skill-benchmark
- Repo stars 498
- Author updated Live
- Author repo agent-skills-standard
- Domain
- Engineering
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @HoangNguyen0403 · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: skill-benchmark
description: Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.…
category: engineering
runtime: no special runtime
---
# skill-benchmark output preview
## PART A: Task fit
- Use case: Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints. When the user asks to perform this workflow, execute the following steps: Identify the tech stack and all active skills in AGENTS.md. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Instructions / Step 1 — Project Context & Active Skills / Step 2 — Auto-Select a Legacy Trap” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints. When the user asks to perform this workflow, execute the following steps: Identify the tech stack and all active skills in AGENTS.md. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Instructions / Step 1 — Project Context & Active Skills / Step 2 — Auto-Select a Legacy Trap” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Instructions / Step 1 — Project Context & Active Skills / Step 2 — Auto-Select a Legacy Trap”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: skill-benchmark
description: Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.…
category: engineering
source: HoangNguyen0403/agent-skills-standard
---
# skill-benchmark
## When to use
- Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints. When the user asks to…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Instructions / Step 1 — Project Context & Active Skills / Step 2 — Auto-Select a Legacy Trap” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "skill-benchmark" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Instructions / Step 1 — Project Context & Active Skills / Step 2 — Auto-Select a Legacy Trap
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Skill Benchmark Skill
[!IMPORTANT] Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
Instructions
When the user asks to perform this workflow, execute the following steps:
📊 Skill Benchmark Orchestrator
Goal: Quantify how much active skills improve implementation quality. Deliver a prioritized compliance delta and skill applicability report.
Step 1 — Project Context & Active Skills
Identify the tech stack and all active skills in AGENTS.md.
# 1. Total source files and lines changed
find src -name "*.ts" -o -name "*.tsx" | xargs wc -l 2>/dev/null | sort -rn | head -20
# 2. Check active skill registry
cat AGENTS.md | head -80Step 2 — Auto-Select a Legacy Trap
Pick the file automatically. Rank candidates by the severity of anti-patterns:
- 🔴 P0: Hardcoded secrets; Logic inside UI components.
- 🟠 P1: Wrong Router pattern; Global state for local concerns; Missing design tokens.
- 🟡 P2: Raw user-facing strings (i18n).
Step 3 — Build Eval-Driven Scorecard
Source your scorecard from evals/evals.json, not from hardcoded patterns.
Follow the Scorecard Rubric in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced:
- Read
<SKILLS>/<category>/<skill>/evals/evals.json. - Generate columns for Failure Pattern and Success Pattern.
- Refactor the file, citing the exact skill rule for each change.
Step 4 — Benchmark Report & Compliance Delta
Output the scorecard and compliant score using the templates in <SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
- Compliance Score Before vs After.
- Δ Delta: +Z% 🚀.
- Eval Alignment: How well does the skill teach what the eval tests?
Step 5 — Skill Applicability & Iteration
For every ❌ FAIL, identify the root cause using the Iteration Table in:
<SKILLS>/common/common-skill-creator/references/benchmark.md when synced.
- Signal not matching file? → Refine trigger.
- Rule too vague? → Add Anti-Pattern rule.
- Conflict? → Ensure P0 overrides P1.
Suggested .skillsrc Exclusions
Recommend any skills that are noisy or non-applicable for the project.
exclude:
- [skill-id] # reason
Decide Fit First
Design Intent
How To Use It
Boundaries And Review