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 antongulin/agenticflow-ai-skills --skill agenticflow-built-in-creditsgit clone --depth 1 https://github.com/antongulin/agenticflow-ai-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/antongulin/agenticflow-ai-skills/agenticflow-built-in-credits)<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.
<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>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 | $0.00081 | $0.03879 |
| Opus 5 | $0.00041 | $0.01939 |
| Sonnet 5 | $0.00016 | $0.00776 |
| Haiku 4.5 | $0.00008 | $0.00388 |
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.
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 identifiermodels[]— available models that consume credits (source of truth, don't hardcode)agents[]— existing agents to avoid duplication_links.workspace— surface 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
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.
- 12d ago First seen · 432 lines · 81 tokens per session scan A 41a0fc93e8f9
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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