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 iot-camera-recongit 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/iot-camera-recon)<a href="https://agentmods.dev/skills/uphiago/recon-skills/iot-camera-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/iot-camera-recon/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/iot-camera-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/iot-camera-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- medium YARA Match · line 43 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 107 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 | $0.00022 | $0.02951 |
| Opus 5 | $0.00011 | $0.01476 |
| Sonnet 5 | $0.00004 | $0.00590 |
| Haiku 4.5 | $0.00002 | $0.00295 |
Grade A, and why
iot-camera-recon 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
compatibility: Requires curl, nmap, python3, masscan, subfinder, httpx, nuclei How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IoT Camera Recon Skill
IP camera assessment covering RTSP exposure, vendor configuration endpoints, ONVIF service enumeration, authentication controls, and firmware identification.
When to Use
port-mass-scanfinds RTSP (554) or camera HTTP ports (80, 8010, 8011).- Target is a physical security company, traffic management, or government surveillance.
- Shodan search reveals camera devices in the target's IP range.
- After
port-service-discoveryfinds Axis/Hikvision/Dahua ONVIF services.
Prerequisites
terminalwith curl, python3.- For mass scanning: masscan or RustScan (see
port-mass-scan). - VLC or ffmpeg for stream verification (optional).
How to Run
# Quick camera detection on known IP
curl -sk --max-time 5 --connect-timeout 5 "http://IP:8010/axis-cgi/jpg/image.cgi" -o snapshot.jpg
curl -sk --max-time 5 --connect-timeout 5 "http://IP:8010/axis-cgi/admin/param.cgi?action=list" | head -50
# Mass RTSP discovery on a /24
masscan -p554,80,8010,8011 --rate=10000 192.168.0.0/24 -oJ cameras.json
Quick Reference
| Camera Brand | Default HTTP Port | Snapshot URL | Config URL | Default Creds |
|---|---|---|---|---|
| Axis | 80, 8010 | /axis-cgi/jpg/image.cgi |
/axis-cgi/admin/param.cgi?action=list |
root:pass, root:admin |
| Hikvision | 80, 554 | /ISAPI/Streaming/channels/101/picture |
/System/configurationFile?auth=... |
admin:12345, admin:admin |
| Dahua | 80, 554 | /cgi-bin/snapshot.cgi |
/cgi-bin/configManager.cgi?action=getConfig |
admin:admin, admin:password |
| Intelbras | 80 | /cgi-bin/snapshot.cgi |
/web/cgi-bin/hi3510/param.cgi |
admin:admin, admin:123456 |
| ONVIF | 80, 8899 | N/A (SOAP) | /onvif/device_service |
admin:admin |
Procedure
Phase 1 — Mass Camera Discovery
RANGE="$1" # e.g., [REDACTED_IP]/16
OUTDIR="$OUTDIR/cameras"
mkdir -p "$OUTDIR"
echo "[*] Camera hunt on $RANGE"
# Masscan for RTSP + camera HTTP ports
masscan -p554,80,8010,8011,8899 --rate=50000 "$RANGE" -oJ "$OUTDIR/masscan_cameras.json"
# Extract IPs with open camera ports
HITS=$(python3 -c "
import json
with open('$OUTDIR/masscan_cameras.json') as f:
ips = set()
for line in f:
try:
data = json.loads(line.strip()) if line.strip() else {}
ips.add(data.get('ip', ''))
except: pass
for ip in sorted(ips):
print(ip)
" 2>/dev/null)
echo "[+] $(echo "$HITS" | wc -l) IPs with camera ports"
# Probe each with curl
echo "$HITS" | while read ip; do
echo "--- $ip ---"
# Axis snapshot
code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 3 --connect-timeout 3 "http://$ip:8010/axis-cgi/jpg/image.cgi")
[[ "$code" == "200" ]] && echo " [AXIS] Snapshot: http://$ip:8010/axis-cgi/jpg/image.cgi"
# Axis config dump
config=$(curl -sk --max-time 5 --connect-timeout 5 "http://$ip:8010/axis-cgi/admin/param.cgi?action=list" 2>/dev/null)
if [[ -n "$config" ]] && echo "$config" | grep -q "root.Brand"; then
BRAND=$(echo "$config" | grep "root.Brand.Brand=" | cut -d= -f2 | tr -d '"')
MODEL=$(echo "$config" | grep "root.Brand.ProdShortName=" | cut -d= -f2 | tr -d '"')
FIRMWARE=$(echo "$config" | grep "root.Properties.Firmware.Version=" | cut -d= -f2 | tr -d '"')
SERIAL=$(echo "$config" | grep "root.Properties.System.SerialNumber=" | cut -d= -f2 | tr -d '"')
echo " [CONFIG] $BRAND $MODEL — Firmware: $FIRMWARE — Serial: $SERIAL"
echo "$config" | wc -l | xargs echo " Parameters:"
fi
# Generic RTSP
for port in 554 8554; do
code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 3 --connect-timeout 3 "http://$ip:$port/")
[[ "$code" != "000" ]] && echo " [RTSP] Port $port responds (HTTP $code)"
done
# ONVIF discovery (port 8899 or 80)
for port in 8899 80; do
resp=$(curl -sk --max-time 5 --connect-timeout 5 -X POST "http://$ip:$port/onvif/device_service" \
-H "Content-Type: application/soap+xml" \
-d '<s:Envelope xmlns:s="http://www.w3.org/2003/05/soap-envelope"><s:Body><GetDeviceInformation xmlns="http://www.onvif.org/ver10/device/wsdl"/></s:Body></s:Envelope>' 2>/dev/null)
if echo "$resp" | grep -qi "manufacturer\|model\|serial"; then
echo " [ONVIF] Device info available on port $port"
fi
done
done
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 · 256 lines · 22 tokens per session scan A 8c1230e7ea43
iot-camera-recon is a skill published in the GitHub repository uphiago/recon-skills (1,251 stars, last pushed 8d ago), licensed MIT. It adds 22 tokens to every session and 2,951 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
camsnap
Capture frames or clips from RTSP/ONVIF cameras.
amazon-alexa
Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
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.
auditing-terraform-infrastructure-for-security
Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.
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.
detecting-s3-data-exfiltration-attempts
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.