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-information-disclosuregit 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-information-disclosure)<a href="https://agentmods.dev/skills/uphiago/recon-skills/hunt-information-disclosure"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-information-disclosure/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-information-disclosure"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-information-disclosure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 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 Privilege Escalation · line 71 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 71 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 71 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Data Exfiltration · line 45 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 | $0.00025 | $0.02021 |
| Opus 5 | $0.00013 | $0.01010 |
| Sonnet 5 | $0.00005 | $0.00404 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade B, and why
hunt-information-disclosure scanned grade B with 2 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 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.
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.
curl --max-time 30 --connect-timeout 10 -sk -X POST "https://target.com/api/login" -d '{"username":null}' Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
compatibility: Requires curl, python3, httpx How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Information Disclosure Hunting
Hunt for information exposure through stack traces, debug endpoints, versioned path discovery, source maps, and differential oracles. Each disclosure amplifies other vulnerabilities — a version number enables CVE targeting, a server path enables LFI, a schema leak enables auth bypass, and an error message reveals internal infrastructure.
When to Use
- Applications return verbose error messages with stack traces, file paths, or SQL fragments.
- Source maps (.js.map) are deployed to production.
- Versioned static assets reveal framework/CMS versions.
- API responses differ by object existence (user enumeration by status/length/time).
- Debug endpoints, health checks, or status pages expose internal state.
Quick Detection
# Trigger errors on common paths
for path in "/nonexistent" "/%00" "/.." "/error" "/debug"; do
curl --max-time 30 --connect-timeout 10 -sk "https://target.com$path" | grep -iE "stack|trace|exception|error|warning|debug|line [0-9]+" | head -5
done
Procedure
Phase 1 — Error & Exception Leakage
# Trigger errors with malformed input
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/api/users?id='"
curl --max-time 30 --connect-timeout 10 -sk -X POST "https://target.com/api/login" -d '{"username":null}'
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/search?q=%00"
# Check response for sensitive data
# Stack traces → file paths, line numbers, framework version
# SQL errors → table names, column names, DB type
# Deserialization errors → class names, serialization format
# Template errors → template paths, engine type
# Fuzz for debug endpoints
for path in "/debug" "/__debug__" "/debugbar" "/_debug_toolbar" "/.well-known/debug" \
"/actuator" "/actuator/info" "/actuator/env" "/actuator/health"; do
curl --max-time 30 --connect-timeout 10 -sk "https://target.com$path" -w "\n%{http_code} — $path\n" -o /dev/null
done
Phase 2 — DVCS & Config File Discovery
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 · 177 lines · 25 tokens per session scan B d1e4a4632537
hunt-information-disclosure is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 10d ago), licensed MIT. It adds 25 tokens to every session and 2,021 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, 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
performing-aws-account-enumeration-with-scout-suite
Perform comprehensive security posture assessment of AWS accounts using ScoutSuite to enumerate resources, identify misconfigurations, and generate actionable security reports.
detecting-api-enumeration-attacks
Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.
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.