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Model usage — measured AI agent skill | DecimalAI

understudy-ai/model-usage

NewSkillScoreMaintained

Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.

agent-toolingby understudy-ai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/understudy-ai-model-usage/SKILL.md

decimalai skills pull understudy-ai-model-usage --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/understudy-ai-model-usage/SKILL.md https://app.decimal.ai/s/understudy-ai-model-usage/SKILL.md

Raw file: /s/understudy-ai-model-usage/SKILL.md ↗ · machine-readable summary at /s/understudy-ai-model-usage.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 1h ago

Yes — +68% lift-43% tokens0% turns91% of whole cases pass with skill (20/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-02	✗→✓	▲ Improved	-37%	0%
case-04	✗→✓	▲ Improved	-41%	0%
case-05	✗→✓	▲ Improved	-43%	0%
case-08	✗→✓	▲ Improved	-44%	0%
case-12	✗→✓	▲ Improved	-68%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

understudy-ai / understudy

skills/model-usage/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 3 files · scripts

Skill contents

scripts/

references/

SKILL.md

Model usage

Overview

Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude.

TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux.

Quick start

Fetch cost JSON via CodexBar CLI or pass a JSON file.
Use the bundled script to summarize by model.

bash
python {baseDir}/scripts/model_usage.py --provider codex --mode current
python {baseDir}/scripts/model_usage.py --provider codex --mode all
python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --pretty

Current model logic

Uses the most recent daily row with modelBreakdowns.
Picks the model with the highest cost in that row.
Falls back to the last entry in modelsUsed when breakdowns are missing.
Override with --model <name> when you need a specific model.

Inputs

Default: runs codexbar cost --format json --provider <codex|claude>.
File or stdin:

bash
codexbar cost --provider codex --format json > /tmp/cost.json
python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all
cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current

Output

Text (default) or JSON (--format json --pretty).
Values are cost-only per model; tokens are not split by model in CodexBar output.

References

Read references/codexbar-cli.md for CLI flags and cost JSON fields.

Want to see if understudy-ai/model-usage would activate on your phrasing? Try it before you install — no LLM call, no signup.

Other measured skills in the registry, with their headline benchmark lift.