cordon-report

cordon-report is a skill for Claude Code, Codex from iamsecure1920/Cordon-AI. It costs 55 tokens per session (680 once invoked), scanned A, original, MIT.

A Cordon workflow for turning a security-testing engagement into a report that another reviewer can reproduce. It keeps confirmed findings separate from leads that still need manual review.

In plain words
What is it for?
Generating Markdown, CSV, JSON, and task-graph files in the engagement’s reports directory.
Why use it?
It prevents unproven results from being presented as confirmed and records the scope, methods, tools, costs, evidence, and audit trail behind the work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

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/iamsecure1920/cordon-ai/cordon-report
Any agent
npx skills add iamsecure1920/Cordon-AI --skill cordon-report
Clone the repo
git clone --depth 1 https://github.com/iamsecure1920/Cordon-AI

Made for: Claude Code, Codex.

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 cordon-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/iamsecure1920/cordon-ai/cordon-report.svg)](https://agentmods.dev/skills/iamsecure1920/cordon-ai/cordon-report)
Your own site
<a href="https://agentmods.dev/skills/iamsecure1920/cordon-ai/cordon-report"><img src="https://agentmods.dev/badge/skills/iamsecure1920/cordon-ai/cordon-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 680 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 $0.00055 $0.00680
Opus 5 $0.00028 $0.00340
Sonnet 5 $0.00011 $0.00136
Haiku 4.5 $0.00006 $0.00068

Measured 5d ago against content hash c43918e24c01, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

cordon-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 5d 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.

skills/cordon-report/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cordon reporting

findings_list(status="confirmed")
findings_list(status="needs_manual_review")
report_generate()

Writes Report.md, Report.csv, findings.json, and taskgraph.mmd into the engagement's reports/ directory, alongside evidence/ and audit.jsonl.

The structure, and why

Confirmed findings — each with a reproduction a triager can run without asking you a question. If a finding needs a follow-up email to reproduce, it is not finished.

Needs manual review — a separate section, explicitly labelled unproven. Never blend these into the confirmed section to make the report look fuller. A triager who finds one unproven item among your confirmed ones will re-check all of them, and you will have spent credibility that took months to build.

Scope, methodology, tool inventory, cost — how a reader checks your work. The task graph shows why each step happened; the audit log records every request, including the refusals.

Before you hand it over

Read Report.md yourself and check:

  1. Every confirmed finding reproduces from the document alone.
  2. Nothing unproven leaked into the confirmed section.
  3. Severity matches the evidence, not the vulnerability class's potential. An exposed .env with live AWS keys is critical. An exposed .env containing only APP_NAME is not, however much the filename suggests otherwise.
  4. Impact is stated in the target's terms — what an attacker gets — not in terms of the vulnerability's name.
  5. impact_limit_note is present on every PoC, so the program can see exactly how far you went.

Partial reports

If a budget ceiling fired, the report is labelled PARTIAL on its first page and states that coverage is incomplete. Leave that label in place. "We ran out of budget at 60% coverage" is honest and useful; silently shipping a partial report as complete tells the program their surface is clean when you never looked.

Severity honesty

The most common way to lose a program's trust is inflating severity. Some specifics:

Read the full file on GitHub · 73 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. 5d ago First seen · 73 lines · 55 tokens per session scan A c43918e24c01

Subscribe to this mod's changes

cordon-report is a skill published in the GitHub repository iamsecure1920/Cordon-AI (0 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 680 once invoked, about $0.0003 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens