mid-engagement-ir-detection

mid-engagement-ir-detection is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 120 tokens per session (3,871 once invoked), scanned A, a copy of mid-engagement-ir-detection, MIT.

A red-team testing method for noticing when a client’s security team or systems change during an authorized engagement. A red team is hired to simulate attackers and test defenses.

In plain words
What is it for?
Use it to record mid-engagement patches, new web-application-firewall behavior, response changes, and evidence of incident-response activity.
Why use it?
It prevents a vulnerability from being misclassified when patches, firewall rules, monitoring, or lockouts change the results during testing.

Skill for Claude CodeCodex

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

Good fit Use it to record mid-engagement patches, new web-application-firewall behavior, response changes, and evidence of incident-response activity.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection
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 adriannoes/awesome-agentic-ai --skill mid-engagement-ir-detection
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

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/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection/github.svg)](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection)
Your own site
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/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/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/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 3,871 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.
Origin 91% copy Near-identical to another mod 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 $0.03871
Opus 5 $0.00060 $0.01936
Sonnet 5 $0.00024 $0.00774
Haiku 4.5 $0.00012 $0.00387

Measured 9d ago against content hash 5e24398f0f13, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 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.

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)
Origin

This is a copy

91% identical to mid-engagement-ir-detection — 50 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.

cursor-claude-codex/skills/bug-hunter/skills/mid-engagement-ir-detection/SKILL.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 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 · 352 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. 9d ago First seen · 352 lines · 120 tokens per session scan A 5e24398f0f13

Subscribe to this mod's changes

mid-engagement-ir-detection is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 120 tokens to every session and 3,871 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to mid-engagement-ir-detection, differing in 50 lines, and is treated as a copy.

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