taiga-api — taiga-api python setup taiga-api, config, community, taiga-api python setup, ide skills, taiga-api documentation, taiga-api query examples, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Python-based AI Agents needing seamless integration with the Taiga evaluation platform API for job results and transcripts analysis. Taiga-api is a Python-based interface for querying the Taiga evaluation platform API, enabling developers to retrieve job results, transcripts, and problem runs.

Features

Queries the hosted Taiga evaluation platform API using Python
Loads cookies from a .env file using a Python helper function
Retrieves job results, transcripts, and problem runs from the Taiga API
Avoids shell env var and pipe bugs that strip cookie values
Utilizes a Python-based approach for reliable API requests

# Core Topics

atondwal atondwal
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The taiga-api skill by atondwal is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for taiga-api python setup, taiga-api documentation, taiga-api query examples.

Ideal Agent Persona

Perfect for Python-based AI Agents needing seamless integration with the Taiga evaluation platform API for job results and transcripts analysis.

Core Value

Empowers agents to query the Taiga API using Python, providing a helper function to load cookies and ensuring secure API requests, while leveraging the Taiga-API for job results, transcripts, and problem runs retrieval.

Capabilities Granted for taiga-api

Querying job results from the Taiga evaluation platform
Retrieving transcripts and problem runs using the Taiga API
Automating Taiga API requests with Python

! Prerequisites & Limits

  • Requires Python environment
  • Must use Python for API requests due to shell limitations with env vars and pipes
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

taiga-api

Install taiga-api, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

Taiga API

Query the hosted Taiga evaluation platform API for job results, transcripts, and problem runs.

IMPORTANT: Use Python, Not Shell

Always use Python for Taiga API requests. Shell has env var + pipe bugs that strip cookie values.

Python helper to load cookie:

python
1def get_cookie(): 2 with open('/home/atondwal/dmodel/ant/taiga-worktree/.env') as f: 3 for line in f: 4 if line.startswith('TAIGA_IAP_COOKIE='): 5 return line.split('=', 1)[1].strip().strip('"')

IMPORTANT: Always Use Opus 4.5

When submitting jobs, ALWAYS use claude-opus-4-5-20251101 as the model. Never use Sonnet or other models unless explicitly requested.

Authentication

Cookie stored in ~/dmodel/ant/taiga-worktree/.env. Uses __Host- prefix (session-only). If auth fails, ask user to refresh from browser DevTools → Network → copy Cookie header.

Making Requests

python
1import urllib.request, json 2 3def taiga_get(endpoint): 4 cookie = get_cookie() # see helper above 5 req = urllib.request.Request(f"https://taiga.ant.dev/api{endpoint}") 6 req.add_header('Cookie', cookie) 7 return json.loads(urllib.request.urlopen(req).read()) 8 9# Example: get job problems 10data = taiga_get(f"/jobs/{job_id}/problems")

API Reference

Full docs at: https://taiga.ant.dev/api/docs

Jobs (Most Common)

EndpointMethodPurpose
/jobsGETList all jobs
/jobs?environment_id={id}GETList jobs for environment
/jobs/{job_id}GETGet job details
/jobs/{job_id}/problemsGETGet problem results (passrates, scores)
/jobs/{job_id}/problems/streamGETStream problem results
/jobs/{job_id}/error-summaryGETGet error summary
/jobsPOSTCreate job with problems
/cancel-job/{job_id}POSTCancel running job
/resubmit-problem/{job_id}/{problem_id}POSTResubmit specific problem

Transcripts

EndpointMethodPurpose
/transcript/{problem_run_id}GETGet full transcript
/transcript/stream/{problem_run_id}GETStream transcript

Problem Runs

EndpointMethodPurpose
/problem_runs/{problem_id}GETList runs for problem
/problem-runs/{id}/container-logsGETGet container logs
/problem-runs/{id}/mcp-server-logsGETGet MCP server logs
/problem-runs/{id}/download-outputGETDownload output directory

Environments

EndpointMethodPurpose
/environmentsGETList environments
/environments/{id}GETGet environment details
/environments?skip=0&limit=100GETPaginated list

Problems

EndpointMethodPurpose
/problems/{problem_id}/attemptsGETGet problem attempts
/problems/versions/{version_id}GETGet problem version
/problems/versions/{version_id}/runPOSTRun problem version
/problem-crudGETList all problems
/problem-crud/stats/pass-ratesPOSTGet pass rate stats

Docker Images

EndpointMethodPurpose
/docker-imagesGETList docker images
/docker-images/{id}/downloadGETDownload image source

Common Workflows

Get Passrates for a Job

python
1job_id = "3c300cca-707a-4e92-ac71-5688165f9ae1" # from URL ?id= param 2data = taiga_get(f"/jobs/{job_id}/problems") 3for r in data: 4 print(f"{r['problem_id']}: {r['final_score']}")

Aggregate Passrates

python
1from collections import defaultdict 2 3job_id = "YOUR_JOB_ID" 4data = taiga_get(f"/jobs/{job_id}/problems") 5 6problems = defaultdict(list) 7for r in data: 8 problems[r['problem_id']].append(r['final_score']) 9 10total_pass = total_runs = 0 11for pid, scores in sorted(problems.items()): 12 passed = sum(1 for s in scores if s == 1.0) 13 total = len(scores) 14 total_pass += passed 15 total_runs += total 16 print(f"{pid}: {passed}/{total} ({100*passed/total:.0f}%)") 17 18print(f"\nOverall: {total_pass}/{total_runs} ({100*total_pass/total_runs:.1f}%)")

Get Transcript

python
1problem_run_id = "118ed21a-9864-4c8c-b34b-d92428f1c22a" 2transcript = taiga_get(f"/transcript/{problem_run_id}")

List Jobs for Environment

python
1env_id = "8e646c11-1461-44a4-9e8d-e3800a02ba07" 2jobs = taiga_get(f"/jobs?environment_id={env_id}") 3for j in jobs: 4 print(f"{j['id']}: {j['status']}")

Check Job Status

python
1job = taiga_get(f"/jobs/{job_id}") 2print(f"Status: {job['status']}, Completed: {job.get('completed_count')}")

Create a Job

python
1import urllib.request, json 2 3with open('problems-metadata.json') as f: 4 problems = json.load(f) 5 6payload = { 7 "name": "my-job-name", 8 "problems_metadata": problems, 9 "n_attempts_per_problem": 10, 10 "api_model_name": "claude-opus-4-5-20251101" # ALWAYS use Opus 4.5 11} 12 13cookie = get_cookie() 14req = urllib.request.Request( 15 "https://taiga.ant.dev/api/jobs", 16 data=json.dumps(payload).encode(), 17 headers={"Cookie": cookie, "Content-Type": "application/json"} 18) 19resp = json.loads(urllib.request.urlopen(req).read()) 20print(f"Job ID: {resp.get('job_id')}")

Response Schemas

Problem Run

json
1{ 2 "id": "118ed21a-...", 3 "problem_id": "sort-unique", 4 "attempt_number": 1, 5 "final_score": 1.0, 6 "status": "completed", 7 "subscores": {"matched_solution": 1.0}, 8 "weights": {"matched_solution": 1.0}, 9 "execution_time_ms": 467000, 10 "total_tokens": 34205 11}

Job

json
1{ 2 "id": "3c300cca-...", 3 "status": "completed", 4 "environment_id": "8e646c11-...", 5 "api_model_name": "claude-opus-4-5-20251101", 6 "created_at": "2025-11-24T17:46:30Z" 7}

URL Patterns

From Taiga web UI URLs:

  • Job page: https://taiga.ant.dev/job?id={job_id}&environmentId={env_id}
  • Transcripts: https://taiga.ant.dev/transcripts?id={job_id}&problemId={problem_id}&...

The id parameter in URLs is the job_id.

Tips

  1. Use Python with urllib.request - avoid shell due to env var bugs
  2. Cookie expires periodically - refresh from browser if auth fails
  3. /jobs/{id}/problems is the main endpoint for checking pass rates
  4. For streaming large responses, use the /stream variants

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is taiga-api?

Perfect for Python-based AI Agents needing seamless integration with the Taiga evaluation platform API for job results and transcripts analysis. Taiga-api is a Python-based interface for querying the Taiga evaluation platform API, enabling developers to retrieve job results, transcripts, and problem runs.

How do I install taiga-api?

Run the command: npx killer-skills add atondwal/config. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for taiga-api?

Key use cases include: Querying job results from the Taiga evaluation platform, Retrieving transcripts and problem runs using the Taiga API, Automating Taiga API requests with Python.

Which IDEs are compatible with taiga-api?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for taiga-api?

Requires Python environment. Must use Python for API requests due to shell limitations with env vars and pipes.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add atondwal/config. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use taiga-api immediately in the current project.

Related Skills

Looking for an alternative to taiga-api or another community skill for your workflow? Explore these related open-source skills.

View All

widget-generator

Logo of f
f

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

149.6k
0
AI

flags

Logo of vercel
vercel

flags is a Next.js feature management skill that enables developers to efficiently add or modify framework feature flags, streamlining React application development.

138.4k
0
Browser

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI