"score": 0.8,
"reason": "Contains expected content",
"named_scores": {"relevance": 0.9}
}
def custom_check(output: str, context: dict) -> dict:
"""Custom named assertion."""
word_count = len(output.split())
passed = 100 <= word_count <= 500
return {
"pass": passed,
"score": min(1.0, word_count / 300),
"reason": f"Word count: {word_count}"
}Key points:
get_assertfile://path.py:function_namebool, float (score), or dict with pass/score/reasoncontext['vars']assert:
- type: llm-rubric
value: |
Evaluate the response based on:
1. Accuracy of information
2. Clarity of explanation
3. Completeness
Score 0.0-1.0 where 0.7+ is passing.
threshold: 0.7
provider: openai:gpt-4.1 # Optional: override grader modelWhen using a relay/proxy API, each llm-rubric assertion needs its own provider config with apiBaseUrl. Otherwise the grader falls back to the default Anthropic/OpenAI endpoint and gets 401 errors:
assert:
- type: llm-rubric
value: |
Evaluate quality on a 0-1 scale.
threshold: 0.7
provider:
id: anthropic:messages:claude-sonnet-4-6
config:
apiBaseUrl: https://your-relay.example.com/apiBest practices:
threshold to set minimum passing scorellm-rubric must have its own provider with apiBaseUrl — the main provider's apiBaseUrl is NOT inherited| Type | Usage | Example |
|---|---|---|
contains | Check substring | value: "hello" |
icontains | Case-insensitive | value: "HELLO" |
equals | Exact match | value: "42" |
regex | Pattern match | value: "\\d{4}" |
python | Custom logic | value: file://script.py |
llm-rubric | LLM grading | value: "Is professional" |
latency | Response time | threshold: 1000 |
All file:// paths are resolved relative to promptfooconfig.yaml location (NOT the YAML file containing the reference). This is a common gotcha when tests: references a separate YAML file — the file:// paths inside that test file still resolve from the config root.
# Load file content as variable
vars:
content: file://data/input.txt
# Load prompt from file
prompts:
- file://prompts/main.md
# Load test cases from file
tests: file://tests/cases.yaml
# Load Python assertion
assert:
- type: python
value: file://scripts/check.py:validate# Basic run
npx promptfoo@latest eval
# With specific config
npx promptfoo@latest eval --config path/to/config.yaml
# Output to file
npx promptfoo@latest eval --output results.json
# Filter tests
npx promptfoo@latest eval --filter-metadata category=math
# View results
npx promptfoo@latest viewWhen using an API relay or proxy instead of direct Anthropic/OpenAI endpoints:
providers:
- id: anthropic:messages:claude-sonnet-4-6
label: Claude-Sonnet-4.6
config:
max_tokens: 4096
apiBaseUrl: https://your-relay.example.com/api # Promptfoo appends /v1/messages
# CRITICAL: maxConcurrency MUST be under commandLineOptions (NOT top-level)
commandLineOptions:
maxConcurrency: 1 # Respect relay rate limitsKey rules:
apiBaseUrl goes in providers[].config — Promptfoo appends /v1/messages automaticallymaxConcurrency must be under commandLineOptions: — placing it at top level is silently ignoredmaxConcurrency: 1 to avoid concurrent request limits (generation + grading share the same pool)ANTHROPIC_API_KEY env varPython not found:
export PROMPTFOO_PYTHON=python3Large outputs truncated:
Outputs over 30000 characters are truncated. Use head_limit in assertions.
File not found errors:
All file:// paths resolve relative to promptfooconfig.yaml location.
maxConcurrency ignored (shows "up to N at a time"):
maxConcurrency must be under commandLineOptions:, not at the YAML top level. This is a common mistake.
LLM-as-judge returns 401 with relay API:
Each llm-rubric assertion must have its own provider with apiBaseUrl. The main provider config is not inherited by grader assertions.
HTML tags in model output inflating metrics:
Models may output <br>, <b>, etc. in structured content. Strip HTML in Python assertions before measuring:
import re
clean_text = re.sub(r'<[^>]+>', '', raw_text)Use the echo provider to preview rendered prompts without making API calls:
# promptfooconfig-preview.yaml
providers:
- echo # Returns prompt as output, no API calls
tests:
- vars:
input: "test content"Use cases:
# Run preview mode
npx promptfoo@latest eval --config promptfooconfig-preview.yamlCost: Free - no API tokens consumed.
For complex few-shot learning with full examples:
[
{"role": "system", "content": "{{system_prompt}}"},
// Few-shot Example 1
{"role": "user", "content": "Task: {{example_input_1}}"},
{"role": "assistant", "content": "{{example_output_1}}"},
// Few-shot Example 2 (optional)
{"role": "user", "content": "Task: {{example_input_2}}"},
{"role": "assistant", "content": "{{example_output_2}}"},
// Actual test
{"role": "user", "content": "Task: {{actual_input}}"}
]Test case configuration:
tests:
- vars:
system_prompt: file://prompts/system.md
# Few-shot examples
example_input_1: file://data/examples/input1.txt
example_output_1: file://data/examples/output1.txt
example_input_2: file://data/examples/input2.txt
example_output_2: file://data/examples/output2.txt
# Actual test
actual_input: file://data/test1.txtBest practices:
For Chinese/long-form content evaluations (10k+ characters):
Configuration:
providers:
- id: anthropic:messages:claude-sonnet-4-6
config:
max_tokens: 8192 # Increase for long outputs
defaultTest:
assert:
- type: python
value: file://scripts/metrics.py:check_lengthPython assertion for text metrics:
import re
def strip_tags(text: str) -> str:
"""Remove HTML tags for pure text."""
return re.sub(r'<[^>]+>', '', text)
def check_length(output: str, context: dict) -> dict:
"""Check output length constraints."""
raw_input = context['vars'].get('raw_input', '')
input_len = len(strip_tags(raw_input))
output_len = len(strip_tags(output))
reduction_ratio = 1 - (output_len / input_len) if input_len > 0 else 0
return {
"pass": 0.7 <= reduction_ratio <= 0.9,
"score": reduction_ratio,
"reason": f"Reduction: {reduction_ratio:.1%} (target: 70-90%)",
"named_scores": {
"input_length": input_len,
"output_length": output_len,
"reduction_ratio": reduction_ratio
}
}Project: Chinese short-video content curation from long transcripts
Structure:
tiaogaoren/
├── promptfooconfig.yaml # Production config
├── promptfooconfig-preview.yaml # Preview config (echo provider)
├── prompts/
│ ├── tiaogaoren-prompt.json # Chat format with few-shot
│ └── v4/system-v4.md # System prompt
├── tests/cases.yaml # 3 test samples
├── scripts/metrics.py # Custom metrics (reduction ratio, etc.)
├── data/ # 5 samples (2 few-shot, 3 eval)
└── results/See: ./tiaogaoren/ (example project root) for full implementation.
For detailed API reference and advanced patterns, see references/promptfoo_api.md.
© daymade, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files (scripts, references) in promptfoo-evaluation of daymade/claude-code-skills.
Open the folder on GitHubat commit 3c268d6
Promptfoo Evaluation 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 |
|---|---|---|---|---|---|---|
| Promptfoo Evaluation this skilldaymade/claude-code-skills | 1.4k | — | ~3k | Automated safety check: Pass | MIT | |
| Clawpathy AutoresearchClawBio/ClawBio | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Azure AI Projects Python SDKmicrosoft/skills | 3.1k | 6 repos | ~2.8k | Automated safety check: Pass | MIT | |
| Agents Best PracticesDenisSergeevitch/agents-best-practices | 2.4k | — | ~7.4k | Automated safety check: Pass | MIT | |
| Opikcomet-ml/opik-mcp | 219 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Guidance Constrained GenerationOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~3.6k | Automated safety check: Pass | MIT |
ClawBio/ClawBio
Eval-driven skill tuning. An agent skill from ClawBio/ClawBio.
microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
DenisSergeevitch/agents-best-practices
A skill your agent uses when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain.
comet-ml/opik-mcp
Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST).
Orchestra-Research/AI-Research-SKILLs
Constrains language model output with regex, selections and grammars using the Guidance library, so JSON, XML, code or formatted fields come out valid.
woocommerce/woocommerce-ios
Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring.
daymade/claude-code-skills
This skill should be used when comparing two videos to analyze compression results or quality differences.
daymade/claude-code-skills
Generates professional animated CLI demos as GIFs using VHS terminal recordings.
daymade/claude-code-skills
Converts DOCX/PDF/PPTX and saved HTML/HTM to high-quality Markdown with automatic post-processing.
daymade/claude-code-skills
Generates several distinct, clickable HTML interaction prototypes for one product surface into a Design Board and collects selection/remix feedback before implementation.
daymade/claude-code-skills
Diagnoses and repairs repository setup and guarded Git workflows for Claude Code or Codex — environment repair, startup sync, hook auditing, collaborator handoff.
daymade/claude-code-skills
Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/ REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series…
Categories
Configures and runs LLM evaluation using Promptfoo framework. Promptfoo Evaluation is an agent skill from daymade/claude-code-skills. Configures and runs LLM evaluation using Promptfoo framework.
Promptfoo Evaluation fits situations like: setting up prompt testing; creating evaluation configs (promptfooconfig.yaml); writing Python custom assertions; implementing llm-rubric for LLM-as-judge.
Run `npx skills add daymade/claude-code-skills --skill promptfoo-evaluation -a claude-code`. Or copy the skill folder (promptfoo-evaluation in daymade/claude-code-skills) into .claude/skills/promptfoo-evaluation in your project. Claude Code loads it when a task matches its description.
Run `npx skills add daymade/claude-code-skills --skill promptfoo-evaluation -a codex`. Or copy the skill folder (promptfoo-evaluation in daymade/claude-code-skills) into .agents/skills/promptfoo-evaluation in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add daymade/claude-code-skills --skill promptfoo-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/promptfoo-evaluation, .gemini/skills/promptfoo-evaluation, .github/skills/promptfoo-evaluation and .opencode/skills/promptfoo-evaluation in your project.
Going by SKILL.md and its folder, Promptfoo Evaluation needs Python for the scripts in its folder, the command-line tools its instructions call (npx) and credentials named ANTHROPIC_API_KEY. Our summary lists: Python 3; Node.js; A credential in ANTHROPIC_API_KEY.
SKILL.md names 1 domain. In commands or code: promptfoo.dev; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Promptfoo Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Promptfoo Evaluation: Clawpathy Autoresearch (ClawBio/ClawBio, 1.2k stars), Azure AI Projects Python SDK (microsoft/skills, 3.1k stars), Agents Best Practices (DenisSergeevitch/agents-best-practices, 2.4k stars) and Opik (comet-ml/opik-mcp, 219 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
daymade (a GitHub user) maintains it in daymade/claude-code-skills, which has 1,443 GitHub stars. The repository holds 102 skills in this directory. The repository was last updated on October 7, 2026.
Source: daymade/claude-code-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.