fpf

fpf is a skill for Claude Code, Codex from m0n0x41d/claude-code-fpf. It costs 26 tokens per session (2,954 once invoked), scanned A, original, MIT.

A way to reason about difficult problems by breaking them into basic facts, possible solutions, and evidence for choosing between them.

In plain words
What is it for?
Use it for architecture and strategy decisions, comparing alternatives, defining acceptance criteria, and recording the reasons behind a decision.
Why use it?
It helps clarify vague requests, separate assumptions from facts, and make decisions when several people or alternatives are involved.

Skill for Claude CodeCodex

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/m0n0x41d/claude-code-fpf/fpf
Any agent
npx skills add m0n0x41d/claude-code-fpf --skill fpf
Clone the repo
git clone --depth 1 https://github.com/m0n0x41d/claude-code-fpf

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 fpf

README.md
[![agentmods](https://agentmods.dev/badge/skills/m0n0x41d/claude-code-fpf/fpf.svg)](https://agentmods.dev/skills/m0n0x41d/claude-code-fpf/fpf)
Your own site
<a href="https://agentmods.dev/skills/m0n0x41d/claude-code-fpf/fpf"><img src="https://agentmods.dev/badge/skills/m0n0x41d/claude-code-fpf/fpf.svg" alt="Measured on agentmods" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,954 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 $0.00026 $0.02954
Opus 5 $0.00013 $0.01477
Sonnet 5 $0.00005 $0.00591
Haiku 4.5 $0.00003 $0.00295

Measured 4d ago against content hash 2facb4bfc34c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fpf 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 4d 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.

skill/fpf/SKILL.md · 311 lines

How it starts

The opening of the file, as written. The whole thing — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FPF — First Principles Framework

FPF is a systems thinking methodology by Anatoly Levenchuk. This skill gives you its operational core — apply it to reason about problems, solutions, and decisions.


When to invoke

Use this skill when at least one is true:

  • the task is ambiguous or badly framed;
  • the choice is architectural, organizational, strategic, or hard to reverse;
  • multiple stakeholders or viewpoints matter;
  • you need to compare serious alternatives;
  • acceptance is unclear and must be designed;
  • you must separate target system from creator system;
  • overloaded words (process, service, function, quality, done, validated) are causing confusion;
  • you need an ADR/DRR-like rationale, parity plan, evidence pack, or explicit selection policy.

Do not invoke for tiny local edits with explicit acceptance and low blast radius.


Depth calibration

Before starting, assess the scale of the request:

Mode When What to do
Quick Tactical choices, clear trade-offs, ≤2 real options Frame → Variants (table) → Recommendation. 1-2 paragraphs.
Deep Architectural decisions, ambiguous problems, irreversible choices, user explicitly asks for depth Full ADI cycle with evidence records, Pareto analysis, lifecycle stage.

Default is Quick. Escalate to Deep when: the decision is hard to reverse, multiple stakeholders are affected, or the problem framing itself is unclear.


What to do first

Before proposing solutions, state:

  1. Lifecycle stageExplore | Shape | Evidence | Operate
  2. Target system — what must work in operation
  3. Creator system — who builds/changes/operates it
  4. Context — which bounded context defines the meaning of terms and rules
  5. Problem owner — whose problem this is

If these are fuzzy, the task is still under-framed.


Core thinking algorithm

1. Frame the problem BEFORE solving it

The bottleneck is problem quality, not solution speed. Before generating any solution:

Read the full file on GitHub · 311 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. 4d ago First seen · 311 lines · 26 tokens per session scan A 2facb4bfc34c

Subscribe to this mod's changes

fpf is a skill published in the GitHub repository m0n0x41d/claude-code-fpf (11 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 2,954 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.

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