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Ghost Report

Organization
ghostsecurity
ghost-report

Ghost Security — combined security report. Aggregates findings from all scan skills (scan-deps, scan-secrets, scan-code) into a single prioritized report focused on the highest risk, highest confidence issues. Use when the user requests a security overview, vulnerability summary, full security audit, or combined scan results.

Overview

Publisherghostsecurity
Repositoryskills
Skill nameghost-report
Stars
417
Forks
25
Bundled files
1
Licenseapache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ghostsecurity on GitHub. Read the source before you install it.

Installation

Install the Ghost Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ghostsecurity/skills/tree/main/plugins/ghost/skills/report
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ghostsecurity/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/ghost/skills/report .claude/skills/ghost-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ghost Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ghost Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ghost Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Combined Security Report

You aggregate findings from all scan skills (scan-deps, scan-secrets, scan-code) into a single prioritized report. Do all work yourself — do not spawn subagents or delegate.

$ARGUMENTS


Step 0: Setup

Run this Bash command to compute paths:

bash
repo_name=$(basename "$(pwd)") && remote_url=$(git remote get-url origin 2>/dev/null || pwd) && short_hash=$(printf '%s' "$remote_url" | git hash-object --stdin | cut -c1-8) && repo_id="${repo_name}-${short_hash}" && short_sha=$(git rev-parse --short HEAD 2>/dev/null || date +%Y%m%d) && ghost_repo_dir="$HOME/.ghost/repos/${repo_id}" && scans_dir="${ghost_repo_dir}/scans/${short_sha}" && cache_dir="${ghost_repo_dir}/cache" && skill_dir=$(find . -path '*/skills/report/SKILL.md' 2>/dev/null | head -1 | xargs dirname) && echo "scans_dir=$scans_dir cache_dir=$cache_dir skill_dir=$skill_dir"

Store scans_dir (commit-level scan directory), cache_dir, and skill_dir.


Cache Check

If <scans_dir>/report.md already exists, show:

Combined security report is at: <scans_dir>/report.md

And stop. Do not regenerate it.


Step 1: Read Repo Context

Read <cache_dir>/repo.md if it exists. Extract:

  • Business criticality
  • Sensitive data types
  • Component map

If it does not exist, continue without it — this is not an error.


Step 2: Discover Scan Results

List the contents of <scans_dir> to see which scan-type directories exist. Recognized types:

  • deps/ — SCA / dependency vulnerability scan
  • secrets/ — secrets and credentials scan
  • code/ — code security scan (SAST)

If none of these directories exist, report an error:

No scan results found in <scans_dir>. Run one or more scan skills first:
  /ghost-scan-deps
  /ghost-scan-secrets
  /ghost-scan-code

And stop.


Step 3: Collect Findings

For each scan type that exists, glob <scans_dir>/<type>/findings/*.md and read each finding file in full. Retain the complete markdown body of every finding — the report will inline this content directly so readers never need to open individual finding files.

From each finding, also extract these metadata fields for filtering and sorting:

  • ID — from ## Metadata → ID
  • Type — the scan type (deps, secrets, or code)
  • Severity — from ## Metadata → Severity (high, medium, low)
  • Status — from ## Metadata → Status (e.g., confirmed-exploitable, unverified, verified, rejected, clean)

Step 4: Filter and Sort

Filter: Keep only high-confidence findings:

  • For deps findings: status is confirmed-exploitable
  • For secrets findings: status is NOT clean and NOT rejected
  • For code findings: status is verified or unverified (NOT rejected)

Exclude any finding with status clean, rejected, or false-positive.

Sort the remaining findings:

  1. By severity: high first, then medium, then low
  2. Within same severity: deps before secrets before code

Step 5: Read Per-Scan Reports

For deps and secrets scan types, read <scans_dir>/<type>/report.md if present. Extract:

  • Statistics (candidates scanned, confirmed findings, false positives filtered)
  • Executive summary highlights

Note: code does not produce a report.md. For code scan coverage, count the finding files in <scans_dir>/code/findings/ directly. The "Candidates Scanned" count is the total number of finding files (all statuses). "Confirmed Findings" is the count with status verified, confirmed, or unverified. "False Positives Filtered" is the count with status rejected. Do NOT count clean file analyses from the nomination/analysis funnel — those never became findings.

If a per-scan report does not exist for deps or secrets, note it as unavailable.


Step 6: Generate Report

  1. Read <skill_dir>/report-template.md
  2. Populate the template with collected data:
    • Fill Scan Information with repository name, commit SHA, date, and which scans ran
    • Write Executive Summary using repo context and aggregated findings
    • For all writing elements in this security-focused, objective and fact based report, use a neutral, human tone that balances expertise with ease of reading. Do not use emojis, em-dashes, etc.
    • For Critical & High findings (severity = high): inline the substantive content from each finding file directly into the report — include code snippets, assessment tables, remediation commands, and all relevant detail so the report is fully self-contained
    • For Medium findings: write a full subsection per finding with description, location, code context, and remediation (not a condensed table)
    • Omit low-severity findings (they remain in per-scan finding files only)
    • Fill Scan Coverage table from per-scan report statistics (for code, use finding file counts from Step 5)
    • Add a brief methodology note per scan type that ran (1-2 sentences drawn from per-scan reports)
    • Do NOT include links to per-scan reports or individual finding files — all content is inlined
  3. Write the report to <scans_dir>/report.md

Step 7: Show Output

Combined security report is at: <scans_dir>/report.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ghost Report AI skill do?

Ghost Security — combined security report. Aggregates findings from all scan skills (scan-deps, scan-secrets, scan-code) into a single prioritized report focused on the highest risk, highest confidence issues. Use when the user requests a security overview, vulnerability summary, full security audit, or combined scan results.

Why use Ghost Report on TypingMind?

Because you install it once and use it with any model. Ghost Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ghost Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ghostsecurity/skills/tree/main/plugins/ghost/skills/report. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ghost Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ghost Report?

As many as you like. As long as a model supports skills, you can use Ghost Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ghost Report AI skill free?

Yes. It is published on GitHub by ghostsecurity under the apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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