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 agentmods add instructions/jackjin1997/agentbench-live/claude-mdgit clone --depth 1 https://github.com/jackjin1997/AgentBench-LiveWrote 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/instructions/jackjin1997/agentbench-live/claude-md)<a href="https://agentmods.dev/instructions/jackjin1997/agentbench-live/claude-md"><img src="https://agentmods.dev/badge/instructions/jackjin1997/agentbench-live/claude-md.svg" alt="Measured on agentmods" 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 | $0.00536 | $0.00536 |
| Opus 5 | $0.00268 | $0.00268 |
| Sonnet 5 | $0.00107 | $0.00107 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
AgentBench-Live CLAUDE.md 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentBench-Live
Project Goal
Get on GitHub Trending. Current: 2 stars. Need: 50-150+ stars/day spike.
Strategy: Credibility First, Then Ride the Wave
- Fix trust gaps (Docker sandbox, more agents, real scores)
- Wait for a major agent launch, benchmark it day-1
- Coordinated multi-channel social push (Twitter, Reddit, HN, 小红书, V2EX)
Current Sprint Scope (accepted from CEO review 2026-03-19)
- Docker sandbox (replace tempdir with real isolation)
- Add Codex CLI adapter (already existed)
- Add Aider adapter
- Re-run ALL agents × ALL tasks with Docker (get real scores) ← USER ACTION NEEDED
- Methodology doc (docs/methodology.md)
- Frontend redesign (radar charts, domain filtering, hero section)
- Social card generator (shareable comparison images)
- Update README for 4 agents
Deferred (TODOS)
- Agent Output Viewer (show actual agent work per task) — P2
- GitHub Actions automated benchmark CI — P2
- Automated nightly benchmark runs — Phase 2
Architecture
CLI (click) → Runner → Adapter Registry → SandboxFactory
├── DockerSandbox (primary)
└── LocalSandbox (fallback)
→ Evaluator (auto + LLM judge)
→ Ranking → Leaderboard (GitHub Pages)
Key Technical Decisions
- 4 agents: Claude Code, Gemini CLI, Codex CLI, Aider
- Docker sandbox with fallback to local tempdir
- SandboxFactory pattern (not inheritance replacement)
- 1 trial for initial run, pass@k requires multiple trials
- Sanitize API keys from logs/output
Critical Gaps to Fix
- Docker not available → fall back to LocalSandbox gracefully
- Agent timeout → record score=0 and continue, don't crash
- LLM judge failures → fall back to auto-eval, don't crash
Competitors
- SWE-bench: ~4k stars (academic, GitHub issues)
- PinchBench: ~640 stars (company-backed)
- ai-coding-lang-bench: ~98 stars (language comparison)
- TheAgentCompany: ~659 stars (simulated company)
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.
- 4d ago First seen · 56 lines · 536 tokens per session scan A 5cb1cc85dfe9
AgentBench-Live CLAUDE.md is an instructions file published in the GitHub repository jackjin1997/AgentBench-Live (4 stars, last pushed 3mo ago), licensed MIT. It adds 536 tokens to every session, about $0.0027 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
ClawBench AGENTS.md
AGENTS.md instructions for TIGER-AI-Lab/ClawBench, covering clawbench -- agent context, what this is, project structure, setup and 2. configure at least one model.
eval-view AGENTS.md
Instructions for hidai25/eval-view, covering evalview agent instructions, what evalview is, core concepts, testcase and evaluationresult.
inspect-robots CLAUDE.md
Claude Code instructions for robocurve/inspect-robots, covering inspect robots — agent guide, the one big idea, layout, working here and out of scope (separate repos / plugins).
argus-validation-benchmarks CLAUDE.md
Instructions for pensar-x/argus-validation-benchmarks, covering project overview, what you're building, the goal, success criteria and what is apex?.
skill-optimizer AGENTS.md
Instructions for fastxyz/skill-optimizer, covering agents.md, project overview, key commands, important files and installation docs.
benchjack CLAUDE.md
Instructions for benchjack/benchjack, covering claude.md, project overview, tech stack, repository layout and common commands.