agentleFS
AhmedAl-AshwalAhmedAl-Ashwal/claude-code-global-config/skills/report-writing/SKILL.md

report-writing

Skill1 starsChanged 52 days agoMITView on GitHub

Write professional Arabic or English reports, analyses, guides, manuals, test reports, and migration guides as Markdown first, then convert once to HTML, DOCX, and PDF with bundled scripts instead of regenerating the text per format. Use for any تقرير, report, دليل, guide, memo, documentation deliverable, "write up the findings", or "compare X with this PDF and report" request, even when the user only asks for a summary document.

  • Installs packages

17 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use cloud-pentest, llm-redteam and 10 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

  • hhjkjkjkSkill

    bb-methodology

    Use at the START of any bug bounty hunting session, when switching targets, or when feeling lost about what to do next. Master orchestrator that combines the 5-phase non-linear hunting workflow with the critical thinking framework (developer psychology, anomaly detection, What-If experiments). Routes to all other skills based on current hunting phase. Also use when asking "what should I do next" or "where am I in the process.

    Also in 6 projects that use this

  • hhjkjkjkSkill

    meme-coin-audit

    Meme coin and token security audit — rug pull detection (honeypot, hidden mint, fee manipulation, LP lock bypass), Solana SPL token analysis (freeze authority, mint authority, metadata mutability), Token-2022 extension risks (transfer hooks, permanent delegate), DEX liquidity pool attacks (sandwich amplification, LP drain, bonding curve exploits), pump.fun/Raydium/Jupiter integration risks, token_scanner.py automation, and real exploit examples from 2024-2025. Use for any token audit, rug pull assessment, meme coin security review, or pre-investment due diligence.

    Also in 5 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

4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 3

    Distinctive

    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
  1. Very specific:

    Write the Markdown once to Reports/<Title> <YYYY-MM-DD>.md.

    line 37
  2. Distinctive:

    Every finding cites its evidence: a file, screen, DocType, test id, or page number.

    line 51
  3. Distinctive:

    No filler: no restating the brief, no generic advice, no repeated headings.

    line 52
  4. Distinctive:

    Keep versions: a new date-stamped file per revision.

    line 53

“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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Reports: gather with scripts, write once, convert by script

Why this playbook exists

A report costs tokens three ways: reading sources in full, drafting the same text several times, and regenerating it per output format. Cut all three: extract facts with scripts into a compact facts file, write the Markdown once, and convert with the bundled scripts.

Workflow

  1. Facts first. Put every number, list, and finding into facts.md or facts.json produced by scripts (see the data-analysis skill). Read PDFs by page range (the Read tool's pages argument) or extract text with a script into a file you grep; never load a whole document into the conversation. Reuse the previous report of the same series in Reports/ for structure and terminology instead of re-deriving it.
  2. Outline. Use this skeleton unless the series already has its own:

    # Title (date, version)
    ## Executive summary          five lines at most
    ## Scope and method
    ## Findings                   a table: id, area, severity, evidence, recommendation
    ## Recommendations            prioritized
    ## Appendix                   raw tables, test log summaries
  3. Write the Markdown once to Reports/<Title> <YYYY-MM-DD>.md. Arabic reports: Modern Standard Arabic, the technical term in English in parentheses on first use, one numeral system throughout (match the series), short sentences, tables instead of prose for comparisons.
  4. Convert by script, never by rewriting:
    • HTML: python <skill>/scripts/md_to_html.py report.md report.html --rtl (drop --rtl for English).
    • DOCX: python <skill>/scripts/md_to_docx.py report.md report.docx --rtl.
    • PDF from the HTML with headless Chrome: "C:/Program Files/Google/Chrome/Application/chrome.exe" --headless=new --disable-gpu --no-pdf-header-footer --print-to-pdf="report.pdf" "file:///C:/path/report.html" The scripts need markdown and python-docx (pip install markdown python-docx).
  5. Deliver the file paths plus a five-line summary in chat. Never paste the report body into chat.

Style

  • Every finding cites its evidence: a file, screen, DocType, test id, or page number.
  • No filler: no restating the brief, no generic advice, no repeated headings.
  • Keep versions: a new date-stamped file per revision; add one line to the series index or the project CHANGELOG when one exists.
  • Word or PowerPoint requested by name with features the scripts do not cover (tracked changes, templates, slides): use the docx or pptx skill from the same Markdown.

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