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 password-spray-methodologygit 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/password-spray-methodology)<a href="https://agentmods.dev/skills/uphiago/recon-skills/password-spray-methodology"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/password-spray-methodology/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/password-spray-methodology"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/password-spray-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 390 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- medium Data Exfiltration · line 104 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00076 | $0.05692 |
| Opus 5 | $0.00038 | $0.02846 |
| Sonnet 5 | $0.00015 | $0.01138 |
| Haiku 4.5 | $0.00008 | $0.00569 |
Grade A, and why
password-spray-methodology scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
code=$(curl --max-time 30 --connect-timeout 10 -sk -u "user${n}@target.com:WrongP@ss" -o /dev/null -w "%{http_code}" \ How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Password Spray Methodology
Password spraying is the highest-ROI credential attack: one (or few) passwords tried against many users. It's quiet, avoids lockouts, and succeeds where traditional brute force fails. This skill consolidates every spray vector, enumeration technique, pattern, and protocol across the entire skill catalog.
This is a methodology skill. Protocol-specific details live in their respective skills — cross-reference them for deep dives. This skill covers the universal spray pipeline that applies everywhere.
When to Use
- Starting ANY engagement with a user list but no credentials
- Finding internet-facing login portals (OWA, Okta, ADFS, VPN, SSO, OIDC)
- After
hunt-ntlm-inforeveals AD domain/UPN format — feed into spray - After
hunt-ldapenumerates sAMAccountNames — feed into spray - After
js-secrets-extractionfinds email patterns — feed into spray - OneDrive 302/404 enum (from
m365-entra-attack) confirms licensed users — spray them - Any leaked credential dump from client — validate against all services
- Active-attacker detection via Smart Lockout differential
Universal Spray Pipeline (5 Phases)
Phase 1: ENUMERATE USERS → valid user list
Phase 2: DETECT LOCKOUT → max attempts/min, throttle rate
Phase 3: GENERATE PASSWORDS → 5-20 high-probability candidates
Phase 4: EXECUTE SPRAY → per-protocol, low-and-slow
Phase 5: INTERPRET RESULTS → error code differential → finding or discard
Phase 1 — User Enumeration
The spray is only as good as the user list. Gather usernames BEFORE touching any auth endpoint.
Universal Enumeration Sources
| Source | Technique | Lockout Risk | Skill Reference |
|---|---|---|---|
| LinkedIn / company page | Scrape employee names → username-anarchy |
Zero | — |
| OneDrive personal site | GET /personal/<user>_<domain>_com/_layouts/15/onedrive.aspx → 302=exists, 404=no |
Zero | m365-entra-attack |
| Kerberos pre-auth | kerbrute userenum --dc <DC> --domain <DOMAIN> users.txt |
Zero (KDC_ERR_PREAUTH_REQUIRED does not count as auth failure) | — |
| NTLM Type-2 decode | Extract AD domain, NetBIOS name, computer name from anonymous probe | Zero | hunt-ntlm-info |
| Jira user picker | /rest/api/2/user/picker?query= — public on misconfigured instances |
Zero | — |
| Jenkins | /asynchPeople/ or /securityRealm/user/<name>/ |
Zero | — |
| ManageEngine ADManager Plus | Unauthenticated user listing on exposed instances | Zero | — |
| WordPress REST API | /wp-json/wp/v2/users — dumps authors with usernames |
Zero | hunt-wordpress |
| GitLab | /api/v4/users — public on open instances |
Zero | — |
| O365 Autodiscover | HARDENED — returns identical 200 for all (2024+) | Zero but dead | m365-entra-attack |
| GetUserRealm | HARDENED — returns same XML for any email in tenant | Zero but dead | m365-entra-attack |
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.
- 7d ago First seen · 428 lines · 76 tokens per session scan A 4eed6a0af7a1
password-spray-methodology is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 9d ago), licensed MIT. It adds 76 tokens to every session and 5,692 once invoked, about $0.0004 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
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detecting-compromised-cloud-credentials
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detecting-misconfigured-azure-storage
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detecting-s3-data-exfiltration-attempts
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