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/compute-lifecyclenpx skills add Abilityai/cornelius --skill compute-lifecyclegit 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 | $0.00024 | $0.00432 |
| Opus 5 | $0.00012 | $0.00216 |
| Sonnet 5 | $0.00005 | $0.00086 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
compute-lifecycle 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
Compute Lifecycle Scores
Computes lifecycle scores (0.0 reflective -> 1.0 generative) for all insight and framework notes based on behavioral signals: citation frequency, generative ratio, cross-domain reach, and temporal acceleration.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Enrichments | resources/brain-graph/data/graph_enrichments.json |
✓ | ✓ | Updated lifecycle scores |
| LBS Graph | resources/local-brain-search/data/brain_graph.pkl |
✓ | NetworkX graph | |
| Brain files | Brain/**/*.md |
✓ | File mtimes for temporal signals |
Process
Step 1: Run lifecycle computation
cd $PROJECT_ROOT/resources/brain-graph
../local-brain-search/venv/bin/python cli.py lifecycle
For JSON output:
../local-brain-search/venv/bin/python cli.py lifecycle --json
Step 2: Present transitions
Focus on notes that crossed phase boundaries:
- Reflective -> Crystallizing: Note is starting to generate its own connections
- Crystallizing -> Generative: Note has become a driver of new insights
For promotable notes, suggest:
- "Consider promoting to framework status"
- "This note drives connections across N domains"
Lifecycle Phases
| Score Range | Phase | Meaning |
|---|---|---|
| 0.0 - 0.3 | Reflective | Tracks sources, sources win on conflict |
| 0.3 - 0.6 | Crystallizing | Generating own connections, authority contested |
| 0.6 - 1.0 | Generative | Drives downstream notes, this note wins on conflict |
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 · 52 lines · 24 tokens per session scan A 73a95c6ce1de
compute-lifecycle is a skill published in the GitHub repository Abilityai/cornelius (104 stars, last pushed 9d ago), licensed MIT. It adds 24 tokens to every session and 432 once invoked, about $0.0001 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.
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