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 redteam-report-templategit 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/redteam-report-template)<a href="https://agentmods.dev/skills/uphiago/recon-skills/redteam-report-template"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/redteam-report-template/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/redteam-report-template"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/redteam-report-template.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00139 | $0.03864 |
| Opus 5 | $0.00069 | $0.01932 |
| Sonnet 5 | $0.00028 | $0.00773 |
| Haiku 4.5 | $0.00014 | $0.00386 |
Grade A, and why
redteam-report-template 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.
<full HTTP request or curl one-liner> Copies of this mod
1 near-identical copy found in the catalogue:
- redteam-report-template — 95% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
Use this skill for client-deliverable reports:
- External red-team engagements with a signed SOW
- Pentest reports going to a CISO / IT-Sec team (not a triager)
- Findings that will be reviewed by both technical and non-technical stakeholders
- Reports that need DOCX/PDF output (not just markdown / platform UI)
Do NOT use for:
- Bug-bounty platform submissions (use
report-writing/bugcrowd-reportinginstead) - Quick proof-of-concept memos
- Internal team writeups
The 6-section format per finding
This is the canonical structure each finding follows:
## Finding F##: <descriptive title>
**Severity:** Critical / High / Medium / Low / Informational
**Status:** Confirmed / Patched mid-engagement / Suspected (1 signal)
**CVSS 3.1:** <score> (<vector>)
**Affected Asset:** <URL / IP / app name>
### 1. Subject
<One-line statement of the issue. Plain English, no jargon.>
### 2. Observations
<Bulleted list of what was observed during testing. Concrete facts only — no interpretation yet.>
- <Observation 1>
- <Observation 2>
- ...
### 3. Description
<Technical explanation of the vulnerability. 2-4 paragraphs. Reader should understand WHY the observations indicate a vulnerability, what the underlying flaw is.>
### 4. Impact
<What an attacker could achieve. Concrete attacker outcomes, NOT generic CIA triad statements. Tie to the client's business — money, data, reputation, regulatory exposure.>
### 5. Recommendation
<Specific, actionable remediation. Vendor patch, configuration change, code-level fix. Avoid "implement security best practices" — say what specifically.>
### 6. Proof of Concept (PoC)
<Steps to reproduce, numbered. Include the exact HTTP requests, payloads,tools used.>
**Step 1:** <action>
```http
<full HTTP request or curl one-liner>
Step 2:
<response excerpt>
Screenshot:

---
## Severity & status disciplines
### Severity table (client-facing — different from CVSS-only)
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 · 376 lines · 139 tokens per session scan A 2bb13a5b49c8
redteam-report-template is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 10d ago), licensed MIT. It adds 139 tokens to every session and 3,864 once invoked, about $0.0007 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.