opsec-discipline
Skill401 starsChanged 55 days agoMITView on GitHub
Use when about to take any outward or offensive action (request, payload, persistence, lateral movement, exfil, or feeding captured traffic to the model) — to decide detection footprint, cleanup, and secret redaction first
1 other project uses a version of this.
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Most recently taken up by
- mahumadad/quarrytheir own version, first seen Nov 8, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- mahumadad/quarry★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository38 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- active-directory-attackSkill
- advanced-redteamSkill
- ai-agent-redteamSkill
- ai-securitySkill
- browser-exploitationSkill
- cicd-supply-chainSkill
- cloud-securitySkill
- coding-masterySkill
- container-k8s-escapeSkill
- crypto-analysisSkill
- edr-evasionSkill
- engagement-flowSkill
- engagement-memorySkill
- exploit-developmentSkill
- finding-disciplineSkill
- incident-responseSkill
- initial-accessSkill
- keylogger-archSkill
- malware-analysisSkill
- mobile-pentestSkill
- network-attackSkill
- privesc-linuxSkill
- privesc-windowsSkill
- recon-osintSkill
- red-team-opsSkill
- reverse-engineeringSkill
- scope-disciplineSkill
- shellcode-devSkill
- threat-huntingSkill
- threat-model-disciplineSkill
- using-offensive-claudeSkill
- vulnerability-analysisSkill
- web-pentestSkill
- windows-boundariesSkill
- windows-mitigations-bypassSkill
- wireless-rfSkill
- writing-offensive-skillsSkill
The file
Files
OPSEC Discipline
Overview
Decide the detection footprint and cleanup before acting, not after. Every offensive technique in this repo pairs with the telemetry it generates and a detection signature — use that pairing to choose the quieter path and to know what you'll need to clean up.
Before acting, answer three questions
- Detection — what telemetry does this generate? Which Sigma/EDR rule would catch it? (Each domain skill's OPSEC & Detection table has this.) Pick the lower-noise variant when one exists.
- Cleanup — what does this touch (files, registry, services, accounts, tickets)? How is it reverted? Stage the cleanup before you create the artifact.
- Secret hygiene — any captured traffic / output that reaches the model or the report must be
redacted at the boundary:
python skills/coding-mastery/scripts/_lib/redact_headers.py < exchange.txt(orredact_text()), so Authorization/Cookie/API-key/JWT values never land in context or the report.
Red Flags — STOP, decide OPSEC first
- "I'll worry about cleanup after I'm in" (you won't; decide now)
- "Just paste the raw request/response" (redact secrets at the boundary first)
- "Loudest exploit is fine, it works" (pick the variant with the smaller footprint)
- "Persistence now, document later" (persistence on un-authorized hosts is out — see scope-discipline)
- "The token's only in a log" (a logged secret is a leaked secret — mask it)
Rationalizations
| Excuse | Reality |
| "OPSEC slows me down" | Unplanned noise/loot is how engagements get burned and how data leaks. |
| "It's an internal tool log" | Logs get shipped, shared, and indexed. Redact. |
| "Cleanup is a reporting-phase task" | You can't clean what you didn't track. Track at action time. |
OPSEC is part of every action, not a phase. Detection-awareness also strengthens the defensive value of the finding (you can tell the client exactly how to catch it).
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

