Eval
agentevals-dev/agentevals
Evaluate and score agent behavior against a golden reference.
Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment).
Install Exploring LLM Evaluations
$ npx skills add PostHog/posthog --skill exploring-llm-evaluations -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install PostHog/posthog exploring-llm-evaluations --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/PostHog/posthog.git skills-src && mkdir -p .claude/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .claude/skills/exploring-llm-evaluations && 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 "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .claude/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluationsType 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 PostHog/posthog --skill exploring-llm-evaluations -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install PostHog/posthog exploring-llm-evaluations --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PostHog/posthog.git skills-src && mkdir -p .agents/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .agents/skills/exploring-llm-evaluations && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .agents/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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 PostHog/posthog --skill exploring-llm-evaluations -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install PostHog/posthog exploring-llm-evaluations --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PostHog/posthog.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .cursor/skills/exploring-llm-evaluations && 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 "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .cursor/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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/PostHog/posthog.git --path products/ai_observability/skills/exploring-llm-evaluations--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 PostHog/posthog --skill exploring-llm-evaluations -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install PostHog/posthog exploring-llm-evaluations --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PostHog/posthog.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .gemini/skills/exploring-llm-evaluations && 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 "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .gemini/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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 PostHog/posthog exploring-llm-evaluationsInstalls 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 PostHog/posthog --skill exploring-llm-evaluations -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/PostHog/posthog.git skills-src && mkdir -p .github/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .github/skills/exploring-llm-evaluations && 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 "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .github/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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 PostHog/posthog --skill exploring-llm-evaluations -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install PostHog/posthog exploring-llm-evaluations --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/PostHog/posthog.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/products/ai_observability/skills/exploring-llm-evaluations .opencode/skills/exploring-llm-evaluations && 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 "exploring-llm-evaluations" agent skill from https://github.com/PostHog/posthog/tree/master/products/ai_observability/skills/exploring-llm-evaluations into .opencode/skills/exploring-llm-evaluations/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "exploring-llm-evaluations", 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
exploring-llm-evaluations- GitHub stars
- 40k
- Token cost
- ~5.7k tokens
- SKILL.md length
- 2,126 words
- Files
- 1
- Skills in repo
- 252
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment).
- Works in 4 steps: Find the evaluation → Break down pass, fail, and N/A → Read the failing runs → …
- The user asks to debug why an evaluation is failing
- SKILL.md covers Tools, Event schema, Workflow: investigate why an… and Workflow: run an evaluation…, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Exploring LLM Evaluations is an agent skill from PostHog/posthog, published by the product's own GitHub organization. Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific generations, query individual results, and set up scheduled reports on an evaluation. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, inspect sentiment…
Its SKILL.md is about 5.7k 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 and Observability. It works with PostHog. The repository describes itself as: :hedgehog: PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error…
When your agent uses it
- The user asks to debug why an evaluation is failing
- Surface common failure modes
- Compare results across filters
- Dry-run a Hog evaluator
Example prompts
- “/exploring-llm-evaluations”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
- Find the evaluation
- Break down pass, fail, and N/A
- Read the failing runs
- Drill into example failing runs
What it can do on your machine
Read from SKILL.md and the folder at commit 10f9ad7. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json and sql).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Exploring LLM Evaluations loads about 5.7k tokens when it runs. Until then it costs about 150 tokens; SKILL.md has 2,126 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 2,126 words (~5,718 tokens).
“For stored offline experiment comparisons, scorer history, or externally computed result uploads, use analyzing-offline-evaluations. Offline experiments use pinned scorer configurations and separate result APIs from the online evaluations below.”
- name
- exploring-llm-evaluations
Files
Just SKILL.md in products/ai_observability/skills/exploring-llm-evaluations of PostHog/posthog.
Open the folder on GitHubat commit 10f9ad7
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in PostHog/posthog, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Exploring LLM Evaluations 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 |
|---|---|---|---|---|---|---|
| Exploring LLM Evaluations this skillPostHog/posthog | 40k | — | ~5.7k | Automated safety check: Pass | Custom licence | |
| Evalagentevals-dev/agentevals | 162 | — | ~904 | Automated safety check: Pass | Apache-2.0 | |
| RAG Observability Evalssickn33/agentic-awesome-skills | 47k | 2 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Dt Obs GenaiDynatrace/dynatrace-for-ai | 161 | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | |
| Failproof AI SDK IntegrationFailproofAI/failproofai | 5.3k | — | ~6k | Automated safety check: Pass | Custom licence | |
| Looperksimback/looper | 710 | — | ~2.7k | Automated safety check: Notes | MIT |
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Helps instrument a custom Python or TypeScript agent to record events for Failproof AI, verify what gets written, and run an evaluator worker that scores the runs.
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Related
Works with
Categories
Questions about Exploring LLM Evaluations
What does Exploring LLM Evaluations do?
Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment). Exploring LLM Evaluations is an agent skill from PostHog/posthog, published by the product's own GitHub organization. Investigate AI observability evaluations — hog (deterministic code-based), llmjudge (LLM-prompt-based), and sentiment (user-message sentiment).
When should I use Exploring LLM Evaluations?
Exploring LLM Evaluations fits situations like: the user asks to debug why an evaluation is failing; surface common failure modes; compare results across filters; dry-run a Hog evaluator.
How do I install Exploring LLM Evaluations in Claude Code?
Run `npx skills add PostHog/posthog --skill exploring-llm-evaluations -a claude-code`. Or copy the skill folder (products/ai_observability/skills/exploring-llm-evaluations in PostHog/posthog) into .claude/skills/exploring-llm-evaluations in your project. Claude Code loads it when a task matches its description.
How do I install Exploring LLM Evaluations in Codex?
Run `npx skills add PostHog/posthog --skill exploring-llm-evaluations -a codex`. Or copy the skill folder (products/ai_observability/skills/exploring-llm-evaluations in PostHog/posthog) into .agents/skills/exploring-llm-evaluations in your project. Codex loads it when a task matches its description.
Can I use Exploring LLM Evaluations 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 PostHog/posthog --skill exploring-llm-evaluations -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exploring-llm-evaluations, .gemini/skills/exploring-llm-evaluations, .github/skills/exploring-llm-evaluations and .opencode/skills/exploring-llm-evaluations in your project.
What does Exploring LLM Evaluations need to run?
SKILL.md names no scripts, command-line tools or credentials: Exploring LLM Evaluations is instructions for the agent only.
Does Exploring LLM Evaluations access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Exploring LLM Evaluations 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 Exploring LLM Evaluations use?
Exploring LLM Evaluations has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Exploring LLM Evaluations use?
About 5.7k tokens (SKILL.md is roughly 23k 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 Exploring LLM Evaluations?
Skills that share tags, products or a category with Exploring LLM Evaluations: Eval (agentevals-dev/agentevals, 162 stars), RAG Observability Evals (sickn33/agentic-awesome-skills, 47k stars), Dt Obs Genai (Dynatrace/dynatrace-for-ai, 161 stars) and Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Exploring LLM Evaluations?
PostHog (a GitHub organization, an official publisher) maintains it in PostHog/posthog, which has 40,182 GitHub stars. The repository holds 252 skills in this directory. The repository was last updated on October 8, 2026.
Source: PostHog/posthog on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.