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/dagster-code-location-structurenpx skills add mitodl/agent-kit --skill dagster-code-location-structuregit clone --depth 1 https://github.com/mitodl/agent-kitWhat 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 | $0.00450 |
| Opus 5 | $0.00026 | $0.00225 |
| Sonnet 5 | $0.00010 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
dagster-code-location-structure 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 2d 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
Dagster Code Location Structure
Project layout
Code locations live under dg_projects/ as separate subdirectories, each managed
by dg. Shared code (base classes, utilities, sensors used by multiple locations)
lives in packages/ol-orchestrate-lib/.
dg_projects/
<code_location_name>/
pyproject.toml
src/
<code_location_name>/
assets/
sensors/
...
packages/
ol-orchestrate-lib/
...
Scaffolding new code locations
Use the create-dagster tool to scaffold new code locations — not dg scaffold:
create-dagster <code_location_name>
Asset and sensor placement
Assets and sensors must be placed in the code location that owns them. Do not
accidentally include an asset or sensor from one code location in another.
When migrating or moving definitions, double-check defs.py / __init__.py
imports in each code location.
Migration sequencing
When migrating multiple code locations (e.g., from EC2/docker-compose to Kubernetes), do one code location at a time. The first location becomes the validated template. Never try to migrate all locations in a single PR.
Shared OAuth / base classes
The oauth module inside ol-orchestrate-lib is used as a base class by
multiple code locations. It must remain in the library even if it looks unused
from a single code location's perspective.
Checking partitions
Static partitions with hardcoded values are the old approach. Prefer dynamic/time-based partitions when an up-to-date implementation already exists in another code location — copy that pattern rather than the older one.
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.
- 2d ago First seen · 67 lines · 52 tokens per session scan A 214caf65be3f
dagster-code-location-structure is a skill published in the GitHub repository mitodl/agent-kit (2 stars, last pushed 3d ago), licensed BSD-3-Clause. It adds 52 tokens to every session and 450 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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.