Evals Specify logo

Evals Specify

Organization
tikalk
evals-specify

Use when you want guided bottom-up error analysis, structured failure taxonomy discovery, or comprehensive trace coding before documenting eval criteria. Class boots only suggest capture — they never write drafts; running this skill writes the lightweight eval draft.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameevals-specify
Stars
144
Forks
2
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tikalk on GitHub. Read the source before you install it.

Installation

Install the Evals Specify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tikalk/adlc-team-skills/tree/main/skills/evals/evals-specify
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/evals/evals-specify .claude/skills/evals-specify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evals Specify in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evals Specify on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evals Specify is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

evals-specify

What this skill does

Conducts bottom-up error analysis following EDD Principles III & IX (Error Analysis & Test Data as Code) to discover and document draft evaluation criteria from human observation of system failures.

Output:

  1. Draft Eval Records - Individual EVAL-*.md files in .adlc/drafts/evals/ with open coding notes
  2. Error Pattern Documentation - Bottom-up failure taxonomy from actual traces
  3. Pass/Fail Examples - Real examples that should pass/fail each criterion
  4. Auto-handoff to /evals-clarify for axial coding and clustering

Key EDD Principles Applied:

  • Principle III: Error Analysis & Pattern Discovery - Open coding → failure taxonomy
  • Principle IX: Test Data as Code - Dataset planning and coverage analysis
  • Principle II: Binary Pass/Fail - Maintain strict binary pass/fail conditions
  • Principle V: Trajectory Observability - Track full multi-turn conversation traces

When to use

Note: Class boots detect decisions and suggest this skill — running it is how the capture happens (it writes the lightweight draft to .adlc/drafts/). Use it for interactive deep-dive exploration — when you want guided trade-off analysis, multi-option comparison, or structured decision facilitation before documenting.

  • Starting evaluation development: No existing criteria, need discovery from failure logs
  • Production incident analysis: Recent failures require systematic analysis
  • Quality assessment: Discovering and codifying boundary conditions from failures

When NOT to use

  • No failure traces/specs: Generate synthetic traces first, or use /evals-init to set up security baselines
  • Known criteria already exist: Use /evals-clarify to refine or /evals-implement to generate code

Process

User Input

text
$ARGUMENTS

Treat user input as specific failure areas or error patterns to analyze (e.g., "authentication bypass", "RAG irrelevant results").

  • --traces N — Number of traces to analyze (default: 20, min for theoretical saturation)
  • --source SOURCE — Trace source location (e.g., logs, support tickets)

Execution Steps

Phase 1: Open Coding Analysis
  • Reviews the user-provided failure logs or spec requirements.
  • Conducts open coding of traces to discover recurring failure patterns (EDD Principle III).
  • Identifies: core problem, causal conditions, and consequences.
Phase 2: Create Draft Criteria

Group patterns into draft criteria. For each:

  • Define strict Pass Condition (observable, binary yes/no)
  • Define strict Fail Condition (observable, binary yes/no)
  • Document real pass/fail examples directly from traces
Phase 3: Create Draft Files
  • Copy skills/evals/evals-templates/eval-criterion-template.md to .adlc/drafts/evals/EVAL-{NNN}.md.
  • Populate metadata and error analysis notes.
  • Regenerate index at .adlc/drafts/evals/evals.md.
Phase 4: Auto-Handoff

Trigger /evals-clarify for axial coding and clustering.

Verification

  • Draft files created at .adlc/drafts/evals/EVAL-*.md
  • Index file .adlc/drafts/evals/evals.md updated with draft summaries
  • Each draft contains: status "draft", pass/fail conditions, trace sources, and concrete examples
  • Auto-handoff context produced with list of created drafts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evals Specify AI skill do?

Use when you want guided bottom-up error analysis, structured failure taxonomy discovery, or comprehensive trace coding before documenting eval criteria. Class boots only suggest capture — they never write drafts; running this skill writes the lightweight eval draft.

Why use Evals Specify on TypingMind?

Because you install it once and use it with any model. Evals Specify is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evals Specify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/evals/evals-specify. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evals Specify?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evals Specify?

As many as you like. As long as a model supports skills, you can use Evals Specify with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evals Specify AI skill free?

Yes. It is published on GitHub by tikalk under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇