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/mitodl/agent-kit/creating-skillsnpx skills add mitodl/agent-kit --skill creating-skillsgit clone --depth 1 https://github.com/mitodl/agent-kitWrote 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/mitodl/agent-kit/creating-skills)<a href="https://agentmods.dev/skills/mitodl/agent-kit/creating-skills"><img src="https://agentmods.dev/badge/skills/mitodl/agent-kit/creating-skills.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00052 | $0.00788 |
| Opus 5 | $0.00026 | $0.00394 |
| Sonnet 5 | $0.00010 | $0.00158 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
creating-skills 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating a New Skill
Skills follow the Agent Skills open standard.
1. Choose a category
Place the skill under the most specific matching category in skills/:
| Category | When to use |
|---|---|
python/ |
Python tooling, packaging, dependency management |
dagster/ |
Dagster pipelines, code locations, dg usage |
infrastructure/ |
Pulumi IaC, secrets, Vault, Kubernetes config |
containers/ |
Docker builds, image conventions |
workflow/ |
Cross-cutting process conventions; meta-skills |
If no category fits, create a new one and add a README.md for it.
2. Create the skill directory
The directory name becomes the skill's name. Use lowercase letters and hyphens only.
skills/<category>/<skill-name>/
└── SKILL.md
The name in SKILL.md frontmatter must exactly match the directory name.
3. Write SKILL.md
---
name: <skill-name> # must match directory name; max 64 chars
description: > # what it does AND when to use it; max 1024 chars
<one or two sentences describing the skill and the keywords/scenarios
that should trigger it>
license: BSD-3-Clause
metadata:
category: <category>
---
# Skill Title
...instructions...
Description guidelines (most important field)
The description is what the agent uses to decide whether to activate the skill. Make it trigger-friendly:
- State both what the skill covers and when to use it.
- Include the specific tool names, command names, or scenario keywords an agent would encounter when the skill is relevant.
- Bad: "Conventions for X." — too vague, won't trigger reliably.
- Good: "Apply X conventions. Use this skill when doing Y or Z — covers A, B, C."
4. Apply progressive disclosure
Keep SKILL.md under ~500 lines. If the skill has deep reference material
(e.g., a detailed API reference, form templates, domain-specific lookup tables),
move it to a references/ subdirectory and link to it from SKILL.md:
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.
- 3d ago First seen · 108 lines · 52 tokens per session scan A b107d3036011
creating-skills is a skill published in the GitHub repository mitodl/agent-kit (2 stars, last pushed 5d ago), licensed BSD-3-Clause. It adds 52 tokens to every session and 788 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…