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 agentmods add skills/thematteroftime/agentic-md-for-dummies/creatornpx skills add thematteroftime/agentic-md-for-dummies --skill creatorgit clone --depth 1 https://github.com/thematteroftime/agentic-md-for-dummiesWhat 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 | $0.00057 | $0.01747 |
| Opus 5 | $0.00028 | $0.00873 |
| Sonnet 5 | $0.00011 | $0.00349 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
creator 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creator — meta-skill that builds experiment-orchestration skills
You are generating a skill that lives inside another project. The output is a working clone of paper-to-experiment adapted to that project's simulation framework.
Hard rules
- Inspect first, ask second. Read the user's project root before any question. Files >> their description.
- Don't invent fields. If a question's answer can't be inferred from the codebase, surface it; don't guess.
- Skill output is text-only. No GPU, no simulations, no
--applyto user files outside.claude/skills/<generated>/. - Generated skill must validate. Before handing off, dry-run the schema against one of the user's existing configs.
- Single source of class registry. Generated skill must point at one runtime registry (e.g.
tools/registry.py), never at scattered references. - AI only produces config; never executes. This contract is non-negotiable in every generated skill.
Process
1. Inspect repo → 2. Interview (17 Q) → 3. Fill profile
↓
6. Hand off ← 5. Self-test ← 4. Generate skill
Step 1 — Inspect repo
Read in order:
pyproject.toml/package.json/Cargo.toml(language + deps)- The user's main entry script (whatever runs a simulation)
- One existing config file (the kind of artifact the new skill will produce)
docs/for any README / architecture notes
Catalog: language, config format (JSON/YAML/TOML/Python), entry-script signature, output dir convention, existing analyzer/plotter classes.
Step 2 — Interview
Open templates/interview.md, ask the 17 questions in order. One question per turn. Multiple-choice when possible. Skip questions whose answers you already inferred — and say so ("from inspecting <file>, I'll assume X; correct?").
Step 3 — Fill profile
Save user answers + inspections to docs/specs/<TS>-<framework-name>-profile.md using templates/framework_profile.md as scaffold. This file is the input contract for skill generation.
What ships with it
8 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.
- references/distillation.md 2.3 KB
- templates/framework_profile.md 1.7 KB
- templates/interview.md 1.7 KB
- templates/skill_scaffold/design.md.tmpl 6.3 KB
- templates/skill_scaffold/registry.md.tmpl 3.6 KB
- templates/skill_scaffold/schema.json.tmpl 4.6 KB
- templates/skill_scaffold/SKILL.md.tmpl 3.2 KB
- templates/skill_scaffold/validator.py.tmpl 8.0 KB
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.
- yesterday First seen · 139 lines · 57 tokens per session scan A eaca684c5977
creator is a skill published in the GitHub repository thematteroftime/agentic-md-for-dummies (6 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 1,747 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other skills, from other repositories
customize
Use when the user wants to create or manage a Specialist agent or create, revise, publish, or delete a Skill through the conversational /Customize entry. Routes Skill work to the internal skill-creator and handles Specialist work through the JavaScript host.agents SDK.
scvi-tools
Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score…
openfold3
Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.
ligandmpnn
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be…
scgpt
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For…
alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…