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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/uphiago/recon-skillsnpx agentmods add skills/uphiago/recon-skills/recon-sectorWrote 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/recon-sector)<a href="https://agentmods.dev/skills/uphiago/recon-skills/recon-sector"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/recon-sector/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/recon-sector"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/recon-sector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Data Exfiltration · line 108 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 | $0.00011 | $0.02033 |
| Opus 5 | $0.00005 | $0.01017 |
| Sonnet 5 | $0.00002 | $0.00407 |
| Haiku 4.5 | $0.00001 | $0.00203 |
Grade A, and why
recon-sector scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
compatibility: Requires curl, python3 How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RECON-SECTOR — Parameterized Sector Reconnaissance
Unified sector-specific reconnaissance. Takes a sector name (e.g., plumbing, dentists, hvac), loads sector-specific platform and path data from references/sectors.yaml, and runs the standard recon probe suite: WordPress detection, CORS credential reflection, XMLRPC exposure, debug log mining, source leak checks, and directory listing detection.
Replaces 25 individual recon-* skills that were identical template copies with only sector name and platform names changed.
When to Use
- Starting recon on a target in a known SMB sector.
- After
sector-recon-methodologyproduces a target list and you need to probe. - When you want sector-aware path lists (financing pages, booking portals, etc.) tailored to the target's industry.
Prerequisites
references/sectors.yamlin the same directory as this SKILL.md.- Target domain list file (one domain per line).
How to Run
SECTOR="plumbing"
TARGETS_FILE="targets.txt"
python3 references/probe_sector.py "$SECTOR" "$TARGETS_FILE" output/
Quick Reference
| Check | Paths | Severity if exposed |
|---|---|---|
| WP detection | /wp-login.php, /wp-content/ |
Info |
| REST API users | /wp-json/wp/v2/users |
Medium (user enum) |
| CORS + REST API | /wp-json/wp/v2/users with Origin: https://evil.com |
High (if ACAC: true) |
| XMLRPC | /xmlrpc.php |
Medium (open), High (multicall) |
| Debug log | /wp-content/debug.log |
High (PII/SQL leakage) |
| Directory listing | /wp-content/uploads/ |
Medium-High (file exposure) |
| Source leaks | /.env, /.git/config, /info.php |
Critical (creds in env) |
| Sector-specific paths | From sectors.yaml per sector |
Varies |
Procedure
Step 1 — Load sector data
SECTOR="$1"
TARGETS_FILE="$2"
OUTDIR="${3:-output}"
python3 -c "
import yaml, sys
with open('references/sectors.yaml') as f:
data = yaml.safe_load(f)
sector = data['sectors'].get(sys.argv[1], {})
print('\n'.join(sector.get('high_value_paths', [])))
" "$SECTOR"
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.
- 6d ago First seen · 179 lines · 11 tokens per session scan A 4f4774d4aadc
recon-sector is a skill published in the GitHub repository uphiago/recon-skills (1,251 stars, last pushed 8d ago), licensed MIT. It adds 11 tokens to every session and 2,033 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 2026-09-03.
Other skills, from other repositories
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.
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-misconfigured-azure-storage
Detecting misconfigured Azure Storage accounts including publicly accessible blob containers, missing encryption settings, overly permissive SAS tokens, disabled logging, and network access violations using Azure CLI, PowerShell, and Microsoft Defender for Storage.
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.