
Braintrust Analyze
- 465 installs
- 3.9k repo stars
- Updated January 26, 2026
- parcadei/continuous-claude-v3
braintrust-analyze is an agent evaluation skill that runs structured Braintrust evals of Claude agent outputs against golden datasets and scoring functions for developers who need measured quality gates on prompt and too
About
braintrust-analyze is a continuous-claude-v3 agent skill for structured evaluation of Claude agent outputs inside Braintrust. It compares responses against golden datasets and custom scoring functions to quantify regressions before deployment. Catalog metadata on skills.sh lists 435 installs and rank 56 for this skill slug. Engineering teams reach for braintrust-analyze when agent prompts, tools, or routing logic change and subjective spot checks are insufficient. The skill fits ship-phase testing for LLM agents where repeatable eval harnesses must run in CI or pre-release workflows.
- Runs Braintrust experiments directly from Cursor or Claude Code
- Compares multiple Claude prompts or tool configurations side-by-side
- Calculates custom scores and aggregates results automatically
- Stores evaluation traces for later analysis and iteration
- Enables regression testing as your agent evolves
Braintrust Analyze by the numbers
- 465 all-time installs (skills.sh)
- +2 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #1,858 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill braintrust-analyzeAdd your badge
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| Installs | 465 |
|---|---|
| repo stars | ★ 3.9k |
| Last updated | January 26, 2026 |
| Repository | parcadei/continuous-claude-v3 ↗ |
How do you evaluate Claude agent outputs with Braintrust?
Run structured evaluations of Claude agent outputs against golden datasets and scoring functions inside Braintrust.
Who is it for?
Agent engineers maintaining Claude agents who need Braintrust eval runs with golden datasets before merging prompt changes.
Skip if: Developers without Braintrust setup or who only need unit tests on deterministic functions should skip braintrust-analyze.
When should I use this skill?
A developer asks to evaluate agent outputs, run Braintrust evals, or score Claude responses against a golden dataset.
What you get
Braintrust eval run results, scored outputs, and regression signals against golden datasets.
- Eval run results
- Scored agent output reports
By the numbers
- Listed with 435 installs on skills.sh
- Ranked 56 in skills.sh catalog metadata
Files
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-trendsOptions
--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
Examples
Quick Retrospective
# What happened in my last session?
uv run python -m runtime.harness scripts/braintrust_analyze.py --last-sessionOutput:
## Session Analysis
**ID:** `92940b91...`
**Started:** 2025-12-24T01:31:05Z
**Spans:** 14
### Tool Usage
- Read: 4
- Bash: 2
- Edit: 2
...Find Loops
uv run python -m runtime.harness scripts/braintrust_analyze.py --detect-loopsWeekly Review
uv run python -m runtime.harness scripts/braintrust_analyze.py --weekly-summaryRequirements
- BRAINTRUST_API_KEY in ~/.claude/.env or project .env
- Braintrust tracing enabled (via braintrust-claude-plugin)
---
name: braintrust-analyze
description: Analyze Claude Code sessions via Braintrust
---
# 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:
```bash
# 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
## Examples
### Quick Retrospective
```bash
# What happened in my last session?
uv run python -m runtime.harness scripts/braintrust_analyze.py --last-session
```
Output:
```
## Session Analysis
**ID:** `92940b91...`
**Started:** 2025-12-24T01:31:05Z
**Spans:** 14
### Tool Usage
- Read: 4
- Bash: 2
- Edit: 2
...
```
### Find Loops
```bash
uv run python -m runtime.harness scripts/braintrust_analyze.py --detect-loops
```
### Weekly Review
```bash
uv run python -m runtime.harness scripts/braintrust_analyze.py --weekly-summary
```
## Requirements
- BRAINTRUST_API_KEY in ~/.claude/.env or project .env
- Braintrust tracing enabled (via braintrust-claude-plugin)
Option: braintrust-analyze
I (Initiation)
activate: [retrospective, debug_failure, weekly_review, skill_opportunity, token_analysis] skip: [active_implementation, planning_phase]
Y (Observation Space)
| signal | source | interpretation |
|---|---|---|
| session_id | Braintrust API | target session |
| spans | Braintrust traces | tool/agent/skill calls |
| token_usage | span metadata | consumption patterns |
U (Action Space)
primary: [Bash] forbidden: [Write, Edit]
pi (Policy)
P0: Mode Selection
eta |-> last_session if no_params
eta |-> specific_mode if param_provided| action | Q | why | mitigation |
|---|---|---|---|
| guess_session | -inf | Wrong data analyzed | use --last-session |
| skip_api_check | -inf | BRAINTRUST_API_KEY may be missing | check env first |
P1: Execute Analysis
eta |-> run_script(mode) via Bash
mode in {--last-session, --sessions N, --agent-stats, --skill-stats, --detect-loops, --replay ID, --weekly-summary, --token-trends}| action | Q | why |
|---|---|---|
| last_session | HIGH | Most common use case |
| detect_loops | HIGH | Finds inefficiencies |
| agent_stats | MED | Weekly review |
Command Reference
uv run python -m runtime.harness scripts/braintrust_analyze.py [OPTIONS]beta (Termination)
beta(eta) = 1.0 if analysis_displayed OR api_errorsuccess: [patterns_identified, loops_found, summary_generated] failure: [api_key_missing, no_sessions_found]
Output Schema
sections: [session_id, tool_breakdown, agent_spawns, skill_activations, recommendations]Invariants
inv_1: never write files (read-only analysis)
inv_2: always check BRAINTRUST_API_KEY existsRelated skills
How it compares
Use braintrust-analyze for dataset-driven agent eval loops instead of manual prompt spot checking alone.
FAQ
What does braintrust-analyze evaluate?
braintrust-analyze runs structured evaluations of Claude agent outputs inside Braintrust, comparing results against golden datasets and custom scoring functions to surface quality regressions.
How popular is braintrust-analyze on skills.sh?
skills.sh catalog metadata for braintrust-analyze reports 435 installs and rank 56 from the parcadei/continuous-claude-v3 source repository.