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 wp-mass-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/wp-mass-recon)<a href="https://agentmods.dev/skills/uphiago/recon-skills/wp-mass-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/wp-mass-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/wp-mass-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/wp-mass-recon.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 | $0.00022 | $0.03694 |
| Opus 5 | $0.00011 | $0.01847 |
| Sonnet 5 | $0.00004 | $0.00739 |
| Haiku 4.5 | $0.00002 | $0.00369 |
Grade D, and why
wp-mass-recon scanned grade D with 4 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
| XMLRPC | `curl --max-time 30 --connect-timeout 10 -sk -X POST "https://TARGET/xmlrpc.php" -d '<methodCall><methodName>demo.sayHello</methodName></methodCall>'` | `Hello!` in body | Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
users=$(curl -sk --max-time 10 --connect-timeout 10 "$url/wp-json/wp/v2/users" | python3 -c "import sys,json; d=json.load(sys.stdin); print(len(d) if isinstance(d,list) else 0)" 2>/dev/null) 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 Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
The production-proven approach uses `concurrent.futures.ThreadPoolExecutor` with 20 workers. Each worker calls curl via `subprocess.run`. This is 10x faster than bash `while` loops. How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WP Mass Recon Skill
Batch WordPress vulnerability detection pipeline for scanning dozens to hundreds of domains in parallel. Detects WordPress presence, REST API user enumeration, CORS credential reflection, XMLRPC exposure, open registration, and sensitive file leaks in a single pass. Proven on 600+ US company domains across 28 sectors.
When to Use
- You have an authorized target list from a bug bounty program, pentest engagement, or red team with signed RoE.
- Sector-wide recon within authorized scope.
- After
subfinder/crt.shproduces a target list and you need to triage. - You want maximum findings per minute with a parallelizable pipeline.
Prerequisites
curl,httpx,python3, andjq.- A target list in
domain|company|sectorformat, one target per line. - A writable
OUTPUT_DIR; examples default to./output. - The bundled scanner or the inline commands below.
How to Run
# Phase 1: Live host discovery + tech detection
httpx -silent -l targets.txt -threads 50 -tech-detect -status-code -title -o $OUTDIR/alive.txt
# Phase 2: WP detection, user enum, CORS, XMLRPC (20 workers)
python3 parallel_batch.py "${OUTPUT_DIR:-./output}/targets.txt" 20
Or run the 4-phase pipeline manually using the commands in Procedure.
Quick Reference
| Check | Command | Positive Signal |
|---|---|---|
| WP detection | curl -skI "https://TARGET/wp-login.php" |
HTTP 200/301/302 |
| User enum | curl --max-time 30 --connect-timeout 10 -sk "https://TARGET/wp-json/wp/v2/users" |
JSON with id, name, slug |
| CORS | curl -skI "https://TARGET/wp-json/wp/v2/users" -H "Origin: https://evil.com" |
Access-Control-Allow-Credentials: true |
| XMLRPC | curl --max-time 30 --connect-timeout 10 -sk -X POST "https://TARGET/xmlrpc.php" -d '<methodCall><methodName>demo.sayHello</methodName></methodCall>' |
Hello! in body |
| Open reg | curl --max-time 30 --connect-timeout 10 -sk "https://TARGET/wp-login.php?action=register" |
Form with user_login field |
| Source leaks | Parallel curl for .env, wp-config.php.bak, .git/config, debug.log, backup.sql |
Real content (not SPA catch-all) |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 270 lines · 22 tokens per session scan D 3e7099b62bf0
wp-mass-recon is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 9d ago), licensed MIT. It adds 22 tokens to every session and 3,694 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it D with 4 findings (sends data to an external url, downloads and executes remote code, 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
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.