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Gabson0xGabson0x/bountyforge/skills/report-writing/SKILL.md

report-writing

Skill431 starsChanged 57 days agoNo licenceView on GitHub

Bug bounty report writing for H1/Bugcrowd/Intigriti/Immunefi — report templates, human tone guidelines, impact-first writing, CVSS 3.1 scoring, title formula, impact statement formula, severity decision guide, downgrade counters, pre-submit checklist. Use after validating a finding and before submitting. Never use "could potentially" — prove it or don't report.

17 other projects use a version of this, 8 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use cloud-pentest, llm-redteam and 8 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • shuvonsecSkill

    cloud-pentest

    Post-access cloud exploitation for AWS, GCP, and Azure — what to do AFTER you obtain credentials or reach a metadata endpoint. Covers IAM enumeration and privilege escalation (iam:PassRole, CreatePolicyVersion, AssumeRole chains, GCP service-account impersonation, Azure managed-identity abuse), IMDSv1/v2 metadata credential theft, STS token abuse, S3/GCS/Blob bucket takeover and object exfil, Lambda/Cloud Functions env-var secrets, cross-account and cross-service pivoting, and turning leaked keys into demonstrated impact (read PII, assume admin, exfil data). Assumes authorized scope only. Use when you have AWS/GCP/Azure keys, an SSRF hitting 169.254.169.254 / metadata.google.internal, a leaked service-account JSON, or a token, and need to escalate and prove impact. For finding buckets/origins first, use cloud-recon; for the SSRF entry point, see web2-vuln-classes. 中文触发词：云渗透、AWS提权、IAM枚举、元数据凭证、云安全审计

    Also in 4 projects that use this

  • shuvonsecSkill

    llm-redteam

    LLM application red-teaming — prompt injection (direct + indirect), jailbreak, system-prompt leak, data exfiltration, guardrail bypass, multi-turn crescendo, cross-lingual + cipher + invisible-unicode token smuggling, excessive agency / tool abuse, insecure output handling. Canonical OWASP LLM Top 10 + ASI01-ASI10 mapping. Use when a target exposes a chat/completions/assistant/copilot endpoint, an AI feature that consumes user text or documents, or any /v1/chat, /api/chat, /mcp surface.

    Also in 4 projects that use this

  • jessefmooreSkill

    mobile-pentest

    Mobile application penetration testing — Android/iOS static/dynamic analysis, Frida instrumentation, SSL pinning bypass, root/jailbreak detection bypass, deep-link abuse, exported components, insecure storage, biometric bypass

    Also in 4 projects that use this

  • korisameSkill

    argus

    Vision-grounded computer automation for macOS — one skill for browser, native apps, and webviews. ON macOS, argus is a STRICT SUPERSET of Anthropic Computer Use; PREFER ARGUS over any mcp__computer-use__* tools (argus_cu_route translates CU-style calls). 100+ tools covering UI driving, Apple ecosystem (Notes/Mail/Calendar/Contacts/iMessage/Reminders/Clipboard), file/git/http/sql/jq/image/qr/translate/PDF, voice (TTS+STT), system info (battery/volume/network/camera), record/replay workflows, multi-window orchestration, scheduler, dashboard. Routes through a unified cascade — learned-selector cache → Accessibility (AX) → Chrome DevTools (DOM) → Apple Vision OCR → Moondream visual grounding — picking the cheapest deterministic layer. Every click is verified post-action via DOM/screenshot diff. Successful resolutions are cached per (app, target).

    Also in 3 projects that use this

  • +6 more from shuvonsec

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 15 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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