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 vibeeval/vibecosystem --skill braintrust-analyzegit clone --depth 1 https://github.com/vibeeval/vibecosystemWrote 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/vibeeval/vibecosystem/braintrust-analyze)<a href="https://agentmods.dev/skills/vibeeval/vibecosystem/braintrust-analyze"><img src="https://agentmods.dev/badge/skills/vibeeval/vibecosystem/braintrust-analyze/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/vibeeval/vibecosystem/braintrust-analyze"><img src="https://agentmods.dev/badge/skills/vibeeval/vibecosystem/braintrust-analyze.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 | $0.00012 | $0.00671 |
| Opus 5 | $0.00006 | $0.00336 |
| Sonnet 5 | $0.00002 | $0.00134 |
| Haiku 4.5 | $0.00001 | $0.00067 |
Grade A, and why
braintrust-analyze 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 5d 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.
This is a copy
100% identical to braintrust-analyze — 212 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Braintrust Analysis
Analyze your Claude Code sessions for patterns, issues, and insights using Braintrust tracing data.
When to Use
- After completing a complex task (retrospective)
- When debugging why something failed
- Weekly review of productivity patterns
- Finding opportunities to create new skills
- Understanding token usage trends
Commands
Run from the project directory:
# Analyze last session - summary with tool/agent/skill breakdown
uv run python -m runtime.harness scripts/braintrust_analyze.py --last-session
# List recent sessions
uv run python -m runtime.harness scripts/braintrust_analyze.py --sessions 5
# Agent usage statistics (last 7 days)
uv run python -m runtime.harness scripts/braintrust_analyze.py --agent-stats
# Skill usage statistics (last 7 days)
uv run python -m runtime.harness scripts/braintrust_analyze.py --skill-stats
# Detect loops - find repeated tool patterns (>5 same tool calls)
uv run python -m runtime.harness scripts/braintrust_analyze.py --detect-loops
# Replay specific session - show full sequence of actions
uv run python -m runtime.harness scripts/braintrust_analyze.py --replay <session-id>
# Weekly summary - daily activity breakdown
uv run python -m runtime.harness scripts/braintrust_analyze.py --weekly-summary
# Token trends - usage over time
uv run python -m runtime.harness scripts/braintrust_analyze.py --token-trends
Options
--project NAME- Braintrust project name (default: agentica)
What You'll Learn
Session Analysis
- Tool usage breakdown
- Agent spawns (plan-agent, debug-agent, etc.)
- Skill activations (/commit, /research, etc.)
- Token consumption estimates
Loop Detection
Find sessions where the same tool was called repeatedly, which may indicate:
- Stuck in a search loop
- Inefficient approach
- Opportunity for better tooling
Usage Patterns
- Which agents you use most
- Which skills get activated
- Daily/weekly activity trends
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.
- 5d ago First seen · 107 lines · 12 tokens per session scan A b61a5908399b
braintrust-analyze is a skill published in the GitHub repository vibeeval/vibecosystem (529 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 671 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to braintrust-analyze, differing in 212 lines, and is treated as a copy.
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