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 evidence-hygienegit 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/evidence-hygiene)<a href="https://agentmods.dev/skills/uphiago/recon-skills/evidence-hygiene"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/evidence-hygiene/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/evidence-hygiene"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/evidence-hygiene.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 | $0.00190 | $0.05531 |
| Opus 5 | $0.00095 | $0.02766 |
| Sonnet 5 | $0.00038 | $0.01106 |
| Haiku 4.5 | $0.00019 | $0.00553 |
Grade A, and why
evidence-hygiene 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 11d 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 evidence-hygiene — 96 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 — 467 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EVIDENCE HYGIENE — PoC Capture & Redaction Discipline
Use this skill BEFORE capturing any screenshot, exporting any HAR, or attaching any evidence to a bug-bounty submission. It catches the most common evidence-hygiene mistakes that cause cookies to leak, PII to be shared without consent, or screenshots to be unsuitable for triage.
The core principle: Bug-bounty evidence is meant to convince a triager. Anything beyond that — live cookies, real-user PII, internal trace IDs that aren't useful — should not be in the evidence.
1. Two Categories of Sensitive Data
Every PoC artifact (screenshot, HAR, raw HTTP request, terminal transcript) potentially contains data that needs different treatment.
| Category | Examples | Treatment |
|---|---|---|
| Your-account secrets | Session cookies, OAuth tokens, refresh tokens, API keys | Always redact. Even in private bug-bounty platform attachments. Your account, your session — protect it. |
| Other users' PII | Real names, emails, phone numbers, addresses, profile photos, account IDs | Redact unless explicitly demonstrating cross-account impact. Even then, mask faces and minimize the data you display. |
| Triager-useful metadata | Trace IDs (x-datadog-trace-id), request IDs, server timestamps, your test account UID/email, GraphQL operation names, response shapes |
Leave visible — these help the triager correlate to logs and reproduce. |
| Test-account passwords (limited use) | Throwaway passwords on a test account (e.g., Testing@5678) |
Acceptable in screenshots if you rotate immediately after submission so the value shown is dead. Don't leave real-use passwords in evidence. |
2. Cookie Redaction Protocol
2.1 What must be masked
The session cookie value is the highest-value secret in any PoC. Mask:
- The session cookie (
authn,session,sid,__Secure-id, etc. — name varies per target) csrf-tokenif it's bound to your sessionAuthorizationheaders (Bearer tokens, JWT)Cookierequest header values for any session-bearing cookieSet-Cookieresponse header values for any session-bearing cookie
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.
- 11d ago First seen · 467 lines · 190 tokens per session scan A 50ba370c3fab
evidence-hygiene is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 9d ago), licensed MIT. It adds 190 tokens to every session and 5,531 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to evidence-hygiene, differing in 96 lines, and is treated as a copy.
Other skills, from other repositories
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implementing-aws-config-rules-for-compliance
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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
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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.