archestra-dev-bench-analysis

archestra-dev-bench-analysis is a skill for Claude Code, Codex from archestra-ai/archestra. It costs 44 tokens per session (1,557 once invoked), scanned A, original, no licence file.

A method for turning a completed archestra-bench run into a two-level improvement report using Claude subagents. The report uses Tier 1 and Tier 2 categories and does not need an API key.

In plain words
What is it for?
Use it to analyze a finished archestra-bench run and produce an improvement report.
Why use it?
It organizes raw benchmark results into findings that can guide improvements.

Skill for Claude CodeCodex

Written for Claude Code and Codex: argument-hint in frontmatter, but also installed under .codex/.

Good fit Use it to analyze a finished archestra-bench run and produce an improvement report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/archestra-ai/archestra/archestra-dev-bench-analysis
Install

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.

Any agent
npx skills add archestra-ai/archestra --skill archestra-dev-bench-analysis
Clone the repo
git clone --depth 1 https://github.com/archestra-ai/archestra

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for archestra-dev-bench-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/archestra-ai/archestra/archestra-dev-bench-analysis.svg)](https://agentmods.dev/skills/archestra-ai/archestra/archestra-dev-bench-analysis)
Your own site
<a href="https://agentmods.dev/skills/archestra-ai/archestra/archestra-dev-bench-analysis"><img src="https://agentmods.dev/badge/skills/archestra-ai/archestra/archestra-dev-bench-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,557 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.01557
Opus 5 $0.00022 $0.00779
Sonnet 5 $0.00009 $0.00311
Haiku 4.5 $0.00004 $0.00156

Measured 7d ago against content hash 570cf53a2ac2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

archestra-dev-bench-analysis 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 7d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (bin/prepare.sh, bin/render-triage.mjs, bin/render-triage.test.mjs, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.codex/skills/archestra-dev-bench-analysis/SKILL.md · 106 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Files

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.

Changes

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.

  1. 7d ago First seen · 106 lines · 44 tokens per session scan A 570cf53a2ac2

Subscribe to this mod's changes

archestra-dev-bench-analysis is a skill published in the GitHub repository archestra-ai/archestra (4,262 stars, last pushed today), with no licence file. It adds 44 tokens to every session and 1,557 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.

Related

Other skills, from other repositories

peer-review

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…

xintaofei/codeg · 71 tokens

scientific-critical-thinking

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…

xintaofei/codeg · 63 tokens

scientific-schematics

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways…

xintaofei/codeg · 68 tokens

pr-writing-review

Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer…

evalstate/fast-agent · 64 tokens

session-investigator

Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool…

evalstate/fast-agent · 68 tokens

quality-loop

Use this workflow recipe when a draft, plan, proposal, or other deliverable should be independently reviewed and revised until it satisfies explicit quality criteria.

trpc-group/trpc-agent-go · 32 tokens