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 skills add Tano73/agent-skills --skill ffpa-analyzergit clone --depth 1 https://github.com/Tano73/agent-skillsWrote 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/tano73/agent-skills/ffpa-analyzer)<a href="https://agentmods.dev/skills/tano73/agent-skills/ffpa-analyzer"><img src="https://agentmods.dev/badge/skills/tano73/agent-skills/ffpa-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tano73/agent-skills/ffpa-analyzer"><img src="https://agentmods.dev/badge/skills/tano73/agent-skills/ffpa-analyzer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00192 | $0.05685 |
| Opus 5 | $0.00096 | $0.02842 |
| Sonnet 5 | $0.00038 | $0.01137 |
| Haiku 4.5 | $0.00019 | $0.00568 |
Grade A, and why
ffpa-analyzer 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 9d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FFPA Analyzer — Analisi Function Point (Metodologia FFPA)
Contesto
Questa skill applica la metodologia Fast Function Points Analysis (FFPA) come descritta nel Manuale Misura Software per Unità di Prodotto (IT) v5.0 — il riferimento normativo ufficiale.
FFPA amplia la metodologia tradizionale IFPUG (Function Point Analysis) integrandola con due modelli aggiuntivi:
- un modello per valutare le Regole di Business implementate negli algoritmi (Classe D)
- un modello per le funzionalità realizzate tramite configurazione di package
L'unità di misura è il FFP (Fast Function Point), sempre di tipo Unadjusted (UFP) nel senso IFPUG: non si applica mai un fattore di aggiustamento (Value Adjustment Factor).
Il conteggio si esegue su oggetti logici riconoscibili dall'utente — non su oggetti fisici o tecnici — ed è indipendente dalla tecnologia di implementazione. È applicabile sia al codice sorgente che alle specifiche, in tutte le fasi del ciclo di vita.
Leggi references/ffpa-weights-v2.md per la tabella completa dei pesi, le soglie di complessità, le regole su cosa si conta, e la mappatura delle colonne del template Excel. Quello è il tuo riferimento normativo operativo.
Tipi di input accettati
- Codice sorgente: entità/modelli → A, controller/API/form → B/C, algoritmi/logiche → D
- Specifiche o documenti funzionali: identifica le funzionalità dal punto di vista dell'utente
- Requisiti utente / User Story: ogni story descrive 1-N processi funzionali
- Descrizione testuale: estrai solo ciò che è percepibile e richiesto dall'utente
Processo di analisi
Step 1 — Raccolta del contesto
Prima di procedere, identifica:
- Cosa fa l'applicazione? (processo di business supportato)
- Chi sono gli utenti? (umani, altre applicazioni, o entrambi — includono tutti i soggetti che interagiscono con il confine funzionale)
- Confine funzionale: cosa è dentro e cosa è fuori. Le comunicazioni tra sotto-componenti dello stesso confine non si contano come flussi I/O.
- Tipo di conteggio: New Development, Enhancement (ADD/CHG/DEL), o Application totale
- Tipo di contratto: in FP o in UdA (determina il template Excel da usare)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 310 lines · 192 tokens per session scan A 9c0e36df8878
ffpa-analyzer is a skill published in the GitHub repository Tano73/agent-skills (2 stars, last pushed 5d ago), licensed MIT. It adds 192 tokens to every session and 5,685 once invoked, about $0.0010 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.
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