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Evidence synthesis

Skill cdeust/zetetic-team-subagents/skills/evidence-synthesis

Turn a pile of sources into a defensible claim. Use for "what does the literature actually say?", conflicting studies or benchmarks, "is this result trustworthy?", arguments that need claim-evidence-warrant structure, qualitative data needing coding, or a theory with no catalog of its own contradicting evidence.From its SKILL.md

Install
npx -y skills add cdeust/zetetic-team-subagents --skill evidence-synthesis

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 3 commands, including `tools/genius-invoker.sh route "<problem>"` and 2 more.

SKILL.md

2.9 KB, 614 tokens by cl100k_base, as published. Nobody here has run it

Evidence Synthesis

Problem shape: multiple sources (papers, benchmarks, interviews, texts) must be combined into one graded conclusion — and the current draft cherry-picks, skips heterogeneity, or has no explicit warrant connecting evidence to claim.

Relevant geniuses

AgentUse when
cochranestudies must be systematically found, graded, and pooled; publication bias suspected; effect sizes needed, not vote-counting
toulminan argument needs explicit claim-evidence-warrant structure with qualifiers and rebuttals; standards differ by field
feynmana result is suspiciously clean; a citation is used without the ability to rederive it; integrity audit before publishing
darwinevidence accumulates over a long horizon; the theory needs a difficulty book of its own contradictions; hardest case first
laplaceevidence must update a belief quantitatively — priors, likelihoods, calibration
gadamersources must be interpreted, not just counted — pre-understandings audited, the charitable reading found first
straussqualitative data (interviews, tickets, reviews) needs open/axial coding until saturation, not anecdote-picking
geertzthe evidence is behavior in context — thick description and emic vs etic separation before conclusions

Invocation

  1. Pick the best-fit agent above. If two or more fit, run tools/genius-invoker.sh route "<problem>" and take the top ranked match.
  2. Load it: tools/genius-invoker.sh invoke <agent> "<problem>", then read agents/genius/<agent>.md in full.
  3. Apply the agent's <workflow> step by step and answer in its <output-format>. Every synthesized claim carries its evidence grade and its strongest known counter-evidence (coding-standards §8: no source, no claim).
  4. Typical chain: cochrane grades the corpus → toulmin structures the argument → feynman audits the integrity. Run via tools/genius-invoker.sh compose cochrane toulmin -- "<problem>".
  5. If no shape above matches, use a standard team agent instead.

Refuse when

  • The conclusion is fixed in advance and only supporting sources are wanted.
  • Primary sources are unavailable and only summaries-of-summaries exist — say "I don't know" rather than synthesize hearsay.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.