automation-recon-pipeline
Skill26 starsChanged 34 days agoMITView on GitHub
Chain recon tools into a repeatable, resumable pipeline for continuous bug-bounty coverage. Load on "automate recon", "recon pipeline", monitoring many programs, or scaling subdomain→ live→scan. Signals: wildcard scope at scale, wanting scheduled/continuous discovery.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- NoorQureshi/roninword for word, first seen Oct 11, 2026
- NoorQureshi/security-skillsword for word, first seen Oct 11, 2026
Other versions2 projects use a version of this.
Word for word · 2
- NoorQureshi/security-skills★ 27100% same
- NoorQureshi/ronin★ 26100% same
Edited · 0
None found.
Wrote their own version · 0
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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 repository207 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- ad-adcsSkill
- ad-dacl-abuseSkill
- ad-delegation-abuseSkill
- ad-kerberoastingSkill
- ad-pivot-arsenalSkill
- ai-agent-tool-abuseSkill
- ai-insecure-output-handlingSkill
- ai-jailbreakSkill
- ai-llm-dosSkill
- ai-mcp-securitySkill
- ai-model-extractionSkill
- ai-prompt-injectionSkill
- ai-rag-poisoningSkill
- ai-sensitive-data-disclosureSkill
- ai-supply-chainSkill
- ai-system-prompt-leakageSkill
- api-auth-attacksSkill
- api-bolaSkill
- api-fuzzingSkill
- api-graphqlSkill
- api-grpcSkill
- api-mass-assignmentSkill
- api-mongo-agg-facet-bypassSkill
- api-testing-checklistSkill
- api-versioningSkill
- automation-browserSkill
- automation-nuclei-templatesSkill
- cloud-azureSkill
- cloud-container-escapeSkill
- cloud-docker-api-abuseSkill
- cloud-docker-registrySkill
- cloud-gcpSkill
- cloud-iam-privescSkill
- cloud-imds-ssrfSkill
- cloud-kubernetesSkill
- cloud-s3-exposureSkill
- code-review-cicdSkill
- code-review-cppSkill
- code-review-dangerous-sinksSkill
- code-review-dotnetSkill
- code-review-goSkill
- code-review-iacSkill
- code-review-javaSkill
- code-review-methodologySkill
- code-review-nodejsSkill
- code-review-phpSkill
- code-review-pythonSkill
- code-review-rubySkill
- code-review-rustSkill
- code-review-secrets-detectionSkill
- code-review-soliditySkill
- crypto-oracle-attacksSkill
- crypto-rsa-attacksSkill
- ctf-cryptoSkill
- ctf-forensicsSkill
- ctf-methodologySkill
- ctf-pwnSkill
- ctf-revSkill
- The first 60 shown.
The file
Files
Recon automation pipeline
When it applies
You track one or more wildcard programs and want discovery to run repeatably (and on a schedule), surfacing only new assets/findings instead of re-reviewing everything.
Why it works
Recon is a directed flow: roots → subdomains → resolve → live → ports → nuclei. Wiring the
ProjectDiscovery tools together (each reads/writes lines) makes it composable, and diffing
against last run (anew) turns it into continuous monitoring that alerts on change.
Method
- Pipeline (each stage feeds the next):
subfinder -dL roots.txt -all -silent \ | dnsx -silent -a -resp-only \ | httpx -silent -sc -title -tech-detect \ | tee live.txt \ | nuclei -silent -severity low,medium,high,critical - Diff for "only new": pipe each stage through
anew subs.txt/anew live.txtso reruns emit only newly-seen lines — the basis of continuous monitoring. - Notify: pipe results to
notify(Slack/Discord/Telegram) so new hosts/findings alert you. - Schedule with cron/systemd-timer/GitHub Actions; persist state files per program so runs resume.
- Keep templates current:
nuclei -update-templatesbefore each run.
Gotchas
- Stay in scope: feed only in-scope roots, filter out-of-scope hosts before scanning.
- Respect rate limits (
-rl,-c) and program automation rules — don't hammer. - De-dupe wildcard DNS (
dnsxwildcard filtering) or you'll alert on noise forever.
Verify success
A rerun surfaces only newly-appeared assets/findings and notifies you — hands-off continuous coverage.
References
ProjectDiscovery pipeline docs; TomNomNom anew; nuclei templates.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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.

