Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add telagod/code-abyss --skill mlgit clone --depth 1 https://github.com/telagod/code-abyssWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/telagod/code-abyss/ml)<a href="https://agentmods.dev/skills/telagod/code-abyss/ml"><img src="https://agentmods.dev/badge/skills/telagod/code-abyss/ml/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/telagod/code-abyss/ml"><img src="https://agentmods.dev/badge/skills/telagod/code-abyss/ml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.00573 |
| Opus 5.5 | $0.00046 | $0.00229 |
| Sonnet 5.5 | $0.00023 | $0.00115 |
| Haiku 4.5 | $0.00011 | $0.00057 |
Grade A, and why
ml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
ML — approach, data, evals, LLM craft, traps
Rule content lives in the five files below; this SKILL.md only routes
(doctrine/04-maintenance.md governs edits to this bundle too).
Route by moment
| 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.
Scope and neighbors
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 stance
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.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 39 lines · 114 tokens per session scan A 8396ad545ca3
ml is a skill published in the GitHub repository telagod/code-abyss (244 stars, last pushed 2mo ago), licensed MIT. It adds 114 tokens to every session and 573 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
hipson-gpt
Use as a reference package for prompt architecture, reasoning, agent orchestration, AI coding workflows, fullstack guidance, multimodal prompting, and evaluation/security guardrails.
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
prompt-optimization
Use when auditing an existing prompt for clarity, structure, or completeness issues, fixing vague instructions, or producing a before/after optimization report.
v4-best-practices
Use when working with deepseek-v4-pro or deepseek-v4-flash in thinking mode on multi-step or plan-driven tasks. Provides rules to prevent stale references, unverified plan assumptions, and vague plan output.