promptmeter 0.2.1

Pre-flight token analyzer and usage tracker for Claude — gas optimization for LLM prompts

Token Tracker

Gas optimization for LLM prompts. A pre-flight analyzer + usage tracker for the Claude API. Catches vague, wasteful, or open-ended prompts before they hit the wire — and suggests a leaner rewrite.

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The idea

Solidity devs use a gas estimator before deploying. LLM devs send blind prompts and wait for the bill.

Token Tracker fixes that. It wraps the Anthropic SDK and adds three layers:

Layer	What it does	Analogy
Token Tracker	Logs every API call — tokens in/out, cost, session	Gas meter
Pre-flight Analyzer	Scores your prompt before sending — flags 8 patterns of waste	Gas estimator + linter
Prompt Optimizer	Rewrites bad prompts into leaner versions, side-by-side	Compiler optimizer

All analysis is offline — zero extra API calls. Storage is local SQLite.

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Demo

Run tt demo yourself to see all features without using any API credits.

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Quickstart

pip install promptmeter
export ANTHROPIC_API_KEY="sk-ant-..."

As a drop-in for the Anthropic SDK

from token_tracker import TrackedClient

client = TrackedClient(session_name="my-app")

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=512,
    messages=[{"role": "user", "content": "List 3 Python web frameworks."}],
)

That's it. Every call is now analyzed, logged, and reportable.

As a standalone CLI

tt analyze "Please could you kindly tell me something about Python"
tt report                # today's usage summary
tt report --week         # last 7 days
tt cost-model            # cost breakdown by model
tt top-waste             # most expensive flagged prompts
tt sessions              # list named sessions
tt demo                  # guided walkthrough — zero API calls

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What the pre-flight catches

8 rules, each with severity and a suggested fix:

Rule	Severity	Detects
VAGUE_INTENT	high	"tell me something about X" — no clear task
OPEN_ENDED_TASK	high	"explain everything about X" — unbounded scope
WALL_OF_TEXT	high	unstructured 500+ token dumps
MISSING_FORMAT	medium	asks for a list without specifying format
MISSING_SCOPE	medium	"explain X" with no length/depth limit
REDUNDANT_CONTEXT	medium	the same sentence twice
FILLER_WORDS	low	"please", "could you kindly", "thank you in advance"
AMBIGUOUS_PRONOUN	low	"fix it" — fix what?

Each rule contributes to a 0–100 efficiency score. Below 60 triggers a warning. Below 40 hard-blocks (in interactive mode).

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Try the walkthrough

tt demo

Runs through every feature with three example prompts (good, bad, terrible) and renders the live dashboard. No API calls, safe to run anytime.

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How it actually saves tokens

Without Token Tracker	With Token Tracker
Vague prompt → Claude asks clarifying questions → 2-3 round trips	Pre-flight catches vagueness → fix once → one round trip
"explain X" → Claude writes 800 words when you needed 100	MISSING_SCOPE flags it → add "in 3 sentences" → 6× cheaper output
Same context pasted in every message	REDUNDANT_CONTEXT warns → use prompt caching → 90% cheaper input
No visibility into spend → no behavior change	Daily report shows cost per session → habits adjust

Conservative real-world reduction: 30–50% of token spend for a developer who acts on warnings.

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Configuration

TrackedClient(
    api_key="sk-ant-...",
    session_name="my-app",      # tag this session in the DB
    analyze=True,               # run pre-flight before every call
    interactive=True,           # prompt user on flagged prompts (s/o/c menu)
    warn_threshold=60,          # show warning when score drops below this
)

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Where data lives

A single SQLite file at ~/.token_tracker/usage.db. Two tables: sessions, usage_records. Open it with DB Browser for SQLite to poke around.

Token Tracker stores a hash of each prompt — never the full text. Your prompts stay in your process.

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Contributing

The lowest-friction first contribution is adding a new rule in token_tracker/analyzer/rules.py — about 10 lines of Python. See CONTRIBUTING.md.

Found a false positive? Open an issue with the prompt and the rule that misfired.

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License

MIT — see LICENSE.