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 skills add armelhbobdad/oh-my-skills --skill oms-cogneegit clone --depth 1 https://github.com/armelhbobdad/oh-my-skillsWrote 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/armelhbobdad/oh-my-skills/oms-cognee)<a href="https://agentmods.dev/skills/armelhbobdad/oh-my-skills/oms-cognee"><img src="https://agentmods.dev/badge/skills/armelhbobdad/oh-my-skills/oms-cognee/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/armelhbobdad/oh-my-skills/oms-cognee"><img src="https://agentmods.dev/badge/skills/armelhbobdad/oh-my-skills/oms-cognee.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00220 | $0.04176 |
| Opus 5 | $0.00110 | $0.02088 |
| Sonnet 5 | $0.00044 | $0.00835 |
| Haiku 4.5 | $0.00022 | $0.00418 |
Grade A, and why
oms-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 12d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **`cognee.start_ui` is sync (not async) and requires a `pid_callback` positional argument.** Do not call `await cognee.start_ui()` — the function returns `Optional[subprocess.Popen]` synchronously. Signature: `start_ui How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
oms-cognee
Overview
Cognee is an open-source knowledge-graph memory engine for AI agents. It combines a vector store (semantic search), a graph store (entities + relationships), and a relational store (provenance) into a single three-layer memory architecture. The canonical workflow is add → cognify → search: ingest data, build a knowledge graph, then query it.
- Source: topoteretes/cognee @
v0.5.8(commitb51dcce)[SRC:pyproject.toml:L4] - Language: Python >=3.10, <3.14
[SRC:pyproject.toml:L10] - Forge tier: Deep (AST + ccc + QMD + docs fetch)
- Public exports: 25 top-level names in
cognee/__init__.py[AST:cognee/__init__.py:L1] - Confidence: All T1 (AST-verified from source clone)
- Async model: Cognee is async-first — nearly all top-level functions are coroutines and must be
awaited[EXT:https://docs.cognee.ai/getting-started/quickstart]
Quick Start
import asyncio
import cognee
from cognee import SearchType
async def main():
# (optional) start from a clean slate
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
# 1) Ingest data — text, file path, URL, or list of any of those
await cognee.add(
"Cognee turns documents into AI memory.",
dataset_name="main_dataset",
)
# 2) Build the knowledge graph
await cognee.cognify(datasets="main_dataset")
# 3) Query the graph with graph-backed LLM completion (default)
results = await cognee.search(
query_text="What does Cognee do?",
query_type=SearchType.GRAPH_COMPLETION,
)
for r in results:
print(r)
if __name__ == "__main__":
asyncio.run(main())
Signatures: [AST:cognee/api/v1/add/add.py:L21] · [AST:cognee/api/v1/cognify/cognify.py:L44] · [AST:cognee/api/v1/search/search.py:L27]
Before running, set LLM_API_KEY for graph extraction and completion; Cognee defaults to OpenAI but supports litellm-compatible providers (Anthropic, Gemini, Ollama, etc.) via cognee.config.set_llm_provider(...) and friends. [AST:cognee/api/v1/config/config.py:L141] · [SRC:cognee/api/v1/add/add.py:L166]
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 194 lines · 220 tokens per session scan A fcfbbf8239a7
oms-cognee is a skill published in the GitHub repository armelhbobdad/oh-my-skills (7 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 220 tokens to every session and 4,176 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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Package for distribution and inject context into CLAUDE.md/AGENTS.md/.cursorrules. Use when the user requests to "export" or "package a skill.".
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