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/bug-bountyWrote 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/bug-bounty)<a href="https://agentmods.dev/skills/uphiago/recon-skills/bug-bounty"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/bug-bounty/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/bug-bounty"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/bug-bounty.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 | $0.00121 | $0.21017 |
| Opus 5 | $0.00060 | $0.10509 |
| Sonnet 5 | $0.00024 | $0.04203 |
| Haiku 4.5 | $0.00012 | $0.02102 |
Grade A, and why
bug-bounty scanned grade A with 0 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 9d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to bug-bounty — 202 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,659 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bug Bounty Master Workflow
Full pipeline: Recon -> Learn -> Hunt -> Validate -> Report. One skill for everything.
THE ONLY QUESTION THAT MATTERS
"Can an attacker do this RIGHT NOW against a real user who has taken NO unusual actions -- and does it cause real harm (stolen money, leaked PII, account takeover, code execution)?"
If the answer is NO -- STOP. Do not write. Do not explore further. Move on.
Theoretical Bug = Wasted Time. Kill These Immediately:
| Pattern | Kill Reason |
|---|---|
| "Could theoretically allow..." | Not exploitable = not a bug |
| "An attacker with X, Y, Z conditions could..." | Too many preconditions |
| "Wrong implementation but no practical impact" | Wrong but harmless = not a bug |
| Dead code with a bug in it | Not reachable = not a bug |
| Source maps without secrets | No impact |
| SSRF with DNS-only callback | Need data exfil or internal access |
| Open redirect alone | Need ATO or OAuth chain |
| "Could be used in a chain if..." | Build the chain first, THEN report |
You must demonstrate actual harm. "Could" is not a bug. Prove it works or drop it.
CRITICAL RULES
- READ FULL SCOPE FIRST -- verify every asset/domain is owned by the target org
- NO THEORETICAL BUGS -- "Can an attacker steal funds, leak PII, takeover account, or execute code RIGHT NOW?" If no, STOP.
- KILL WEAK FINDINGS FAST -- run the 7-Question Gate BEFORE writing any report
- Validate before writing -- check CHANGELOG, design docs, deployment scripts FIRST
- One bug class at a time -- go deep, don't spray
- Verify data isn't already public -- check web UI in incognito before reporting API "leaks"
- 5-MINUTE RULE -- if a target shows nothing after 5 min probing (all 401/403/404), MOVE ON
- IMPACT-FIRST HUNTING -- ask "what's the worst thing if auth was broken?" If nothing valuable, skip target
- CREDENTIAL LEAKS need exploitation proof -- finding keys isn't enough, must PROVE what they access
- STOP SHALLOW RECON SPIRALS -- don't probe 403s, don't grep for analytics keys, don't check staging domains that lead nowhere
- BUSINESS IMPACT over vuln class -- severity depends on CONTEXT, not just vuln type
- UNDERSTAND THE TARGET DEEPLY -- before hunting, learn the app like a real user
- DON'T OVER-RELY ON AUTOMATION -- automated scans hit WAFs, trigger rate limits, find the same bugs everyone else finds
- HUNT LESS-SATURATED VULN CLASSES -- XSS/SSRF/XXE have the most competition. Expand into: cache poisoning, Android/mobile vulns, business logic, race conditions, OAuth/OIDC chains, CI/CD pipeline attacks
- ONE-HOUR RULE -- stuck on one target for an hour with no progress? SWITCH CONTEXT
- TWO-EYE APPROACH -- combine systematic testing (checklist) with anomaly detection (watch for unexpected behavior)
- T-SHAPED KNOWLEDGE -- go DEEP in one area and BROAD across everything else
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.
- 9d ago First seen · 1,659 lines · 121 tokens per session scan F cfff4cedba01
bug-bounty is a skill published in the GitHub repository uphiago/recon-skills (1,247 stars, last pushed 7d ago), licensed MIT. It adds 121 tokens to every session and 21,017 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to bug-bounty, differing in 202 lines, and is treated as a copy.
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.