Agent skill

Microbenchmarking

by rodri-oliveira-dev in rodri-oliveira-dev/Dapper-FluentMap

Activate when BenchmarkDotNet is involved or when a .NET performance question requires controlled microbenchmark measurement.

MITAuto-check passedTesting & QA

Install Microbenchmarking

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/microbenchmarking .claude/skills/microbenchmarking && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .claude/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/microbenchmarking .agents/skills/microbenchmarking && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .agents/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/microbenchmarking .cursor/skills/microbenchmarking && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .cursor/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git --path .agents/skills/microbenchmarking

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/microbenchmarking .gemini/skills/microbenchmarking && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .gemini/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/microbenchmarking .github/skills/microbenchmarking && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .github/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install rodri-oliveira-dev/Dapper-FluentMap microbenchmarking --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rodri-oliveira-dev/Dapper-FluentMap.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/microbenchmarking .opencode/skills/microbenchmarking && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "microbenchmarking" agent skill from https://github.com/rodri-oliveira-dev/Dapper-FluentMap/tree/main/.agents/skills/microbenchmarking into .opencode/skills/microbenchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "microbenchmarking", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: microbenchmarking
GitHub stars: 453
Token cost: ~1.1k tokens
SKILL.md length: 436 words
Files: 6 (incl. references)
Skills in repo: 11
Repo updated: Oct 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Activate when BenchmarkDotNet is involved or when a .NET performance question requires controlled microbenchmark measurement.

Works in 4 steps: Coverage suite — permanent… → Issue investigation — task-scoped… → Change validation — before/after… → …
Profiling/tracing
SKILL.md covers Benchmarks are comparative…, Benchmark lifecycle, Cost awareness and Running benchmarks, plus 4 more sections
Calls dotnet

What it does

Microbenchmarking is an agent skill from rodri-oliveira-dev/Dapper-FluentMap. Activate when BenchmarkDotNet is involved or when a .NET performance question requires controlled microbenchmark measurement. Covers benchmark design, BDN configuration, project setup, cost-aware execution, side-by-side comparisons, diagnostics, and interpretation. Do not use for profiling/tracing, production telemetry, or load/stress testing.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/bdn-internals-and-tuning.md`, `references/comparison-strategies.md` and `references/diagnosers-and-exporters.md`).

It sits in Testing & QA, covering Load testing. It works with .NET. The repository describes itself as: Fluent mapping for Dapper, with conventions, immutable object materialization, analyzers, source generators, DI integration, and Dommel support. The licence is MIT.

When your agent uses it

Profiling/tracing
Production telemetry
Load/stress testing

Example prompts

“/microbenchmarking”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Coverage suite — permanent representative benchmarks.
Issue investigation — task-scoped reproduction of a performance problem.
Change validation — before/after validation for a PR.
Development feedback — temporary experiment.

What it can do on your machine

Read from SKILL.md and the folder at commit 90600cb. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- dotnet
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Microbenchmarking loads about 1.1k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 436 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~91
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rodri-oliveira-dev/Dapper-FluentMap at commit 90600cb, republished under its MIT licence (© rodri-oliveira-dev). 436 words, ~1,105 tokens.

Download SKILL.mdSave it as .claude/skills/microbenchmarking/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: microbenchmarking
description: Activate when BenchmarkDotNet is involved or when a .NET performance question requires controlled microbenchmark measurement. Covers benchmark design, BDN configuration, project setup, cost-aware execution, side-by-side comparisons, diagnostics, and interpretation. Do not use for profiling/tracing, production telemetry, or load/stress testing.
license: MIT

Benchmark Authoring Guidelines

BenchmarkDotNet (BDN) is the default tool for controlled .NET microbenchmarks in this skill.

Dapper-FluentMap integration: prefer the existing benchmarks/Dapper.FluentMap.Benchmarks project. Preserve repository dependency/versioning conventions as they actually exist; this repository currently uses explicit PackageReference versions rather than Central Package Management. Do not introduce CPM as incidental benchmark work. Benchmark changes must not alter public behavior merely to improve measurements.

Benchmarks are comparative instruments

A single number has limited value. Identify the comparison axis first:

mapping approaches;
current vs candidate implementation;
runtime/package versions;
reflection/runtime mapping vs generated paths;
input scale;
allocation behavior;
historical measurements.

See references/comparison-strategies.md before configuring non-trivial comparisons.

Benchmark lifecycle

Choose the use case before creating code:

Coverage suite — permanent representative benchmarks.
Issue investigation — task-scoped reproduction of a performance problem.
Change validation — before/after validation for a PR.
Development feedback — temporary experiment.

Only permanent coverage-suite benchmarks should automatically become repository code. Temporary experiments should remain isolated unless explicitly requested.

Cost awareness

Each BDN case has real wall-clock cost. [Params] creates Cartesian products and multiple jobs multiply the case count.

Preset	Typical purpose
`--job Dry`	correctness/compilation validation
`--job Short`	quick development measurements
default	final normal measurements
`--job Medium`	higher confidence
`--job Long`	exceptional high-confidence runs

Always estimate method × parameter × job case count before a large run.

Running benchmarks

Inspect the current benchmark entry point before assuming CLI forwarding. Use a narrow filter and redirect verbose output:

bash

dotnet run --project ./benchmarks/Dapper.FluentMap.Benchmarks/Dapper.FluentMap.Benchmarks.csproj -c Release -- --filter "*MethodName" --noOverwrite > benchmark.log 2>&1

Run a dry representative case before longer measurements.

Show full SKILL.md (196 more words)Show less

Writing new benchmarks

Determine the real caller scenario, comparison axis, input shape, setup/reset needs, parameter count, and whether allocation diagnostics matter.

Key invariants:

return results when needed to prevent dead-code elimination;
move initialization to [GlobalSetup];
do not add manual loops merely to increase measurement work;
mark an explicit baseline;
store inputs in fields/params rather than foldable constants;
use seeded randomness for reproducibility;
materialize deferred execution when execution is what should be measured;
isolate global FluentMap/Dapper configuration so one case does not contaminate another.

See references/writing-benchmarks.md.

Dependency/setup rule

BenchmarkDotNet is already present in the repository benchmark project. Do not add another benchmark project or change dependency-management strategy unless the task demonstrates a need. If dependency versions change, follow the repository's existing package-version conventions and validate the benchmark project explicitly.

Diagnostics

Use references/diagnosers-and-exporters.md when timing alone is insufficient. Allocation data can be especially relevant for a mapping library.

Validation

Build the benchmark project in Release.
Run a dry representative case.
Run a narrow real measurement.
Confirm baseline and input set.
Read generated Markdown/CSV results.
Report runtime, hardware/OS and statistical limitations.
Do not treat tiny differences inside measurement noise as meaningful regressions/improvements.

Files

SKILL.md and 5 other files (references) in .agents/skills/microbenchmarking of rodri-oliveira-dev/Dapper-FluentMap.

SKILL.md
references/bdn-internals-and-tuning.md
references/comparison-strategies.md
references/diagnosers-and-exporters.md
references/project-setup-and-running.md
references/writing-benchmarks.md

Open the folder on GitHubat commit 90600cb

Compare with similar skills

Microbenchmarking next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Microbenchmarking compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Microbenchmarking this skillrodri-oliveira-dev/Dapper-FluentMap	453	—	~1.1k	Automated safety check: Pass	MIT	yesterday
Microbenchmarkingdotnet/skills	5.6k	3 repos	~3.3k	Automated safety check: Pass	MIT	today
Microbenchmarkingatherio-danp/cde-dotnetcc	109	—	~979	Automated safety check: Notes	None	2 mo ago
Code Reviewdotnet/macios	2.9k	—	~1.7k	Automated safety check: Pass	Custom licence	today
Profilingmanagedcode/dotnet-skills	486	—	~1.6k	Automated safety check: Pass	MIT	today
Evaluate PR Testsdotnet/maui	23k	—	~2.9k	Automated safety check: Pass	MIT	today

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Binlog Failure Analysis
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CI Release Governance
rodri-oliveira-dev/Dapper-FluentMap
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453 GitHub stars~1.8k tokensUpdated yesterday
Auto-check passed

Works with

.NET

Questions about Microbenchmarking

What does Microbenchmarking do?

Activate when BenchmarkDotNet is involved or when a .NET performance question requires controlled microbenchmark measurement. Microbenchmarking is an agent skill from rodri-oliveira-dev/Dapper-FluentMap.NET performance question requires controlled microbenchmark measurement.

When should I use Microbenchmarking?

Microbenchmarking fits situations like: profiling/tracing; production telemetry; load/stress testing.

How do I install Microbenchmarking in Claude Code?

Run `npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a claude-code`. Or copy the skill folder (.agents/skills/microbenchmarking in rodri-oliveira-dev/Dapper-FluentMap) into .claude/skills/microbenchmarking in your project. Claude Code loads it when a task matches its description.

How do I install Microbenchmarking in Codex?

Run `npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a codex`. Or copy the skill folder (.agents/skills/microbenchmarking in rodri-oliveira-dev/Dapper-FluentMap) into .agents/skills/microbenchmarking in your project. Codex loads it when a task matches its description.

Can I use Microbenchmarking in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rodri-oliveira-dev/Dapper-FluentMap --skill microbenchmarking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/microbenchmarking, .gemini/skills/microbenchmarking, .github/skills/microbenchmarking and .opencode/skills/microbenchmarking in your project.

What does Microbenchmarking need to run?

Going by SKILL.md and its folder, Microbenchmarking needs the command-line tools its instructions call (dotnet).

Does Microbenchmarking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Microbenchmarking safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Microbenchmarking use?

Microbenchmarking is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Microbenchmarking use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.

What are the alternatives to Microbenchmarking?

Skills that share tags, products or a category with Microbenchmarking: Microbenchmarking (dotnet/skills, 5.6k stars), Microbenchmarking (atherio-danp/cde-dotnetcc, 109 stars), Code Review (dotnet/macios, 2.9k stars) and Profiling (managedcode/dotnet-skills, 486 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Microbenchmarking?

rodri-oliveira-dev (a GitHub user) maintains it in rodri-oliveira-dev/Dapper-FluentMap, which has 453 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 6, 2026.

Source: rodri-oliveira-dev/Dapper-FluentMap on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Microbenchmarking

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Benchmark Authoring Guidelines

Benchmarks are comparative instruments

Benchmark lifecycle

Cost awareness

Running benchmarks

Writing new benchmarks

Dependency/setup rule

Diagnostics

Validation

Files

Compare with similar skills

Similar skills

More from rodri-oliveira-dev/Dapper-FluentMap

Related

Questions about Microbenchmarking