Recon Skills is a pack of security-testing skills covering reconnaissance, web applications, APIs, authentication, vulnerability validation, cloud infrastructure, and reporting. Security professionals use it for authorized assessments of systems they own or have written permission to test. The catalogue entries are individual skills from the pack.
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 uphiago/recon-skills --skill hunt-ntlm-infogit clone --depth 1 https://github.com/uphiago/recon-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/uphiago/recon-skills/hunt-ntlm-info)<a href="https://agentmods.dev/skills/uphiago/recon-skills/hunt-ntlm-info"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-ntlm-info/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/uphiago/recon-skills/hunt-ntlm-info"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-ntlm-info.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.04592 |
| Opus 5 | $0.00046 | $0.02296 |
| Sonnet 5 | $0.00019 | $0.00918 |
| Haiku 4.5 | $0.00009 | $0.00459 |
Grade A, and why
hunt-ntlm-info 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 8d 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.
3. **Use a keep-alive raw socket, not Python requests / curl one-shot.** Most HTTP libraries close the connection between the Type-1 send and Type-2 reception. Use one of: How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Crown Jewel Targets
NTLM info disclosure is a Medium-severity finding when chained to context — the leak itself is intentional protocol behavior (RFC-compliant NTLMSSP challenge), but on internet-exposed enterprise infrastructure it provides exact reconnaissance for the next stage of an attack. Highest-value targets:
- Internet-reachable IIS / SharePoint / Exchange / OWA with dual-auth (Forms + NTLM, or NTLM + Kerberos)
- Citrix NetScaler / VMware Horizon View internet-facing gateways with NTLM-backed AD auth
- Lync / Skype for Business / Teams On-Prem edge servers
- WSUS / Windows Update Services with NTLM-protected admin paths
- CIFS-style fileshare proxies (HCL Sametime, IBM Notes Domino) that proxy NTLM
- Legacy SharePoint farms that left NTLM enabled on the public-zone IIS binding
What makes this pay:
- Internal AD domain disclosure (parent-forest mapping, e.g.
customer.parent-corp.example→ tenant inside corporate-AD tree) - Default-Windows-hostname disclosure (
WIN-XXXXXXXXXXXpattern signals rushed provisioning → likely default service-account passwords) - Timestamp leak (used in NTLMv2 hash cracking acceleration)
- Direct attack-map enrichment for credential spraying combined with
hunt-auth-bypassLegacy-Protocol Matrix
Attack Surface Signals
Response headers signaling NTLM availability:
WWW-Authenticate: NTLM
WWW-Authenticate: Negotiate
WWW-Authenticate: NTLM, Negotiate
WWW-Authenticate: Negotiate, NTLM
URL patterns where NTLM is commonly exposed:
/_api/web/CurrentUser (SharePoint REST)
/_vti_bin/*.asmx (SharePoint legacy SOAP)
/EWS/Exchange.asmx (Exchange Web Services)
/Autodiscover/Autodiscover.xml (Exchange autodiscover)
/owa/ (Outlook Web App)
/Microsoft-Server-ActiveSync (ActiveSync)
/PowerShell (Exchange Mgmt Shell over HTTPS)
/api/v3/ (TeamCity, Atlassian)
/wsus/ (Windows Server Update Services)
/manager/html (some Tomcat behind IIS)
/iisstart.htm (default IIS, sometimes reveals NTLM upstream)
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.
- 8d ago First seen · 307 lines · 93 tokens per session scan A 6b3b7bb58419
hunt-ntlm-info is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 10d ago), licensed MIT. It adds 93 tokens to every session and 4,592 once invoked, about $0.0005 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
auditing-gcp-iam-permissions
Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.
detecting-compromised-cloud-credentials
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
implementing-aws-config-rules-for-compliance
Implementing AWS Config rules for continuous compliance monitoring of AWS resources, deploying managed and custom rules aligned to CIS and PCI DSS frameworks, configuring automatic remediation with SSM Automation, and aggregating compliance data across accounts.
implementing-cloud-dlp-for-data-protection
Implementing Cloud Data Loss Prevention (DLP) using Amazon Macie, Azure Information Protection, and Google Cloud DLP API to discover, classify, and protect sensitive data across cloud storage, databases, and data pipelines.
implementing-cloud-trail-log-analysis
Implementing AWS CloudTrail log analysis for security monitoring, threat detection, and forensic investigation using Athena, CloudWatch Logs Insights, and SIEM integration to identify unauthorized access, privilege escalation, and suspicious API activity.
implementing-zero-trust-network-access
Implementing Zero Trust Network Access (ZTNA) in cloud environments by configuring identity-aware proxies, micro-segmentation, continuous verification with conditional access policies, and replacing traditional VPN-based access with BeyondCorp-style architectures across AWS, Azure, and GCP.