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 adriannoes/awesome-agentic-ai --skill mid-engagement-ir-detectiongit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/mid-engagement-ir-detection)<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.
<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>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 | $0.00120 | $0.03871 |
| Opus 5 | $0.00060 | $0.01936 |
| Sonnet 5 | $0.00024 | $0.00774 |
| Haiku 4.5 | $0.00012 | $0.00387 |
Grade A, and why
mid-engagement-ir-detection scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
ms=$(curl -sk -o /dev/null -w "%{time_total}" "$target" --max-time 30) 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.
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:
- Themselves valuable findings (positive operational observations about IR responsiveness)
- Confirmation evidence (mid-engagement patch = the original vulnerability was real)
- 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.
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 · 352 lines · 120 tokens per session scan A 5e24398f0f13
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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starting-a-new-project
Use when the workspace is empty — no code yet — and the user brings a raw idea: the brand-new branch of setting-up-a-project, normally reached via that dispatcher, directly only when the situation is unmistakable. Not for features in an existing project — use brainstorming instead.
todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
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Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.
brainstorming
Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small.
reviewing-changes
Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with addreviewcomment, and settle with exactly one reviewverdict.