Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/GlamgarOnDiscord/claude-saas-blueprintnpx agentmods add skills/glamgarondiscord/claude-saas-blueprint/perfWrote 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/glamgarondiscord/claude-saas-blueprint/perf)<a href="https://agentmods.dev/skills/glamgarondiscord/claude-saas-blueprint/perf"><img src="https://agentmods.dev/badge/skills/glamgarondiscord/claude-saas-blueprint/perf/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/glamgarondiscord/claude-saas-blueprint/perf"><img src="https://agentmods.dev/badge/skills/glamgarondiscord/claude-saas-blueprint/perf.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 | $0.00029 | $0.00705 |
| Opus 5 | $0.00015 | $0.00352 |
| Sonnet 5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
perf 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 11d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 1 — Identifier les bottlenecks
Lancer d'abord :
python scripts/project_health.py # vue globale
Web App (Next.js) :
- Server Components vs Client Components inutiles (
"use client"abusif ?) - Re-renders inutiles (props instables, Context trop large)
- N+1 queries (boucle avec query DB à chaque itération)
- Images non optimisées (pas de
next/image) - Composants lourds non lazy-loadés (
dynamic(() => import(...))) - Bundle size : analyser
next buildoutput — alerter si JS client > 500KB - Pas de
cache()sur les Server Components qui refetch les mêmes données
API + DB :
- Index manquants sur FK et champs de recherche fréquents
- Endpoints sans pagination (retournent tout)
- Opérations lentes dans le handler (email, PDF, webhook) → doivent être async
- Connection pooling Supabase : utiliser le pooler (port 6543), pas direct
Background jobs :
- Emails, webhooks, exports PDF, traitements IA → ne JAMAIS bloquer le handler
- Solutions recommandées : Trigger.dev v4 ou Inngest (voir
docs/integrations/ai-tools-2026.md) - Pattern :
POST /api/invoice→ créer job → return 202 → Trigger.dev exécute en fond
Edge :
- Endpoints légers (auth check, redirects) → Edge Runtime (
export const runtime = 'edge') - DB géo-distribuée pour faible latence globale : Turso (SQLite edge) ou Neon (serverless Postgres)
Phase 2 — Quick wins (par ordre d'impact)
- Index DB manquants — impact immédiat, 0 code à changer
CREATE INDEX ON public.invoices (organization_id, created_at DESC); - Pagination sur tous les endpoints de liste (
limit+cursor) - Lazy load composants lourds (éditeurs, charts, maps)
- Cache React Query :
staleTime: 5 * 60 * 1000sur les queries stables - next/image pour toutes les images + formats WebP/AVIF
- Debounce les inputs de recherche (300ms)
- Passer en async les opérations lentes (email → Resend en background)
Phase 3 — Optimisations avancées
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.
- 11d ago First seen · 63 lines · 29 tokens per session scan A db6d139f2fc5
perf is a skill published in the GitHub repository GlamgarOnDiscord/claude-saas-blueprint (2 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 705 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 2026-08-31.
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