mid-engagement-ir-detection

mid-engagement-ir-detection is a skill for Claude Code, Codex from uphiago/recon-skills. It costs 120 tokens per session (4,382 once invoked), scanned A, original, MIT.

A red-team assessment guide for noticing security changes and attacker activity while an authorized engagement is underway.

In plain words
What is it for?
It is for comparing response times, errors, headers, cookies, response sizes, and lockouts across different testing windows.
Why use it?
It helps determine whether a vulnerability was patched, blocked by a web application firewall, or otherwise changed in response to testing.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit It is for comparing response times, errors, headers, cookies, response sizes, and lockouts across different testing windows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uphiago/recon-skills/mid-engagement-ir-detection
About the project

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.

uphiago/recon-skills · 1,254 stars · on GitHub · hiago.sh

Install

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.

Any agent
npx skills add uphiago/recon-skills --skill mid-engagement-ir-detection
Clone the repo
git clone --depth 1 https://github.com/uphiago/recon-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for mid-engagement-ir-detection

README.md
[![agentmods](https://agentmods.dev/badge/skills/uphiago/recon-skills/mid-engagement-ir-detection/github.svg)](https://agentmods.dev/skills/uphiago/recon-skills/mid-engagement-ir-detection)
Your own site
<a href="https://agentmods.dev/skills/uphiago/recon-skills/mid-engagement-ir-detection"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/mid-engagement-ir-detection/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.

agentmods 80×15 button for mid-engagement-ir-detection

Your own site · 80×15
<a href="https://agentmods.dev/skills/uphiago/recon-skills/mid-engagement-ir-detection"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/mid-engagement-ir-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,382 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00120 $0.04382
Opus 5 $0.00060 $0.02191
Sonnet 5 $0.00024 $0.00876
Haiku 4.5 $0.00012 $0.00438

Measured 8d ago against content hash d5ea141170ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

mid-engagement-ir-detection 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.

ms=$(curl -sk -o /dev/null -w "%{time_total}" "$target" --max-time 30 --connect-timeout 10)
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

redteam/mid-engagement-ir-detection/SKILL.md · 382 lines

How it starts

The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.

When to use this skill

Trigger when:

  • Running active testing against a target with active SOC monitoring
  • A confirmed-vulnerable finding stops reproducing on recheck
  • Baseline timing shifts unexpectedly (3× slower, sudden errors, new headers)
  • Response sizes change between test windows
  • New WAF cookies or headers appear that weren't there at session start
  • Lockout / error rates change between test windows (especially LOCKED count for credential attacks)
  • Engagement is "assume breach" or "white box" — client knows you're testing

DO NOT use for:

  • Bug bounty (client doesn't know you're there; no real-time IR)
  • Pure recon (no state-change happening)
  • One-off vulnerability scanning (no temporal dimension)

The core insight

In a real red-team engagement against a competent SOC, the security state of the target is not static. It changes during your test in response to your traffic. These state changes are:

  1. Themselves valuable findings (positive operational observations about IR responsiveness)
  2. Confirmation evidence (mid-engagement patch = the original vulnerability was real)
  3. Classification signals (WAF rule deployment vs code fix — different remediation depth)

Anti-pattern: treating reproduction failure as evidence the original signal was a false positive. Original PoC artifacts captured before the change are still the vulnerability finding.


The discipline — capture before, diff after

Before any active test:

# Capture pre-test fingerprint of the target
fingerprint = {
    "ts_pre": time.time(),
    "ip_seen": "<operator-src-ip>",
    "baseline_response_time_ms": <measure>,
    "baseline_response_size_bytes": <measure>,
    "response_headers": <capture set>,
    "waf_cookies": <list>,
    "lockout_count_in_state": <count from o365_attempts.json>,
}

Persist to engagement_log/fingerprint_pre.json.

During the test:

Log every test result with full context (timestamp, IP, payload, response code, response size, response time, headers if relevant) to JSONL append-only.

Read the full file on GitHub · 382 lines

Changes

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.

  1. 8d ago First seen · 382 lines · 120 tokens per session scan A d5ea141170ce

Subscribe to this mod's changes

mid-engagement-ir-detection is a skill published in the GitHub repository uphiago/recon-skills (1,254 stars, last pushed 10d ago), licensed MIT. It adds 120 tokens to every session and 4,382 once invoked, about $0.0006 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.

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