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/abilityai/cornelius/incubation-loopnpx skills add Abilityai/cornelius --skill incubation-loopgit clone --depth 1 https://github.com/Abilityai/corneliusWhat 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 | $0.00042 | $0.09254 |
| Opus 5 | $0.00021 | $0.04627 |
| Sonnet 5 | $0.00008 | $0.01851 |
| Haiku 4.5 | $0.00004 | $0.00925 |
Grade A, and why
incubation-loop 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 — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incubation Loop
ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change — the top entry of
metadata.changelogabove — e.g.incubation-loop vX.Y — recent: <summary>. Then proceed.
Autonomous thinking engine. Each scheduled run advances all active thinking topics by one analytical move, persisting reasoning state across runs until convergence.
Purpose
Continuous intellectual iteration on open questions. Uses a rotating set of research-validated analytical moves (ACH, Bayesian updating, dialectical steelmanning, cross-domain bridging) to build toward well-grounded conclusions - without requiring human presence in each cycle.
Design principle: Each run does one move per topic. Depth accumulates across runs. No single run tries to "solve" the question.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Thinking Registry | Brain/05-Meta/Thinking/THINKING-REGISTRY.md |
✓ | ✓ | Active topics + status |
| Thinking Files | Brain/05-Meta/Thinking/[topic-slug].md |
✓ | ✓ | Per-topic reasoning journal |
| Local Brain Search | resources/local-brain-search/ |
✓ | Semantic search for KB evidence | |
| Permanent Notes | Brain/02-Permanent/ |
✓ | Primary evidence source | |
| Session Changelogs | Brain/05-Meta/Changelogs/ |
✓ | Run log |
Prerequisites
- Registry file exists at
Brain/05-Meta/Thinking/THINKING-REGISTRY.md - At least one active topic seeded (see Seeding section below) - or none, in which case the Starvation Floor (Step 1a) self-seeds one from the watched domains
- Local Brain Search index up-to-date
Composes
/domain-watch- invoked by the Starvation Floor (Step 1a) when the active queue is empty, to formulate and activate a new topic from the domains under surveillance. Called by its unversioned name so its fixes propagate.
Process
Step 1: Get Date and Load Registry
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 · 466 lines · 42 tokens per session scan A 415f26e4a592
incubation-loop is a skill published in the GitHub repository Abilityai/cornelius (104 stars, last pushed 10d ago), licensed MIT. It adds 42 tokens to every session and 9,254 once invoked, about $0.0002 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 2026-08-30.
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…