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Research output templates, distillation workflow, and quality standards for evidence-driven research
Passed 20 of 20 test cases with the skill, and 13 of 20 without it.
and 9 more in the full report
8 fixed14 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
While summarizing research on microservice circuit breakers into a distilled skill file, a developer suggests dropping abstract fault tolerance concepts to make the document shorter. What is the mandatory concept preservation rule during distillation?
| Checked | Without skill | With skill |
|---|---|---|
| The output states that 100% of essential concepts must be preserved without lossy compression | Missed | Passed |
The instructions your model reads when the skill loads.
Use for all research documents in docs/research/.
# Research: {Topic}
**Date**: {ISO-8601} | **Researcher**: nw-researcher (Nova) | **Confidence**: {High/Medium/Low} | **Sources**: {count}
## Executive Summary
{2-3 paragraph overview of key findings, main insights, and overall conclusion}
## Research Methodology
**Search Strategy**: {how sources were found}
**Source Selection**: Types: {academic/official/industry/technical_docs} | Reputation: {high/medium-high min} | Verification: {cross-referencing approach}
**Quality Standards**: Target 3 sources/claim (min 1 authoritative) | All major claims cross-referenced | Avg reputation: {0.0-1.0}
## Findings
### Finding 1: {Descriptive Title}
**Evidence**: "{Direct quote or specific data point}"
**Source**: [{Source Name}]({URL}) - Accessed {YYYY-MM-DD}
**Confidence**: {High/Medium/Low}
**Verification**: [{Source 2}]({URL2}), [{Source 3}]({URL3})
**Analysis**: {Brief interpretation or context}
{Repeat Finding structure as needed}
## Source Analysis
| Source | Domain | Reputation | Type | Access Date | Cross-verified |
|--------|--------|------------|------|-------------|----------------|
| {name} | {domain} | {High/Medium-High/Medium} | {academic/official/industry/technical} | {YYYY-MM-DD} | {Y/N} |
Reputation: High: {count} ({%}) | Medium-high: {count} ({%}) | Avg: {0.0-1.0}
## Knowledge Gaps
### Gap 1: {Description}
**Issue**: {missing/unclear info} | **Attempted**: {sources searched} | **Recommendation**: {how to address}
test cases passed, vs 13 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.