greedy-token 0.2.2

Route dev tasks through tool → python → ollama → RAG before escalating to Claude, ChatGPT, Codex, Cursor, etc.

greedy-token

Route dev tasks through tool → python → ollama → RAG before escalating to Claude / ChatGPT / Codex / Cursor / etc.

Your task  →  greedy-token  →  rg/jq | scripts | Ollama | docs/rag | Cursor

Cursor vs greedy-token (token comparison)

Measured with greedy-token audit-context and greedy-token estimate against zero-design-system (tiktoken cl100k_base; order of magnitude, not API billing).

Cursor context overhead (every new chat)

Context	Tokens	Charged when
Always-on rules (.cursor/rules/*.mdc)	2,524	every chat
Skills on disk (.cursor/skills/*/SKILL.md)	26,386	if agent loads skill
Sampled docs (CONTEXT.md, migration-prompts.md)	3,349	if referenced
Sampled set total	32,259	full agent context
Naive agent baseline (rules + 6k overhead + task)	~8,530	default Cursor path

Rules ≥ 1024 tokens → stable prefix is cache-friendly for Claude API prompt caching.

Task routing: naive Cursor vs greedy-token

Task	Naive Cursor	greedy-token route	Est. tokens	Saved vs Cursor	Savings	Command
какой -D flag для baseUrl в e2e config	~8,534	rag (95%)	1,810	~6,724	~79%	greedy-token rag "baseUrl -D flag"
ADR 002 baseUrl pattern	~8,530	rag (61%)	1,806	~6,724	~79%	greedy-token rag "ADR 002 baseUrl"
find baseUrl in e2e properties	~8,532	tool (59%)	0	~8,532	~100%	greedy-token run "…" --execute → rg
sync phase-manifest и skills-map	~8,532	python (65%)	0	~8,532	~100%	greedy-token scripts --run check-meta-sync --execute
rsync template-project в monorepo	~8,533	python (60%)	0	~8,533	~100%	dry-run script; run manually
batch inventory template-project	~8,532	ollama (66%)	0 cloud	~8,532	~100% cloud	scripts/ollama/batch-inventory.sh (local LLM)
refactor header layout and wire nav links	~8,535	cursor (82%)	8,535	0	—	new Cursor chat + skill from docs/skills-map.md

Tier order: tool (rg/jq) → python → ollama → rag → cursor — first match wins. Ollama tier is skipped when unavailable.

Takeaway: lookup / search / sync / bulk tasks save ~6.7k–8.5k tokens per request; wiring and architecture correctly stay on Cursor.

greedy-token audit-context                    # your workspace overhead
greedy-token estimate "your task here"        # route + savings before opening a chat

Install once, point at your workspace root, route every task through the cheapest tier that can handle it.

Install

pip install greedy-token
# or editable: pip install -e .
# or from git: pip install git+https://github.com/svasenkov/greedy-token.git

tiktoken (exact BPE counts via cl100k_base) is a required dependency. If install fails on an unsupported platform, use a Python version with a prebuilt tiktoken wheel or install from source with a Rust toolchain.

PyPI publish (maintainer)

1. Create project greedy-token on pypi.org
2. Add trusted publisher: Owner svasenkov, repo greedy-token, workflow publish.yml
3. Publish: GitHub → Releases → re-run workflow or new tag

Workspace root

greedy-token runs against a project directory (monorepo, app repo, etc.):

export GREEDY_TOKEN_ROOT=/path/to/your-workspace

Auto-detect works when the workspace has docs/phase-manifest.json and scripts/check-meta-sync.sh (e.g. zero-design-system). Otherwise set GREEDY_TOKEN_ROOT explicitly.

Commands

Command	Purpose
greedy-token route "…"	Recommend: tool | python | ollama | rag | cursor + scoring
greedy-token estimate "…"	Token-aware estimate: complexity, est_tokens, tier scan
greedy-token run "…" [--execute]	Route + dry-run / read-only execute
greedy-token scripts --list	List workspace script wrappers
greedy-token scripts --run ID [--execute]	Dry-run / execute read-only wrapper
greedy-token audit-context	Size of always-on rules/skills (tokens)
greedy-token tokens PATH…	Count tokens in files/directories
greedy-token rag QUERY	Search chunks in docs/rag/
greedy-token compress	Short prompt version (stdin; --ollama for LLM)

Tier order

tool (rg/jq) → python → ollama → rag → cursor

First matching tier wins. Ollama is optional — if unavailable, the tier is skipped.

Examples

greedy-token route "find baseUrl"
# → tool (rg)

greedy-token estimate "refactor header layout"
# → cursor, complexity=high

greedy-token route "batch inventory template-project"
# → ollama

greedy-token route "sync phase-manifest and skills-map"
# → python

greedy-token route "ADR 002 baseUrl pattern"
# → rag

Environment

Var	Default
GREEDY_TOKEN_ROOT	auto-detect or required
OLLAMA_URL	http://localhost:11434
OLLAMA_MODEL	qwen2.5-coder:14b

--execute

Read-only only: rg, jq, check-meta-sync.sh. Rsync/migrate/ollama — dry-run; run manually.

Route config

src/greedy_token/config/routes.yaml — customize patterns and commands for your workspace.

License

MIT