final-report

final-report is a skill for Claude Code from PurpleAILAB/Decepticon. It costs 28 tokens per session (4,836 once invoked), scanned A, original, Apache-2.0.

A report-writing workflow for authorised penetration tests. It combines recorded findings, attack paths, activity logs, plans, and engagement context into executive and technical Markdown reports.

In plain words
What is it for?
Use it to create an executive summary, technical report, findings overview, attack-path narrative, detection-gap matrix, and remediation roadmap.
Why use it?
It removes the need to gather results manually and keeps the final report tied to the evidence and status from the engagement.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

About the project

Decepticon is an autonomous red-team agent that coordinates AI agents, security tools, sandboxes, and supporting services for authorized cybersecurity assessments. Security researchers and red teams can run it through its Docker stack, cloud service, command-line interface, or Python SDK, with the catalogue entries representing its available skills.

PurpleAILAB/Decepticon · 5,450 stars · on GitHub · decepticon.red

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/purpleailab/decepticon/final-report
Any agent
npx skills add PurpleAILAB/Decepticon --skill final-report
Clone the repo
git clone --depth 1 https://github.com/PurpleAILAB/Decepticon

Made for: Claude Code.

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 final-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/purpleailab/decepticon/final-report.svg)](https://agentmods.dev/skills/purpleailab/decepticon/final-report)
Your own site
<a href="https://agentmods.dev/skills/purpleailab/decepticon/final-report"><img src="https://agentmods.dev/badge/skills/purpleailab/decepticon/final-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,836 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00028 $0.04836
Opus 5 $0.00014 $0.02418
Sonnet 5 $0.00006 $0.00967
Haiku 4.5 $0.00003 $0.00484

Measured 2d ago against content hash ce07fac6096a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

final-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 2d 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.

packages/decepticon/decepticon/skills/standard/decepticon/final-report/SKILL.md · 518 lines

How it starts

The opening of the file, as written. The whole thing — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Final Engagement Report Generation

Execute when all OPPLAN objectives are in passed or blocked status. Output is two Markdown documents in report/.

Report Generation Workflow

1. Read all findings/*.md              (parse YAML frontmatter for metadata)
2. Read all findings/attack-paths/PATH-*.md
3. Read timeline.jsonl                 (activity log)
4. Read plan/roe.json + plan/conops.json + plan/deconfliction.json  (engagement context)
5. Read plan/opplan.json               (objective status summary)
6. Generate report/executive-summary.md
7. Generate report/technical-report.md
8. Do not create a legacy findings.md summary; summaries belong in report/

Step 1: Initialize Output Directory

bash(command="mkdir -p /workspace/report", description="Create the report output directory")

Step 2: Aggregate Findings

Read every findings/FIND-*.md (canonical name findings/FIND-{NNN}.md; the id frontmatter field matches the filename) and extract frontmatter fields:

for each FIND-NNN.md:
  - id, title, severity, cwe, cvss_score, cvss_vector, vrt,
    target (affected_target), affected_component, phase,
    technique (ATT&CK ID), status, confidence

Sorting rules:

  • Primary: severity (criticalhighmediumlowinformational)
  • Secondary: CVSS score descending
  • De-duplicate findings that appear across multiple objectives (keep highest-severity instance)
  • Flag confidence: unverified findings — they require a disclaimer note in the report

Step 3: Generate Executive Summary

Write to report/executive-summary.md using the template below. Use plain business language throughout — zero technical jargon.


# Executive Summary
## [Engagement Name] — Penetration Test Results

**Prepared for**: [Client Name]
**Engagement Period**: [Start Date] – [End Date]
**Conducted by**: Decepticon Autonomous Red Team
**Classification**: CONFIDENTIAL

---

## Engagement Overview

[2–3 sentences: what was tested, why, what methodology was used.
Example: "Between [dates], [client] engaged the red team to assess the
security posture of [scope]. The assessment simulated a [threat actor
archetype from conops.json] targeting [primary objective from roe.json].
Testing followed the Penetration Testing Execution Standard (PTES) and
MITRE ATT&CK framework."]

---

## Key Metrics

| Metric | Value |
|--------|-------|
| Total Findings | [N] |
| Critical | [N] |
| High | [N] |
| Medium | [N] |
| Low / Informational | [N] |
| Objectives Completed | [N] / [total] |
| Objectives Blocked | [N] / [total] |
| Attack Success Rate | [%] |
| Detection Rate | [%] (findings detected by existing controls) |

---

## Top 3 Critical Findings

### 1. [FIND-ID]: [Title]
[1 paragraph, business impact focus. No CVE numbers, no tool names.
Example: "Attackers with internet access can gain full administrative
control of the company's customer database without any valid credentials.
This would allow theft of all customer records, potential regulatory
fines under GDPR, and complete loss of customer trust."]

### 2. [FIND-ID]: [Title]
[1 paragraph, business impact focus]

### 3. [FIND-ID]: [Title]
[1 paragraph, business impact focus]

*[If fewer than 3 critical findings, use highest-severity findings available.]*

---

## Overall Risk Rating

**[CRITICAL / HIGH / MEDIUM / LOW]**

[2–3 sentences justifying the rating in business terms. Consider: ease of
exploitation, potential business impact, data sensitivity, regulatory exposure.
Example: "The overall risk is rated CRITICAL because an unauthenticated
attacker on the internet achieved domain administrator access within [X]
minutes, with no detection by existing security controls. This represents
a complete failure of the network perimeter and would result in total
compromise of all company systems."]

---

## Strategic Recommendations

1. **[Recommendation 1 — address most critical finding]** — [1 sentence, business language, e.g. "Immediately restrict internet access to the database server and require multi-factor authentication for all administrative accounts."]
2. **[Recommendation 2]** — [1 sentence]
3. **[Recommendation 3 — detection/monitoring gap]** — [1 sentence]
4. **[Recommendation 4 — process/architecture]** — [1 sentence]
5. **[Recommendation 5 — longer-term posture improvement]** — [1 sentence]

---

*Full technical details, evidence, and step-by-step remediation instructions are provided in the Technical Report.*

Read the full file on GitHub · 518 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. 2d ago First seen · 518 lines · 28 tokens per session scan A ce07fac6096a

Subscribe to this mod's changes

final-report is a skill published in the GitHub repository PurpleAILAB/Decepticon (5,450 stars, last pushed 6d ago), licensed Apache-2.0. It adds 28 tokens to every session and 4,836 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 2026-09-03.