Borrowing it
Nothing to install: this file belongs to donbr/lifesciences-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/donbr/lifesciences-research/main/.claude/commands/graphiti-docker-stats.mdgit clone --depth 1 https://github.com/donbr/lifesciences-researchWrote 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/commands/donbr/lifesciences-research/graphiti-docker-stats)<a href="https://agentmods.dev/commands/donbr/lifesciences-research/graphiti-docker-stats"><img src="https://agentmods.dev/badge/commands/donbr/lifesciences-research/graphiti-docker-stats/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/commands/donbr/lifesciences-research/graphiti-docker-stats"><img src="https://agentmods.dev/badge/commands/donbr/lifesciences-research/graphiti-docker-stats.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 | $0.00011 | $0.01698 |
| Opus 5 | $0.00005 | $0.00849 |
| Sonnet 5 | $0.00002 | $0.00340 |
| Haiku 4.5 | $0.00001 | $0.00170 |
Grade A, and why
graphiti-docker-stats 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 10d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate development environment statistics for Docker Neo4j namespace management and cleanup.
Instructions
Execute these queries sequentially to build a development environment report. Report results to the user in a formatted summary.
Purpose: Namespace cleanup, development progress tracking, and quick sanity checks.
Query 1: Namespace Overview
mcp__neo4j-docker-cypher__read_neo4j_cypher(
query="""
MATCH (e:Episodic)
RETURN e.group_id AS namespace,
count(*) AS episodes,
min(e.created_at) AS first_episode,
max(e.created_at) AS last_episode
ORDER BY episodes DESC
"""
)
Purpose: Shows all namespaces with episode counts and identifies cleanup candidates.
Query 2: Development Namespace Breakdown
mcp__neo4j-docker-cypher__read_neo4j_cypher(
query="""
MATCH (e:Episodic)
WHERE e.group_id STARTS WITH 'dev_'
OR e.group_id STARTS WITH 'test_'
OR e.group_id STARTS WITH 'experimental_'
OR e.group_id STARTS WITH 'scratch_'
OR e.group_id STARTS WITH 'demo_'
RETURN e.group_id AS namespace,
count(*) AS episodes,
max(e.created_at) AS last_activity
ORDER BY last_activity DESC
"""
)
Purpose: Identifies development namespaces with activity timestamps for cleanup decisions.
Query 3: Total Graph Size
mcp__neo4j-docker-cypher__read_neo4j_cypher(
query="""
MATCH (n:Entity)
WITH count(n) AS entity_count
MATCH ()-[r]->()
RETURN entity_count,
count(r) AS relationship_count
"""
)
Purpose: Simple counts for development environment capacity check.
Query 4: Recent Activity (Last 7 Days)
mcp__neo4j-docker-cypher__read_neo4j_cypher(
query="""
MATCH (e:Episodic)
WHERE e.created_at > datetime() - duration('P7D')
RETURN e.group_id AS namespace,
count(*) AS recent_episodes,
max(e.created_at) AS last_episode
ORDER BY recent_episodes DESC
"""
)
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.
- 10d ago First seen · 228 lines · 11 tokens per session scan A 86a66fc26cff
graphiti-docker-stats is a command published in the GitHub repository donbr/lifesciences-research (7 stars, last pushed 7d ago), licensed MIT. It adds 11 tokens to every session and 1,698 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 2026-08-31.
Other commands, from other repositories
by:plan-campaign
Pre-campaign discussion -- capture design preferences before launching.
by:resume
Resume an interrupted campaign from the last checkpoint.
by:campaign-auto
Full autonomous campaign — research, design, screen, rank with minimal interruption.
by:load
Load a protein target and analyze it for design.
by:approve-lab
Approve lab submission to Adaptyv Bio (triple-gated).
by:set-profile
Switch model profile (quality/balanced/budget).