evidence-synthesizer

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• SKILL.md
• openai.yaml · agents/

SKILL.md (first lines)

# Evidence Synthesizer

## Goal

Turn multiple studies or sources into a coherent, confidence-aware evidence picture.

The job of this skill is not to force consensus. The job is to summarize what the literature supports, where it conflicts, what is still weak, and how strong the overall conclusion should be.

This skill synthesizes evidence. It does not replace literature search, and it should not silently smooth over low-quality or conflicting studies.

## Default Posture

- synthesize after search and appraisal
- conflicts are information, not clutter
- evidence strength before narrative elegance
- quality spread matters
- uncertainty stays visible

## When To Use

Run this skill when:

- multiple relevant studies have already been found
- at least light appraisal has been done or is possible
- the user wants to know what the overall evidence says
- a claim-strength judgment depends on seeing the full evidence picture
- `scientific-method-selector` routes to synthesis

This is a strong checkpoint before:

- making a research-backed conclusion
- comparing competing scientific claims
- identifying research gaps from the current literature

## Scope Boundaries

In scope:

- summarize direction of evidence across sources
- note consistency, conflict, and quality spread
- state what seems supported, tentative, or weak
- keep confidence proportional to the literature base

Out of scope by default:

- pretending to be a full systematic review
- meta-analysis style quantitative pooling by default
- hiding conflicting studies to make the answer cleaner
- turning weak literature into firm guidance

## Synthesis Workflow

1. State the target claim or question.
2. Group the relevant studies by:
   - study type
   - population or domain
   - intervention or exposure
   - outcome
3. Note the overall quality spread:
   - stronger studies
   - mixed studies
   - weak or indirect studies
4. Identify where the literature converges.
5. Identify where it conflicts or stays thin.
6. State the most defensible overall conclusion.
7. Name what would most improve confidence.

## Escalation Conditions

Pause and keep the synthesis conservative when:

- the candidate set is still too noisy or weakly screened
- study quality varies so much that simple summary would mislead
- the underlying claim is broader than the evidence base
- the conflict is substantial enough that a clean overall conclusion would overstate certainty

If those conditions persist, recommend returning to `study-quality-appraiser`, `literature-search`, or a narrower claim before stronger synthesis.

## Synthesis Heuristics

Prefer stronger overall confidence when:

- multiple higher-quality studies point in the same direction
- the literature directly matches the target claim
- outcome measures and populations are reasonably aligned
- conflicts are limited and explainable

Reduce overall confidence when:

- most evidence is observational, preclinical, or indirect
- the strongest studies are few or small
- outcomes, populations, or interventions are too heterogeneous
- conflict is substantial and unresolved
- the claim being made is broader than the literature actually covers
- the evidence base is dominated by preprints or non-reviewed sources

## Certainty Lens

When summarizing the overall evidence, explicitly consider:

- `risk_of_bias`
- `inconsistency`
- `indirectness`
- `imprecision`
- `publication_bias_or_reporting_limits`

Use these dimensions to justify the confidence level instead of giving a bare confidence label.

## Preprint-Aware Synthesis

When a meaningful share of the source set is preprint-only:

- state that the synthesis is discovery-rich but lower-certainty
- avoid language that implies settled consensus
- distinguish "emerging pattern" from "established support"
- recommend stronger reviewed corroboration when the claim matters

## Output Contract

From Alubiama/awesome-cognitive-skills, MIT.

More from Alubiama/awesome-cognitive-skills