agenticflow-built-in-credits

agenticflow-built-in-credits is a skill for Claude Code, Codex from antongulin/agenticflow-ai-skills. It costs 81 tokens per session (3,879 once invoked), scanned A, original, MIT.

A skill for buying and managing AgenticFlow’s prepaid credits, which pay for runs by its agents and workforces.

In plain words
What is it for?
Use it to check available models and agents, choose a plan, set up payment, buy credits, configure auto-recharge, and review usage by day, user, or agent.
Why use it?
It keeps credit purchases, payment setup, receipts, plans, automatic top-ups, and usage in one workflow. It is intended for people using their existing AgenticFlow balance before adding external API keys.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument; mentions Claude Code; mentions Codex.

Good fit Use it to check available models and agents, choose a plan, set up payment, buy credits, configure auto-recharge, and review usage by day, user, or agent.

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Install with agentmods
npx agentmods add skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits
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.

Any agent
npx skills add antongulin/agenticflow-ai-skills --skill agenticflow-built-in-credits
Clone the repo
git clone --depth 1 https://github.com/antongulin/agenticflow-ai-skills

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 agenticflow-built-in-credits

README.md
[![agentmods](https://agentmods.dev/badge/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits/github.svg)](https://agentmods.dev/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits)
Your own site
<a href="https://agentmods.dev/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits"><img src="https://agentmods.dev/badge/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits/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.

agentmods 80×15 button for agenticflow-built-in-credits

Your own site · 80×15
<a href="https://agentmods.dev/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits"><img src="https://agentmods.dev/badge/skills/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,879 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00081 $0.03879
Opus 5 $0.00041 $0.01939
Sonnet 5 $0.00016 $0.00776
Haiku 4.5 $0.00008 $0.00388

Measured 12d ago against content hash 41a0fc93e8f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

agenticflow-built-in-credits 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 12d 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.

skills/agenticflow-built-in-credits/SKILL.md · 432 lines

How it starts

The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Author: Anton Gulin · Tool: opencode-skill-creator · GitHub: @antongulin · Registry: skills.sh

AgenticFlow: Credits-First Approach

Primary Philosophy: Use your existing account credits with built-in features
Extension Path: BYOK (Bring Your Own Key) only if unsatisfied or explicitly requested
Goal: Spend your available credits first, upgrade only when needed


When NOT to use this skill

If the user explicitly wants to use external API keys (BYOK) like DALL-E, Stable Diffusion, or OpenAI, use agenticflow-mcp skill instead. If they need specific model recommendations or want to compare available models, use agenticflow-llm-models skill. This skill is for users who want to maximize their existing account credits first.

Orient first

af bootstrap --json

From the response, extract:

  • auth.workspace_id — your workspace identifier
  • models[] — available models that consume credits (source of truth, don't hardcode)
  • agents[] — existing agents to avoid duplication
  • _links.workspacesurface this URL to the user right away: "Your workspace is at <_links.workspace> — open it anytime to see what I'm building."

If data_fresh: false in the response, the backend is degraded — do not mutate. Fix auth/network before proceeding.

Discovery & health

af changelog --json           # What's new in the CLI — check for credit changes
af context --json              # AI agent orientation, env vars, invocation guidance
af bootstrap --strict --json   # Health check — exits non-zero if degraded

af bootstrap returns an invocation block telling you the correct CLI binary to use. af bootstrap --strict exits non-zero when the backend is unhealthy, so CI/automation can abort before mutating against a degraded workspace.


Primary Philosophy: Credits-First

Read the full file on GitHub · 432 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. 12d ago First seen · 432 lines · 81 tokens per session scan A 41a0fc93e8f9

Subscribe to this mod's changes

agenticflow-built-in-credits is a skill published in the GitHub repository antongulin/agenticflow-ai-skills (2 stars, last pushed 7d ago), licensed MIT. It adds 81 tokens to every session and 3,879 once invoked, about $0.0004 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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