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Finding Discipline

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hypnguyen1209
finding-discipline

Use when about to record, claim, rate the severity of, or report any security finding — before marking anything [CONFIRMED] or writing it into the report

Overview

Publisherhypnguyen1209
Repositoryoffensive-claude
Skill namefinding-discipline
Stars
396
Forks
71
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 hypnguyen1209 on GitHub. Read the source before you install it.

Installation

Install the Finding Discipline 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/hypnguyen1209/offensive-claude/tree/main/skills/finding-discipline
  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/hypnguyen1209/offensive-claude.git /tmp/offensive-claude
mkdir -p .claude/skills
cp -r /tmp/offensive-claude/skills/finding-discipline .claude/skills/finding-discipline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Discipline 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 Finding Discipline 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 Finding Discipline 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.

Finding Discipline

Overview

The Iron Law: No [CONFIRMED] without proof. A finding is a claim about real, demonstrated impact — not a status code, a reflected string, or a hunch. This is the offensive equivalent of test-driven development: the proof is the test, and the finding does not exist until it passes.

Violating the letter of this rule is violating its spirit.

The three tiers

  • [CONFIRMED] — impact demonstrated AND grounded in an evidence artifact that exists on disk.
  • [POSSIBLE] — reachable but the class proof bar is not yet met. Keep digging; do not report as real.
  • [INFO] — true but no security impact at the claimed severity.

The proof bar (per class)

A status code is not impact. See skills/references/finding-evidence-standards.md: SSRF needs the internal response; IDOR needs another principal's data; RCE needs command output; XSS needs script execution; open redirect needs an external destination.

Required mechanical gate

Before any finding is recorded or reported:

  1. Set the structured proof signal and run python skills/vulnerability-analysis/scripts/validate_findings.py --findings f.json --evidence ./evidence — it rejects ungrounded findings and per-class false positives.
  2. Pass it through the REQUIRED finding-validator agent (PASS / KILL / DOWNGRADE).
  3. Only a [CONFIRMED] survivor goes in the report. Severity must match what was demonstrated (CVSS honest).

See skills/references/finding-validation-runtime.md for the 7-question gate.

Read-first, never name-guess

If a function calls another, read the callee — do not infer what it does from its name. sanitize(), is_safe(), validate_input() are exactly where vulnerabilities hide; a name is a claim by the author, not evidence. The same applies to a config key, a decorator, or a CVE title: open the thing before you reason about it. An unread dependency in a data-flow claim is a hole.

Quote-grounded confidence tiers

Every claim in a finding carries a confidence grounded in what you can quote, not in how it feels:

  • High — a direct quote from the artifact (the exact source line, the response body, the decompiled instruction) supports the claim.
  • Medium — an explicitly stated assumption bridges a gap you could not directly observe. State the assumption; do not hide it.
  • Low — an inference you are flagging as unverified. Acceptable to record, never to report as fact.

This is orthogonal to the [CONFIRMED]/[POSSIBLE]/[INFO] tier (which is about impact); confidence is about how well-grounded the claim is. A High-confidence claim of a Low-impact bug is still Low.

Red Flags — STOP, downgrade to [POSSIBLE]

  • "I'm sure it's exploitable" (but haven't shown impact)
  • "The payload reflected, so it's XSS" / "I got a 200, so it works"
  • "The DNS callback fired" (SSRF with no internal response)
  • "I changed the id and got data" (but it was my own — self-IDOR)
  • "It probably works on a real target" (default-deployment unproven)

All of these mean: it is [POSSIBLE], not [CONFIRMED]. Get the evidence.

Rationalizations

ExcuseReality
"Pattern-matched, clearly vulnerable"Pattern ≠ exploit. Demonstrate impact or it's POSSIBLE.
"I'll attach evidence later"No artifact on disk = ungrounded = REJECTED now.
"Blind RCE counts"No output / no OOB confirmation = POSSIBLE, not CONFIRMED.
"Severity is obviously Critical"CVSS must reflect demonstrated impact, not the worst case.
"The validator is overkill here"Untested findings are how false positives ship. Run it.
"The function is called sanitize, so it's safe"A name is the author's claim, not proof. Read the callee.
"The solver timed out, so it's not exploitable"A tool limit is feasibility:null (manual), never false.

A short list of CONFIRMED findings beats a long list of POSSIBLEs. Killing a false positive is success.

Frequently asked questions

What does the Finding Discipline AI skill do?

Use when about to record, claim, rate the severity of, or report any security finding — before marking anything [CONFIRMED] or writing it into the report

Why use Finding Discipline on TypingMind?

Because you install it once and use it with any model. Finding Discipline 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 Finding Discipline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hypnguyen1209/offensive-claude/tree/main/skills/finding-discipline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finding Discipline?

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 Finding Discipline?

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

Is the Finding Discipline AI skill free?

Yes. It is published on GitHub by hypnguyen1209 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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