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engage.report

engage.report is a command for Claude Code from hypnguyen1209/offensive-claude. It costs 11 tokens per session (1,785 once invoked), scanned A, original, MIT.

A command for producing the final report at the end of a security assessment. It collects findings, checks their required details, and can create full, executive, or technical reports.

In plain words
What is it for?
Use it to aggregate findings, sort them by severity, assign CVSS risk scores, save confirmed patterns, and generate the final report.
Why use it?
It turns scattered finding files into an ordered report and keeps unconfirmed findings out of reusable security records.

Command for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python skills/engagement-memory/scripts/pattern_db.py record --json '<finding json>'.

Part of the offensive-claude plugin — 30 skills, 18 commands, 8 agents, 1 hook shipped together

not rated 357repo +3 23d ago A scan 11 tokens original MIT

Good fit Use it to aggregate findings, sort them by severity, assign CVSS risk scores, save confirmed patterns, and generate the final report.

Compare 6 commands from other repositories ↓
View source ↗ hypnguyen1209/offensive-claude
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/hypnguyen1209/offensive-claude
agentmods
npx agentmods add commands/hypnguyen1209/offensive-claude/engage.report

Made for: Claude Code.

Or install offensive-claude, the plugin that ships this one along with the rest of its 30 skills, 18 commands, 8 agents, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for engage.report

README.md
[![agentmods](https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.report/github.svg)](https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.report)
Your own site
<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.report"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.report/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for engage.report

Your own site · 80×15
<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.report"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,785 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00011 $0.01785
Opus 5 $0.00005 $0.00892
Sonnet 5 $0.00002 $0.00357
Haiku 4.5 $0.00001 $0.00178

Measured 10d ago against content hash 388f8c6dffc0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

engage.report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/engage.report.md · 247 lines

How it starts

The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/engage.report

Executes Phase 8 (Reporting) of the engagement workflow.

Usage

/engage.report [--format <format>] [--severity-threshold <level>]

Options:

  • --format: Report format (full, executive, technical)
  • --severity-threshold: Minimum severity to include (critical, high, medium, low)

Process

1. Finding Aggregation

Gathers all finding records from exploit/findings/:

  • Reads each finding-<id>.md file
  • Validates all required fields present
  • Sorts findings by severity (Critical > High > Medium > Low)
  • Assigns risk ratings using CVSS v3.1 scoring

1.5 Persist Confirmed Findings (engagement-memory)

For each [CONFIRMED] finding (validated by validate_findings.py / the finding-validator agent), record it as a reusable pattern so future engagements recall it. [POSSIBLE]/[REJECTED] are NOT learned.

python skills/engagement-memory/scripts/pattern_db.py record --json '<finding json>'
# store technique + CWE/CVSS + an evidence *reference* (path), never raw loot

2. Load Report Template

Loads report/technical-report.md template with sections:

  • Executive Summary
  • Engagement Overview
  • Methodology
  • Finding Summary
  • Detailed Findings
  • Attack Narrative
  • Risk Matrix
  • Remediation Roadmap
  • Appendices

3. Report Population

Executive Summary (report/executive-summary.md):

  • Engagement purpose and scope (1-2 sentences)
  • Overall risk posture assessment
  • Critical finding count and themes
  • Key recommendations (top 3-5)
  • Written for non-technical stakeholders

Engagement Overview:

  • Client and engagement dates
  • Scope and methodology
  • Tools and techniques used
  • Team members (if applicable)

Finding Summary Table:

| # | Finding | Severity | CWE | CVSS | Status |
|---|---------|----------|-----|------|--------|
| 1 | Nginx RCE | Critical | CWE-120 | 9.8 | Confirmed |
| 2 | Hard-coded Creds | Critical | CWE-798 | 9.1 | Confirmed |
| ... | ... | ... | ... | ... | ... |

Detailed Findings: For each finding:

  • Title and severity
  • Description and technical details
  • Exploitation procedure (step-by-step)
  • Evidence (screenshots, command output)
  • Business impact
  • Remediation guidance (specific, actionable)
  • References (CVE, CWE, vendor advisory)

Read the full file on GitHub · 247 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 247 lines · 11 tokens per session scan A 388f8c6dffc0

Subscribe to this mod's changes

engage.report is a command published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 23d ago), licensed MIT. It adds 11 tokens to every session and 1,785 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude CodeSecurityWriting and docsclaude-code-skillsexploit-developmentoffensive-securityredteam
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