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 agentmods add skills/evilfreelancer/secs/engineering-detectionsnpx skills add EvilFreelancer/secs --skill engineering-detectionsgit clone --depth 1 https://github.com/EvilFreelancer/secsWhat 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 | $0.00077 | $0.03177 |
| Opus 5 | $0.00039 | $0.01588 |
| Sonnet 5 | $0.00015 | $0.00635 |
| Haiku 4.5 | $0.00008 | $0.00318 |
Grade A, and why
engineering-detections 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 2d 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.
curl -sL "https://defuddle.md/<url>" # scheme in the path is optional This is a copy
100% identical to engineering-detections — 0 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 — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engineering Detections
A detection is a hypothesis about attacker behaviour, expressed as a query, that a human will be paged for. Two properties decide whether it is worth deploying: does it fire on the behaviour, and does it stay quiet otherwise. Most rules fail the second test, and the cost is paid by whoever is on call.
When to Use
- Writing a new detection rule from a TTP, a sample, or an incident
- Reviewing or tuning an existing rule that is noisy or silent
- Converting threat intelligence into deployable detection content
- Assessing detection coverage against ATT&CK
- Setting up detection-as-code: repo layout, testing, CI, deployment
When NOT to Use
- Searching for an unknown compromise right now — use
hunting-threats - Working an active incident — use
responding-to-incidents - Analyzing the sample the detection is for — use
analyzing-malware - Authoring a file/memory signature — use
writing-yara-rules; a log/SIEM rule — usewriting-sigma-rules - Preventive controls and hardening — hardening is not detection; a rule is not a substitute for closing the path
Route to a Depth Skill
| Focus | Skill |
|---|---|
| Authoring a portable Sigma rule specifically — field taxonomy, modifiers, backend conversion, SigmaHQ standards | writing-sigma-rules |
This skill covers the whole detection lifecycle across Sigma, YARA, and
Suricata; reach for writing-sigma-rules when the task is the Sigma rule
itself and its conversion to a target SIEM.
Detect Behaviour, Not Artifacts
Rank what you write by how expensive it is for the adversary to change:
Hash trivial to change → block, don't alert
IP / domain days → block + low-severity alert
Filename / path trivial → weak signal, combine only
Tooling artifact weeks (recompile) → good, decays
Behaviour / TTP expensive → this is the target
The pyramid-of-pain reasoning is the whole discipline: a rule on
mimikatz.exe is worthless; a rule on a process opening a handle to LSASS
with PROCESS_VM_READ catches every tool that does the same thing.
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.
- 2d ago First seen · 316 lines · 77 tokens per session scan A c533bb8c20ad
engineering-detections is a skill published in the GitHub repository EvilFreelancer/secs (10 stars, last pushed 24d ago), licensed Apache-2.0. It adds 77 tokens to every session and 3,177 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to engineering-detections, differing in 0 lines, and is treated as a copy.
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