evals-specify
SkillDev toolsExtract eval criteria from product specs and production failure traces (bottom-up error analysis). Writes proposed criteria to .adlc/drafts/evals/.
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Then ask your AI: use the evals-specify skill
What this skill tells your AI
The instructions your AI receives, as published by tikalk/adlc-team-skills in skills/evals/evals-specify/SKILL.md and read by ahel’s review.
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:
- Draft Eval Records - Individual
EVAL-*.mdfiles in.adlc/drafts/evals/with open coding notes - Error Pattern Documentation - Bottom-up failure taxonomy from actual traces
- Pass/Fail Examples - Real examples that should pass/fail each criterion
- Auto-handoff to
/evals-clarifyfor 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
- 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-initto set up security baselines - Known criteria already exist: Use
/evals-clarifyto refine or/evals-implementto generate code
Process
User Input
$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.mdto.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.mdupdated 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
Signals
- GitHub stars
- 133
- Forks
- 1
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
evals-specify- Source
- github.com/tikalk/adlc-team-skills