Trellis Session Insight
mindfold-ai/Trellis
Reach into past AI conversation history through the trellis mem CLI.
Guide for implementing metacircular evaluators—interpreters that can interpret themselves.
Install Schemelike Metacircular Eval
$ npx skills add lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lazyFrogLOL/Harness_Engineering schemelike-metacircular-eval --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/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/schemelike-metacircular-eval .claude/skills/schemelike-metacircular-eval && 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 "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .claude/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-evalType 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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lazyFrogLOL/Harness_Engineering schemelike-metacircular-eval --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/schemelike-metacircular-eval .agents/skills/schemelike-metacircular-eval && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .agents/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lazyFrogLOL/Harness_Engineering schemelike-metacircular-eval --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/schemelike-metacircular-eval .cursor/skills/schemelike-metacircular-eval && 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 "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .cursor/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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/lazyFrogLOL/Harness_Engineering.git --path skills/schemelike-metacircular-eval--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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lazyFrogLOL/Harness_Engineering schemelike-metacircular-eval --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/schemelike-metacircular-eval .gemini/skills/schemelike-metacircular-eval && 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 "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .gemini/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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 lazyFrogLOL/Harness_Engineering schemelike-metacircular-evalInstalls 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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/schemelike-metacircular-eval .github/skills/schemelike-metacircular-eval && 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 "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .github/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lazyFrogLOL/Harness_Engineering schemelike-metacircular-eval --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lazyFrogLOL/Harness_Engineering.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/schemelike-metacircular-eval .opencode/skills/schemelike-metacircular-eval && 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 "schemelike-metacircular-eval" agent skill from https://github.com/lazyFrogLOL/Harness_Engineering/tree/master/skills/schemelike-metacircular-eval into .opencode/skills/schemelike-metacircular-eval/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "schemelike-metacircular-eval", 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
schemelike-metacircular-eval- GitHub stars
- 128
- Token cost
- ~2.6k tokens
- SKILL.md length
- 970 words
- Files
- 1
- Skills in repo
- 32
- Repo updated
- First seen
- Licence
- None found
At a glance
Guide for implementing metacircular evaluators—interpreters that can interpret themselves.
- Works in 2 steps: Minimal Self-Interpreting Core → Adding Features Safely
- Tasks that involve Debugging
- SKILL.md covers Overview, Critical Success Factors, Implementation Strategy and Environment Implementation, plus 6 more sections
- Calls python3
What it does
Schemelike Metacircular Eval is an agent skill from lazyFrogLOL/Harness_Engineering. Guide for implementing metacircular evaluators—interpreters that can interpret themselves. This skill should be used when building self-interpreting Scheme-like evaluators, debugging multi-level interpretation issues, or implementing language features like environments, closures, and special forms. Focuses on incremental development, continuous metacircular testing, and systematic debugging of nested interpretation failures.
Its SKILL.md is about 2.6k 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 Development, covering Debugging.
When your agent uses it
- Tasks that involve Debugging
Example prompts
- “/schemelike-metacircular-eval”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
- Minimal Self-Interpreting Core
- Adding Features Safely
What it can do on your machine
Read from SKILL.md and the folder at commit cae3b25. 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:
python3
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
Schemelike Metacircular Eval loads about 2.6k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 970 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
Without a licence we can't republish the file, so here is its outline and opening line. It has 970 words (~2,576 tokens).
“This skill provides systematic guidance for implementing metacircular evaluators—interpreters written in the language they interpret. The critical challenge is not just building a working interpreter, but building one that can interpret itself (the metacircular property).”
- name
- schemelike-metacircular-eval
Files
Just SKILL.md in skills/schemelike-metacircular-eval of lazyFrogLOL/Harness_Engineering.
Open the folder on GitHubat commit cae3b25
Compare with similar skills
Schemelike Metacircular Eval 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 |
|---|---|---|---|---|---|---|
| Schemelike Metacircular Eval this skilllazyFrogLOL/Harness_Engineering | 128 | — | ~2.6k | Automated safety check: Pass | None | |
| Trellis Session Insightmindfold-ai/Trellis | 15k | 4 repos | ~1.7k | Automated safety check: Pass | AGPL-3.0 | |
| Native Data FetchingCherryHQ/cherry-studio-app | 4k | 6 repos | ~2.9k | Automated safety check: Notes | MIT | |
| Debugging Executionsn8n-io/n8n | 207k | — | ~2.6k | Automated safety check: Pass | Custom licence | |
| Aoti Debugpytorch/pytorch | 104k | 1 repos | ~1.7k | Automated safety check: Pass | Custom licence | |
| Herdr Throwaway Reproductionherdrdev/herdr | 43k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Native Data Fetching
CherryHQ/cherry-studio-app
A skill your agent uses when implementing or debugging ANY network request, API call, or data fetching.
- Official
Debugging Executions
n8n-io/n8n
Debug failed or wrong-output workflow executions using executions tools.
Aoti Debug
pytorch/pytorch
Debug AOTInductor (AOTI) errors and crashes. An agent skill from pytorch/pytorch.
Herdr Throwaway Reproduction
herdrdev/herdr
Runs a disposable, uniquely named Herdr session inside an existing one so runtime, pane, terminal or API bugs can be reproduced without touching the main session.
Systematic Debugging
ultralisp/ultralisp
A skill your agent uses when encountering any bug, test failure, or unexpected behavior, before proposing fixes
More from lazyFrogLOL/Harness_Engineering
All 32 skills in this repoChess Best Move
lazyFrogLOL/Harness_Engineering
Guide for analyzing chess positions from images and determining optimal moves.
Crack 7z Hash
lazyFrogLOL/Harness_Engineering
This skill provides guidance for cracking 7z archive password hashes.
Distribution Search
lazyFrogLOL/Harness_Engineering
Guidance for finding probability distributions that satisfy specific statistical constraints such as KL divergence targets, entropy requirements, or moment conditions.
Feal Linear Cryptanalysis
lazyFrogLOL/Harness_Engineering
This skill provides guidance for FEAL cipher linear cryptanalysis tasks.
Gcode To Text
lazyFrogLOL/Harness_Engineering
Decode and interpret text content from G-code files by analyzing toolpath geometry and coordinate patterns.
Gpt2 Codegolf
lazyFrogLOL/Harness_Engineering
Guidance for implementing neural network inference (like GPT-2) under extreme code size constraints.
Related
Categories
Questions about Schemelike Metacircular Eval
What does Schemelike Metacircular Eval do?
Guide for implementing metacircular evaluators—interpreters that can interpret themselves. Schemelike Metacircular Eval is an agent skill from lazyFrogLOL/Harness_Engineering. Guide for implementing metacircular evaluators—interpreters that can interpret themselves.
When should I use Schemelike Metacircular Eval?
Schemelike Metacircular Eval fits situations like: tasks that involve Debugging.
How do I install Schemelike Metacircular Eval in Claude Code?
Run `npx skills add lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a claude-code`. Or copy the skill folder (skills/schemelike-metacircular-eval in lazyFrogLOL/Harness_Engineering) into .claude/skills/schemelike-metacircular-eval in your project. Claude Code loads it when a task matches its description.
How do I install Schemelike Metacircular Eval in Codex?
Run `npx skills add lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a codex`. Or copy the skill folder (skills/schemelike-metacircular-eval in lazyFrogLOL/Harness_Engineering) into .agents/skills/schemelike-metacircular-eval in your project. Codex loads it when a task matches its description.
Can I use Schemelike Metacircular Eval 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 lazyFrogLOL/Harness_Engineering --skill schemelike-metacircular-eval -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/schemelike-metacircular-eval, .gemini/skills/schemelike-metacircular-eval, .github/skills/schemelike-metacircular-eval and .opencode/skills/schemelike-metacircular-eval in your project.
What does Schemelike Metacircular Eval need to run?
Going by SKILL.md and its folder, Schemelike Metacircular Eval needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Schemelike Metacircular Eval 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 Schemelike Metacircular Eval 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 Schemelike Metacircular Eval use?
No licence was found for Schemelike Metacircular Eval or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Schemelike Metacircular Eval use?
About 2.6k tokens (SKILL.md is roughly 10k 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 Schemelike Metacircular Eval?
Skills that share tags, products or a category with Schemelike Metacircular Eval: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Debugging Executions (n8n-io/n8n, 207k stars) and Aoti Debug (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Schemelike Metacircular Eval?
lazyFrogLOL (a GitHub user) maintains it in lazyFrogLOL/Harness_Engineering, which has 128 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on May 18, 2026.
Source: lazyFrogLOL/Harness_Engineering on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.