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Sast Report

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utkusen
sast-report

Consolidate all SAST vulnerability results from the sast/ folder into a single final report ranked by severity and confidentiality impact. Reads all *-results.md files and produces sast/final-report.md. Run after all vulnerability detection skills complete. Use when asked to generate a final report, consolidate findings, or summarize security results.

Overview

Publisherutkusen
Repositorysast-skills
Skill namesast-report
Stars
1.3K
Forks
64
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Sast 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/utkusen/sast-skills/tree/main/sast-files/.agents/skills/sast-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/utkusen/sast-skills.git /tmp/sast-skills
mkdir -p .claude/skills
cp -r /tmp/sast-skills/sast-files/.agents/skills/sast-report .claude/skills/sast-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sast 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 Sast 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 Sast 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.

Final Security Report Generation

You are consolidating all completed SAST vulnerability scan results into a single prioritized security report.

Prerequisites: At least one sast/*-results.md file must exist. Run the vulnerability detection skills first if they don't.


What to Include

Only include findings with these classifications from each result file:

  • [VULNERABLE]
  • [LIKELY VULNERABLE]

Exclude [NOT VULNERABLE] and [NEEDS MANUAL REVIEW] findings from the main report body (count them only in the summary).


Severity Ranking

Assign each finding a severity tier — Critical, High, Medium, or Low — using the table below as your baseline. Adjust up or down based on context (e.g., an IDOR that exposes financial records is High, not Medium).

Vulnerability ClassDefault Severity
RCE via command injection, eval, or unsafe deserializationCritical
SSTI (Server-Side Template Injection)Critical
SQLi on authentication endpointsCritical
JWT algorithm confusion (alg:none, RS256→HS256)Critical
File upload leading to code execution (webshell)Critical
SQLi with full data extraction capabilityHigh–Critical
GraphQL injection (user-controlled operation document enabling unauthorized fields or gateway abuse)High–Critical
XXE with file read or internal SSRFHigh–Critical
Missing authentication on sensitive endpointsHigh–Critical
SSRF reaching internal services or cloud metadataHigh
Path traversal reading sensitive or config filesHigh
File upload with stored content accessible to othersHigh
IDOR on PII, financial, or health dataHigh
XSS (stored/persistent)High
JWT with missing or bypassable claim validationMedium–High
Missing authentication on lower-sensitivity endpointsMedium
IDOR on non-sensitive dataMedium
XSS (reflected or DOM)Medium
Business logic flaws (price manipulation, workflow bypass)Medium
Information disclosure of non-sensitive dataLow

Confidentiality as a tiebreaker: When two findings share the same baseline severity, rank higher the one with greater confidentiality impact — i.e., the greater its potential to expose sensitive user data, credentials, or system internals.


Execution

Perform all steps in-session (no subagents needed).

Step 1: Discover result files

Check which of these files exist in sast/:

  • idor-results.md
  • sqli-results.md
  • ssrf-results.md
  • xss-results.md
  • rce-results.md
  • xxe-results.md
  • fileupload-results.md
  • pathtraversal-results.md
  • ssti-results.md
  • jwt-results.md
  • missingauth-results.md
  • businesslogic-results.md
  • graphql-results.md

Also read sast/architecture.md if it exists (use it for the project name and context when writing severity rationale).

Step 2: Read and extract findings

Read each existing result file. For every finding classified as [VULNERABLE] or [LIKELY VULNERABLE], extract:

  • Finding title
  • Vulnerability type (derived from the source file)
  • File / endpoint affected
  • Issue description
  • Impact description
  • Proof / code path
  • Remediation
  • Dynamic test steps (if present)

Step 3: Score and sort

Assign each finding a severity level (Critical / High / Medium / Low) using the table above. Sort all findings:

  1. Critical first, then High, Medium, Low
  2. Within each tier, sort by confidentiality impact (highest first)

Step 4: Write sast/final-report.md

Use exactly this output format:


markdown
# Security Assessment Final Report

**Project**: [name from architecture.md, or infer from codebase]
**Generated**: [current date]
**Scans completed**: [comma-separated list of scan types that had result files]

---

## Executive Summary

| Severity | Count |
|----------|-------|
| Critical | N |
| High     | N |
| Medium   | N |
| Low      | N |
| **Total confirmed findings** | **N** |

Scans with no confirmed vulnerabilities: [list]
Findings requiring manual review: N (see individual result files for details)

---

## Vulnerability Index

| # | Title | Type | Severity | Endpoint / File |
|---|-------|------|----------|----------------|
| 1 | ... | RCE | Critical | `POST /api/exec` |
| 2 | ... | SQLi | High | `GET /api/users` |

---

## Findings

### Critical

#### [Finding Title] — [Vuln Type]

- **Source scan**: `sast/[type]-results.md`
- **Classification**: Vulnerable *(or "Likely Vulnerable")*
- **Endpoint / File**: ...
- **Severity rationale**: [1–2 sentences explaining why this is Critical, with focus on confidentiality and integrity impact]
- **Issue**: ...
- **Impact**: ...
- **Proof**:

[code path or evidence from original finding]

- **Remediation**: ...
- **Dynamic Test**:

[curl command or step-by-step test instructions from original finding]


---

### High

[Same structure as Critical section]

---

### Medium

[Same structure]

---

### Low

[Same structure]

---

## Appendix: Scan Coverage

| Scan | Result File | Status |
|------|-------------|--------|
| IDOR | `sast/idor-results.md` | Completed / Not run |
| SQLi | `sast/sqli-results.md` | Completed / Not run |
| SSRF | `sast/ssrf-results.md` | Completed / Not run |
| XSS | `sast/xss-results.md` | Completed / Not run |
| RCE | `sast/rce-results.md` | Completed / Not run |
| XXE | `sast/xxe-results.md` | Completed / Not run |
| File Upload | `sast/fileupload-results.md` | Completed / Not run |
| Path Traversal | `sast/pathtraversal-results.md` | Completed / Not run |
| SSTI | `sast/ssti-results.md` | Completed / Not run |
| JWT | `sast/jwt-results.md` | Completed / Not run |
| Missing Auth | `sast/missingauth-results.md` | Completed / Not run |
| Business Logic | `sast/businesslogic-results.md` | Completed / Not run |
| GraphQL injection | `sast/graphql-results.md` | Completed / Not run |

Important Reminders

  • Include ONLY [VULNERABLE] and [LIKELY VULNERABLE] findings in the Findings section.
  • Mark [LIKELY VULNERABLE] findings clearly: append ⚠ Likely Vulnerable after the finding title.
  • Preserve all details from the original findings — do not summarize or truncate Proof, Remediation, or Dynamic Test sections.
  • If sast/architecture.md exists, use it to enrich the severity rationale with application-specific context (e.g., "this endpoint handles payment data, making confidentiality impact Critical").
  • Omit severity sections entirely (e.g., the ### Low heading) if no findings fall in that tier.

Frequently asked questions

What does the Sast Report AI skill do?

Consolidate all SAST vulnerability results from the sast/ folder into a single final report ranked by severity and confidentiality impact. Reads all *-results.md files and produces sast/final-report.md. Run after all vulnerability detection skills complete. Use when asked to generate a final report, consolidate findings, or summarize security results.

Why use Sast Report on TypingMind?

Because you install it once and use it with any model. Sast 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 Sast Report in TypingMind?

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

Which AI models can use Sast 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 Sast Report?

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

Is the Sast Report AI skill free?

Yes. It is published on GitHub by utkusen under the MIT 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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