Machine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working…
$ npx skills add telagod/code-abyss --skill ml -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install telagod/code-abyss ml --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/telagod/code-abyss.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/_kernel/ml .claude/skills/ml && 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 "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .claude/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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/telagod/code-abyss/tree/main/skills/_kernel/mlType 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 telagod/code-abyss --skill ml -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install telagod/code-abyss ml --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/_kernel/ml .agents/skills/ml && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .agents/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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 telagod/code-abyss --skill ml -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install telagod/code-abyss ml --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/_kernel/ml .cursor/skills/ml && 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 "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .cursor/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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/telagod/code-abyss.git --path skills/_kernel/ml--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 telagod/code-abyss --skill ml -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install telagod/code-abyss ml --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/_kernel/ml .gemini/skills/ml && 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 "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .gemini/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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 telagod/code-abyss mlInstalls 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 telagod/code-abyss --skill ml -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/_kernel/ml .github/skills/ml && 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 "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .github/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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 telagod/code-abyss --skill ml -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install telagod/code-abyss ml --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/telagod/code-abyss.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/_kernel/ml .opencode/skills/ml && 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 "ml" agent skill from https://github.com/telagod/code-abyss/tree/main/skills/_kernel/ml into .opencode/skills/ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml", 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.
mlMachine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working…
ML is an agent skill from telagod/code-abyss. Machine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working with training/eval data or labels; building or reviewing evals for models and LLM features; designing RAG, structured output, or agent pipelines; or diagnosing why a model/LLM feature underperforms. Method-selection ladder, data and leakage discipline, eval-as-spec rules, LLM-era craft, and a trap catalog.
Its SKILL.md is about 570 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `approach.md`, `data.md` and `evals.md`).
It sits in AI & LLM Engineering, covering LLM evaluation, Structured output and tool calling and Retrieval-augmented generation. The repository describes itself as: Give your AI coding agent a personality. Composable persona + style + skills for Claude Code, Codex, Gemini CLI & OpenClaw. Ships Tech Persona Card v1.0 spec. The licence is MIT.
Read from SKILL.md and the folder at commit 2544577. 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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
ML loads about 566 tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 245 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); files beside SKILL.md are not scanned.
The full file from telagod/code-abyss at commit 2544577, republished under its MIT licence (© telagod). 245 words, ~566 tokens.
.claude/skills/ml/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Rule content lives in the five files below; this SKILL.md only routes
(doctrine/04-maintenance.md governs edits to this bundle too).
| You are about to… | Read (in this folder) |
|---|---|
| Decide whether ML/an LLM is warranted, and which method rung to use | approach.md |
| Touch a dataset, labels, or splits; suspect a score is too good | data.md |
| Define success, build/judge an eval, or assess someone's metric claim | evals.md |
| Build with LLMs: prompts, RAG, structured output, agents, model choice | llm.md |
| Diagnose an underperforming model or LLM feature | data.md §1 first (read real failures), then llm.md §3 if RAG, traps.md to name the pattern |
| Review an ML project's health; name why a claim or pipeline smells wrong | traps.md |
A new ML feature usually runs approach.md (interrogate + pick the rung) →
evals.md §1 (eval BEFORE build) → data.md → then llm.md if the rung is LLM-shaped
→ skim traps.md §C before finalizing any launch or monitoring plan.
Modeling and evaluation judgment. The serving infrastructure around a model is ordinary
backend (backend bundle: APIs, queues, operate.md); experiment execution discipline is
methods (investigate/verify); whether to delegate → doctrine.
The eval is the spec; anything unmeasured is folklore. Look at the data with your
own eyes (data.md §1), climb the method ladder from the cheapest rung (approach.md
§3), and treat every surprising score as leakage until disproven (data.md §2). The
failure mode of this field is not bad models — it is unearned confidence in numbers.
© telagod, 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 5 other files in skills/_kernel/ml of telagod/code-abyss.
Open the folder on GitHubat commit 2544577
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 telagod/code-abyss, which our catalogue first saw on October 7, 2026.
ML 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 |
|---|---|---|---|---|---|---|
| ML this skilltelagod/code-abyss | 244 | 1 repos | ~566 | Automated safety check: Pass | MIT | |
| Agent Harness DesignAnastasiyaW/codex-claude-code-config | 154 | — | ~764 | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Quality FlywheelGoogleCloudPlatform/vertex-ai-samples | 791 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Convex Agentswaynesutton/builder-skills | 404 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Evaluate RAGai-evals-course/evals-skills | 1.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 |
AnastasiyaW/codex-claude-code-config
Designing agent harnesses and tool systems — risk taxonomy for tools, permission decisions, draft/commit pattern, structured tool results, agent budgets (10 types), context trust labels against…
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.
GoogleCloudPlatform/vertex-ai-samples
Evaluate and improve GenAI models and agents using the Google GenAI Evaluation SDK.
waynesutton/builder-skills
Builds AI agents on the Convex agent component: threads, messages, tools that call queries and mutations, streaming, RAG with vector search, and workflows for multi step jobs.
ai-evals-course/evals-skills
Guides evaluation of a RAG system by diagnosing failures in traces, building a retrieval test set and scoring retrieval and generation separately.
intertwine/dspy-agent-skills
Build DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization.
telagod/code-abyss
Scans code with a bundled Node scanner for injection, secret leaks and other dangerous patterns, and requires documented decisions for accepted risks.
telagod/code-abyss
Distills a recurring agent voice from conversations into a restricted Persona Voice Card, validates it for schema, safety and distinctness, and prepares it for community submission.
telagod/code-abyss
Distills repeated workflows into new skills, improves existing ones and promotes them through local, project and community tiers after a default-deny safety scan.
telagod/code-abyss
Routes Word document tasks to the right tool: pandoc for text, raw OOXML for structure and comments, docx-js for new files, and a mandatory redlining flow for edits to others' documents.
telagod/code-abyss
Picks the right Python library or CLI tool for a PDF task, text and table extraction, merging, splitting, OCR, watermarking or form filling, and points to a matching recipe.
telagod/code-abyss
Checks what a code change touched, how far its impact reaches and whether design docs, tests and README files kept up, before commit or in review.
Categories
Machine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working…. ML is an agent skill from telagod/code-abyss. Machine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working with training/eval data or labels; building or reviewing evals for models and LLM features; designing RAG, structured output, or agent pipelines; or diagnosing why a model/LLM feature underperforms.
ML fits situations like: tasks that involve LLM evaluation; tasks that involve Structured output and tool calling; tasks that involve Retrieval-augmented generation.
Run `npx skills add telagod/code-abyss --skill ml -a claude-code`. Or copy the skill folder (skills/_kernel/ml in telagod/code-abyss) into .claude/skills/ml in your project. Claude Code loads it when a task matches its description.
Run `npx skills add telagod/code-abyss --skill ml -a codex`. Or copy the skill folder (skills/_kernel/ml in telagod/code-abyss) into .agents/skills/ml 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 telagod/code-abyss --skill ml -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml, .gemini/skills/ml, .github/skills/ml and .opencode/skills/ml in your project.
SKILL.md names no scripts, command-line tools or credentials: ML is instructions for the agent only.
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.
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.
ML is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 566 tokens (SKILL.md is roughly 2.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with ML: Agent Harness Design (AnastasiyaW/codex-claude-code-config, 154 stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Quality Flywheel (GoogleCloudPlatform/vertex-ai-samples, 791 stars) and Convex Agents (waynesutton/builder-skills, 404 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
telagod (a GitHub user) maintains it in telagod/code-abyss, which has 244 GitHub stars. The repository holds 38 skills in this directory. The repository was last updated on July 19, 2026.
Source: telagod/code-abyss on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
