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 skills/greglas75/zuvo/agent-benchmarknpx skills add greglas75/zuvo --skill agent-benchmarkgit clone --depth 1 https://github.com/greglas75/zuvoWhat 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 | $0.00061 | $0.03093 |
| Opus 5 | $0.00030 | $0.01546 |
| Sonnet 5 | $0.00012 | $0.00619 |
| Haiku 4.5 | $0.00006 | $0.00309 |
Grade A, and why
agent-benchmark 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 3d 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
zuvo:agent-benchmark — Self-Benchmark
You are the subject of this benchmark. YOU write the code and tests. Adversarial review (multi-provider) critiques your work between rounds. You fix based on findings.
Run this skill in different models (Opus, Sonnet, Haiku) to compare agent quality.
Argument Parsing
| Flag | Effect |
|---|---|
--quick |
Skip adversarial rounds (R1 code + R3 tests only, no fixes) |
--no-tests |
Skip test rounds (R1 + R2 only) |
--dry-run |
Print what would happen, don't execute |
| (no flags) | Full 4-round benchmark with adversarial |
Mandatory File Loading
Read these files before starting:
CORE FILES LOADED:
1. ../../shared/includes/benchmark-corpus/task-code.md -- READ/MISSING
2. ../../shared/includes/benchmark-corpus/task-tests.md -- READ/MISSING
3. ../../shared/includes/benchmark-scoring-rubric.md -- READ/MISSING
4. ../../shared/includes/run-logger.md -- READ/MISSING
5. ../../shared/includes/retrospective.md -- READ/MISSING
If any file is missing, stop.
Setup
-
Detect current model: check
$CLAUDE_MODELor infer from context. Record asagent_model. Build a short slug using this table:Model string contains Slug opusopussonnetsonnethaikuhaikugpt-5.4orcodex+5.4codex-5.4gpt-5.3orcodex+5.3codex-5.3gpt-5orcodex(no version)codexgeminigeminicomposercomposercursorcursoranything else use the model name as-is Do NOT use
agentas a slug. The slug must identify which model ran the benchmark. -
Create output directory with agent name in folder:
AGENT_SLUG="<slug from step 1>" RUN_ID="agent-bm-${AGENT_SLUG}-$(date -u +%Y%m%dT%H%M%SZ)" OUT_DIR="zuvo/reports/${RUN_ID}" mkdir -p "$OUT_DIR"
Dispatch is already authorized — do not ask, do not downgrade. Invoking this skill IS the
request for every agent and gate it mandates, so a session rule about unprompted Agent use does not
apply here. Only a harness with NO dispatch capability takes the documented single-agent fallback,
and it still runs every gate inline — see ../../shared/includes/env-compat.md. Skipping a mandated
agent and self-scoring the result is a substituted gate, not a degraded run.
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.
- 3d ago First seen · 341 lines · 61 tokens per session scan A c67e093f7c13
agent-benchmark is a skill published in the GitHub repository greglas75/zuvo (6 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 3,093 once invoked, about $0.0003 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 skills, from other repositories
roam
Codebase comprehension via roam-code CLI. Use when exploring codebases, planning modifications, debugging failures, assessing PR risk, or checking architecture health. Triggers on: understanding project structure, pre-change safety checks, finding symbols/files, blast radius analysis, affected tests, health scoring…
ring:writing-plans
Writing a rolling-wave phased implementation plan from a spec before coding: a phase-epic-task hierarchy where Phase 1 is detailed into dispatch-ready tasks and later phases stay epic-level for elaboration during execution. Use when a multi-file feature needs decomposition; runs after ring:exploring-codebases or…
ring:searching-code
Forensic code search and analysis with optional Chain of Draft (CoD) ultra-concise mode. Five-phase methodology (clarification, planning, execution, analysis, synthesis) with severity assessment. Use for targeted investigation of specific patterns, bugs, or vulnerabilities. Skip for broad architecture mapping (use…
ring:exploring-codebases
Exploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before planning changes, or to orient on an unfamiliar…
ring:generating-release-guides
Generating an internal Operations-facing update/migration guide from the git diff between two refs, documenting per-change client impact, deploy ordering, monitoring, and rollback notes in English, pt-br, or both. Use when preparing a version release or recording what changed for the Ops team. Runs read-only by…
ring:writing-skills
Writing or editing a Ring skill: SKILL.md structure, frontmatter and Agent-Search-Optimization rules, token-efficiency targets, and bulletproofing (Iron Law, rationalization tables, Red Flags) so discipline-enforcing skills resist excuses. Use when creating or revising a skill. Delegates pressure-testing to…