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 mid-engagement-ir-detectiongit 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/mid-engagement-ir-detection)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.04382 |
| Opus 5 | $0.00060 | $0.02191 |
| Sonnet 5 | $0.00024 | $0.00876 |
| Haiku 4.5 | $0.00012 | $0.00438 |
Grade A, and why
mid-engagement-ir-detection scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
ms=$(curl -sk -o /dev/null -w "%{time_total}" "$target" --max-time 30 --connect-timeout 10) Copies of this mod
1 near-identical copy found in the catalogue:
- mid-engagement-ir-detection — 91% identical, 50 lines differ
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:
- 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.
- 8d ago First seen · 382 lines · 120 tokens per session scan A d5ea141170ce
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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