nuclei-suggest
Skill153 starsChanged 46 days agoMITView on GitHub
Browse the nuclei-templates library, recommend a scoped template set for a given stack, target, or CVE, then run it against an authorized target and report findings.
In this repository41 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- broken-access-controlSkill
- cdn-peekSkill
- http-request-smugglingSkill
- jwt-attacksSkill
- ssrf-oobSkill
- webapp-probeSkill
- blockchain-forensicsSkill
- foundry-pocSkill
- safe-huntSkill
- smart-contract-auditSkill
- cloud-bucket-bruteSkill
- infrastructure-auditSkill
- dylib-hijack-scanSkill
- endpoint-threat-huntSkill
- parse-securitySkill
- agentic-seoSkill
- agent-onboardingSkill
- context-window-to-skillSkill
- git-commitSkill
- keen-eyesSkill
- no-aispeakSkill
- secure-1pass-accessSkill
- self-improveSkill
- shadow-partnerSkill
- training-guideSkill
- variant-tableSkill
- auditor-quizSkill
- audit-scopeSkill
- evidence-captureSkill
- external-enumerationSkill
- gdocs-audit-reportSkill
- hackerone-reportSkill
- pre-bountySkill
- sandboxed-audit-runnerSkill
- tiny-auditorSkill
- vm-labSkill
- llm-fuzz-toolsSkill
- mcp-server-pentestSkill
- prompt-injection-auditSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 7
Use nuclei and templates to empower a bug bounty or authorized assessment engagement hunt to the full capabilities of crowdsourced security.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Use nuclei and templates to empower a bug bounty or authorized assessment engagement hunt to the full capabilities of crowdsourced security. see ./references/install.md for first installation of nuclei on the environment
Methodology
- Index once into id, path, severity, tags, tier, replicable; rebuild only when the templates tree mtime changes. See
references/browsing.mdfor traversal. - Confirm scope before any run: in-scope hosts, desired posture (e.g. passive v.s. active scan), and whether OAST callbacks are workable within the enivronment.
- Tier by blast radius from template contents:
file/,dns/,ssl/and single benign GETs are safe;intrusive,brute,fuzz,unsafe: trueandinteractshrequire extra user consideration;code/executes on your own host and should almost never be used. Honor.nuclei-ignoreand never exceed the confirmed ceiling.
Run preferences
Run the scoped set with -duc (disable the update check, which otherwise stalls scripted runs), -jsonl -irr -store-resp -srd <dir> for structured output plus captured request/response, -etags above the cap, -rl rate limiting, and a self-hosted OAST server if callbacks are permitted. On a target that returns a 200 catch-all (SPA app shell), prefer content/word matchers over status-only templates, which false-positive. Treat a skipped template as unrun, not clean.
Output
Surface every finding, ranked by severity, with: its template-id; a full clickable template link, https://github.com/projectdiscovery/nuclei-templates/blob/<sha>/<template-path> where <sha> is git -C <clone> rev-parse HEAD, commit-pinned so the link is the exact template that fired; the matched-at URL; and the captured evidence, http/dns from the JSONL request/response and ssl/network from the stored file under -srd. In the response, re-run the template's own matcher (words/regex/dsl) to locate the indicator and mark it: ANSI-highlight the matched span in a terminal, caret-underline it when piped no-color, and name the pattern that fired.
Researcher needs to know exactly which templates were executed, and if some returned true positive, then the exact request/response or execution log and what indicator is bad.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
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.

