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/coco-research/coco/cogneenpx skills add coco-research/coco --skill cogneegit clone --depth 1 https://github.com/coco-research/cocoWrote 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/coco-research/coco/cognee)<a href="https://agentmods.dev/skills/coco-research/coco/cognee"><img src="https://agentmods.dev/badge/skills/coco-research/coco/cognee.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 5.1 | $0.00055 | $0.01445 |
| Opus 5 | $0.00028 | $0.00723 |
| Sonnet 5 | $0.00011 | $0.00289 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
cognee scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "$COGNEE/health" | jq . The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 165 lines · 55 tokens per session scan A e5ebff8cbdda
cognee is a skill published in the GitHub repository coco-research/coco (218 stars, last pushed yesterday), with no licence file. It adds 55 tokens to every session and 1,445 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
atlas-setup
Use when a repository needs Atlas setup, repair/update, AGENTS.md refresh, AI memory refresh, vocabulary cleanup, or review after major codebase changes.
continual-learning
Orchestrate continual learning by delegating transcript mining to agents-memory-updater and writing durable memory to local JSON (never root AGENTS.md Learned sections). USE WHEN continual learning, mine prior chats, maintain agent memory, or the stop hook triggers this skill.
atlas-compact
Use when AI context needs cleanup — atlas doctor context-size advisories, an oversized AGENTS.md or CLAUDE.md, bloated memory or vocabulary files, or durable docs that need compaction, relocation, or dedup.
maintain-project-ai-records
Initialize and maintain lightweight AI work records in a writable project. Use on the first substantive writable task and after meaningful milestones. Do not create records for read-only inspection or trivial answers.
ackit-context-optimization
Build budgeted context packs and trim instruction bloat for coding agents.
project-memory
Initialize, retrieve, and explicitly update the separate Rootloom Memory plugin's repository-owned architecture, risk, decision, and failure lessons. Use only when explicitly requested. Memory is experimental, advisory, and outside Rootloom Core.