agent-evaluation-reporting SKILL.md skillA
This file is byte-identical to the first copy the registry indexed. Same content hash, so the same checks give both the same grade.
agent-evaluation-reporting from francostino/opencode-skills-collection is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add francostino/opencode-skills-collection/agent-evaluation-reporting@git:20260819.41bdcbbPins git:20260819.41bdcbb in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- francostino/opencode-skills-collection
- Path
bundled-skills/agent-evaluation-reporting/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260819.41bdcbb, committed Aug 19, 2026
- Size
- 8,264 bytes
- Repository stars
- 87
- License
- MIT
Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Agent Evaluation Reporting ## Overview Turn raw agent evaluation runs into a decision-ready report without hiding failures or overstating capability. Keep outcome populations, denominators, latency populations, and experiment conditions explicit so readers can reproduce every headline number. ## When to Use This Skill - Use when reporting benchmark, regression, pilot, or production evaluation runs for an AI agent. - Use when autonomous and human-assisted completions appear in the same result set. - Use when failures, timeouts, infrastructure-invalid runs, retries, or partial results affect the denominator. - Use when comparing two agents, prompts, harnesses, or releases and deciding whether the comparison is valid. ## How It Works ### Step 1: Freeze the comparison contract Record the task set and sampling, model and provider, prompt or policy version, tool and harness versions, evaluator rubric, timeout and retry policy, token or cost budget, environment, and human-intervention policy. Assign the configuration a stable label or digest. …
Read the whole file at its exact version.
How to install
mdr add francostino/opencode-skills-collection/agent-evaluation-reporting@sha256:800b27a3d99da189The line at the top of this page pins the label git:20260819.41bdcbb, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_2rsthiiggij67ny2)
5 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Aug 19, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (8264 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
francostino/opencode-skills-collection · 87 stars · license MIT · pushed 2026-10-18 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_2rsthiiggij67ny2 GET https://markdownregistry.com/api/v1/resolve?ref=francostino/opencode-skills-collection/agent-evaluation-reporting GET https://markdownregistry.com/api/v1/blob/800b27a3d99da18961afaadb8678450f14552313f9f9380b255c893f68f5ee88
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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