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 MingyiSecLab/Mingyi-Atlas --skill bloodhound-querygit clone --depth 1 https://github.com/MingyiSecLab/Mingyi-AtlasWrote 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/mingyiseclab/mingyi-atlas/bloodhound-query)<a href="https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/bloodhound-query"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/bloodhound-query/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/mingyiseclab/mingyi-atlas/bloodhound-query"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/bloodhound-query.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 | $0.00032 | $0.01073 |
| Opus 5 | $0.00016 | $0.00536 |
| Sonnet 5 | $0.00006 | $0.00215 |
| Haiku 4.5 | $0.00003 | $0.00107 |
Grade A, and why
bloodhound-query 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 11d 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.
This is a copy
94% identical to bloodhound-query — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BloodHound Query Playbook
1. Collect
# Python collector (works from Linux attacker box)
bloodhound-python -u USER -p 'PASS' -d DOMAIN -c all --zip --dns-tcp \
-ns DC_IP -o /workspace/bh.zip
# Or SharpHound from a Windows beachhead
# Invoke-BloodHound -CollectionMethod All -ZipFileName bh.zip
If bloodhound-python errors on TLS, add -gc gc.domain.local for the
global catalog FQDN.
2. Ingest into Atlas KG
bh_ingest_zip("/workspace/bh.zip")
This populates User / Computer / Group / GPO / OU nodes with attribute properties (hasspn, dontreqpreauth, enabled, admincount, sidhistory).
3. Canonical Cypher queries
Run via bh_cypher("<query>") or post-process kg_query(kind=...):
| Goal | Cypher |
|---|---|
| Owned principals | MATCH (u) WHERE u.owned=true RETURN u.name |
| Shortest path to DA | MATCH p=shortestPath((u {owned:true})-[*1..]->(g:Group {name:'DOMAIN ADMINS@DOM'})) RETURN p |
| Kerberoastable users | MATCH (u:User {hasspn:true, enabled:true}) RETURN u.name,u.spns |
| AS-REP roastable | MATCH (u:User {dontreqpreauth:true, enabled:true}) RETURN u.name |
| DCSync candidates | `MATCH (n)-[:GetChanges |
| Unconstrained delegation | MATCH (c:Computer {unconstraineddelegation:true}) RETURN c.name |
| RBCD targets | MATCH (n)-[:AddAllowedToAct]->(c:Computer) RETURN n.name,c.name |
| GenericAll on user | MATCH (n)-[:GenericAll]->(u:User) WHERE NOT n=u RETURN n.name,u.name |
| ACL path to high-value | `MATCH p=shortestPath((u {owned:true})-[:GenericAll |
| Sessions on DC | MATCH (u:User)-[:HasSession]->(c:Computer) WHERE c.name CONTAINS 'DC' RETURN u.name,c.name |
| Computers w/ admin from owned | MATCH (u {owned:true})-[:AdminTo*1..2]->(c:Computer) RETURN c.name |
| GPO abuse | MATCH (n)-[:GpLink]->(:OU)-[:Contains*1..]->(c:Computer) WHERE n.name CONTAINS 'unsafe' RETURN n.name,c.name |
4. Auto-prioritize attack paths
After ingest:
plan_attack_chains(promote=True)
This walks the graph from owned → high-value and surfaces:
- Tier-0 reachability (DA / EA / krbtgt)
- Tier-1 reachability (server admins, backup ops)
- Lateral hops (admin → admin via AdminTo)
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.
- 11d ago First seen · 83 lines · 32 tokens per session scan A b58b1ab6a670
bloodhound-query is a skill published in the GitHub repository MingyiSecLab/Mingyi-Atlas (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,073 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to bloodhound-query, differing in 6 lines, and is treated as a copy.
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