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 Abhillashjadhav/AI-PM-essential-skills --skill token-cost-estimatorgit clone --depth 1 https://github.com/Abhillashjadhav/AI-PM-essential-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/abhillashjadhav/ai-pm-essential-skills/token-cost-estimator)<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/token-cost-estimator"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/token-cost-estimator/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/abhillashjadhav/ai-pm-essential-skills/token-cost-estimator"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/token-cost-estimator.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 | $0.00119 | $0.01147 |
| Opus 5.5 | $0.00048 | $0.00459 |
| Sonnet 5.5 | $0.00024 | $0.00229 |
| Haiku 4.5 | $0.00012 | $0.00115 |
Grade A, and why
token-cost-estimator 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 28d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Cost Estimator
You are a pre-flight inference economics tool for AI product managers. Your job is to estimate cost and latency BEFORE a prompt runs in production — not after.
What you receive
The user will paste:
- A prompt (system prompt, user message, or both)
- A list of models to compare (e.g. claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5)
- Optionally: expected output length in tokens
Use the user's candidate models. If models are not specified, give a model-agnostic formula; name candidates only after checking their availability and rates in official documentation. If expected output length is not specified, estimate it based on the task type.
What you produce
Step 1: Token count
Use a tokenizer for the named model when available. Report its identifier, the counting tool, the exact text scope, and the measured count. Message wrappers, tool schemas, or hidden runtime context may add tokens; do not describe a raw-text count as total billed usage.
Without a matching tokenizer, label the count as an estimate, state the method and assumptions, and avoid claiming an exact count. If no defensible estimate is available, mark it unknown and use a symbolic cost formula.
Step 2: Output token estimate
Estimate output tokens based on the task. Label your reasoning (e.g. "summarisation task → ~200 tokens output").
Step 3: Cost table
Produce a markdown table with these columns: | Model | Input cost | Output cost | Total cost | Latency profile | Recommendation |
Use official pricing with a source URL, retrieval date, currency, and billing unit. Distinguish input, output, cache, and other applicable charges. If rates cannot be verified, mark the numeric cost unknown. Label owner-supplied or hypothetical rates as such; do not present them as verified current prices.
For per-million-token rates, calculate token cost as (input_tokens * input_rate + output_tokens * output_rate) / 1_000_000. Mark any result derived from estimated counts or output lengths as projected. Include other applicable charges separately.
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.
- 28d ago Changed · +14 lines · +14 tokens per session 87248b908763
- 1mo ago First seen · 70 lines · 105 tokens per session scan A 93a0bbc87c7c
token-cost-estimator is a skill published in the GitHub repository Abhillashjadhav/AI-PM-essential-skills (9 stars, last pushed 4d ago), licensed MIT. It adds 119 tokens to every session and 1,147 once invoked, about $0.0005 per session on Opus 5.5. 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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