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 reidemeister94/development-skills --skill ai-agent-benchgit clone --depth 1 https://github.com/reidemeister94/development-skillsWrote 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/reidemeister94/development-skills/ai-agent-bench)<a href="https://agentmods.dev/skills/reidemeister94/development-skills/ai-agent-bench"><img src="https://agentmods.dev/badge/skills/reidemeister94/development-skills/ai-agent-bench/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/reidemeister94/development-skills/ai-agent-bench"><img src="https://agentmods.dev/badge/skills/reidemeister94/development-skills/ai-agent-bench.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 | $0.00033 | $0.00412 |
| Opus 5 | $0.00016 | $0.00206 |
| Sonnet 5 | $0.00007 | $0.00082 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
ai-agent-bench scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
AI agent bench
Compare agents only with the same task, starting commit, and outcome check. The harness preserves result branches and removes temporary worktrees.
Create <repo>/.agent-bench.toml:
prompt = "prompts/task.md"
start_branch = "main" # or start_commit
agents = ["claude", "codex"]
outer_check = "./scripts/full_check.sh"
inner_check = "pytest tests/integration/test_x.py -q"
outer_check proves the real outcome before and after, and measures wall time. inner_check gives agents fast feedback.
Require a clean repo, available CLIs, and a passing outer_check. Confirm agents and run ID, then run trials sequentially to avoid load-biased timing:
python <skill>/scripts/run_trial.py --repo "$REPO" --config "$REPO/.agent-bench.toml" --agent "$AGENT" --run "$RUN_ID"
Results go to eval-results/<task>/<agent>/run-<id>-<timestamp>/. Record unexpected behavior in ai-agent-bench-anomalies.md per anomalies.
Aggregate with scripts/parse_transcript.py --aggregate <run-dirs> --output comparison.json --render-report comparison.md. Report gates, branches, time delta, tokens, and cost. Never rank a failed trial.
For plugin behavior rather than a real code task, use the bounded Pydantic runner documented by eval-regression and scripts/run_evals.py.
Never commit on the user's branch. A repeated run creates a new timestamped result and preserves prior evidence.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 37 lines · 33 tokens per session scan A 10fb995f07f0
ai-agent-bench is a skill published in the GitHub repository reidemeister94/development-skills (11 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 412 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
mutation-test
Mutation testing with two engines. Uses the project's NATIVE mutation runner (StrykerJS / Infection / mutmut / PIT / cargo-mutants) when one is configured — installing it on explicit consent when it is not — for a reproducible, comparable score; and an LLM-guided engine for the mutation classes native mutators cannot…
write-tests
Write tests for existing production code. Processes ONE file at a time through a full pipeline: analyze, inventory (frozen BEFORE writing), write, executable coverage gate, verify, blind coverage audit, adversarial review, log. Uses CodeSift for discovery and analysis when available. Modes: [path] (specific target)…
code-audit
Batch audit of production files against CQ1-CQ40 quality gates and CAP1-CAP29 anti-patterns. Tiered output (A/B/C/D), critical gate enforcement, evidence-backed scoring, cross-file pattern analysis, and prioritized execution plan. Flags: zuvo:code-audit all | [path] | [file] | --deep | --quick | --services |…
plan
Analyzes architecture, selects patterns, assesses testability, then decomposes work into ordered TDD tasks with exact verification commands and explicit acceptance mapping. Works from an approved spec (zuvo:brainstorm output) or directly from a user-provided description.
pentest
Hybrid white-box + black-box penetration testing across 7 dimensions (PT1-PT7). Stack-aware source-to-sink tracing, exploit verification, CMS overlay, and deterministic finding aggregation. Uses explicit candidate schemas, canonical-key deduplication, score caps, and MUST-GATE enforcement. Flags: zuvo:pentest [path] |…
api-audit
API and endpoint integrity audit across 10 dimensions (D1-D10) plus optional contract stability (D11) and optional OWASP API Security Top 10 (D12: BOLA/BOPLA/BFLA, mass assignment, JWT alg-confusion, GraphQL introspection). Covers validation, payloads, pagination, errors, caching, HTTP semantics, waterfalls, rate…