gaia-research/skill-cost

cost — Multi-Harness Token Cost Report

Reads the JSONL session logs written by AI agent harnesses on this machine and reports accurate token usage and USD cost per session. Prices come from BerriAI/litellm's canonical model_prices_and_context_window.json catalog and auto-refresh weekly.

Supported harnesses

Auto-detected from ~:

Harness	JSONL root
pi	~/.pi/agent/sessions/**/*.jsonl
Claude Code	~/.claude/projects/**/*.jsonl
OpenAI Codex	~/.codex/sessions/**/*.jsonl
opencode	~/.local/share/opencode/**/*.jsonl

Adding a new harness = one small parse_* function in cost.py.

Run

# [default] the currently active session (newest-mtime JSONL)
python3 cost.py

# Latest session per harness in current cwd
python3 cost.py --latest

# Every session across every harness
python3 cost.py --all --list

# Today only, per-model breakdown
python3 cost.py --today --by-model

# Since a date, one harness only
python3 cost.py --since 2026-07-01 --harness claude-code

# Filter by working directory or session id substring
python3 cost.py --cwd "$PWD"
python3 cost.py --session 019f4e66

# Machine-readable
python3 cost.py --json

# Prices
python3 cost.py --refresh-prices    # force a refresh now
python3 cost.py --offline           # skip any network access

Environment:

• SKILL_COST_MAX_AGE_DAYS=N — refresh threshold (default 7)
• SKILL_COST_NO_AUTO_REFRESH=1 — disable auto-refresh entirely

Pricing model

For each (model, session) bucket, cost = sum of

input_tokens       * input_price
+ output_tokens      * output_price
+ cache_read_tokens  * cache_read_price       (fallback: 0.10 * input_price)
+ cache_write_tokens * cache_creation_price   (fallback: 1.25 * input_price)

Rates come from LiteLLM's catalog per token; fallbacks match Anthropic's public rate card for ephemeral-5m cache.

Presenting results to the user

When invoked via /cost:

1. Run the script with the arguments the user supplied (default to none).
2. Show the script's stdout verbatim in a fenced block.
3. Summarize the grand-total cost and which harness/session it covers.
4. If the output lists any unpriced models, suggest python3 cost.py --refresh-prices.
5. Do not fabricate numbers — always rely on the script's output.

Files

• cost.py — parser + pricer, stdlib only
• prices.json — pruned LiteLLM catalog (auto-refreshed weekly by CI and on-demand at runtime). Includes a _meta entry with source/license/fetch-time.

Attribution

Prices are sourced from BerriAI/litellm's model_prices_and_context_window.json catalog (MIT). We prune it to the fields we need and ship the pruned copy as prices.json. Every catalog refresh writes a _meta block with the source URL, license, and fetch timestamp so the provenance always travels with the data. If a model is missing or mispriced, the fix belongs upstream in LiteLLM — that corrects every tool that reads the catalog.

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