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.
npx agentmods add skills/m0n0x41d/claude-code-fpf/fpfnpx skills add m0n0x41d/claude-code-fpf --skill fpfgit clone --depth 1 https://github.com/m0n0x41d/claude-code-fpfWrote 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.
[](https://agentmods.dev/skills/m0n0x41d/claude-code-fpf/fpf)<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>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.
| Model | Per session | Once 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 |
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.
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:
- Lifecycle stage —
Explore | Shape | Evidence | Operate - Target system — what must work in operation
- Creator system — who builds/changes/operates it
- Context — which bounded context defines the meaning of terms and rules
- 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:
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.
- 4d ago First seen · 311 lines · 26 tokens per session scan A 2facb4bfc34c
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…