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Research · Version 1.0.0 · Reviewed 2026-08-02

Literature Evidence Table Builder

Produce defensible evidence for study characteristic extraction and evidence quality comparison with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Converts papers and reports into a structured evidence table covering question, population, method, intervention, outcome, limitations, effect direction, and applicability.

₹149 one-time

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What this skill helps you do

  • Study characteristic extraction
  • Evidence quality comparison
  • Applicability assessment

How Literature Evidence Table Builder works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for study characteristic extraction

It decides

A evidence quality comparison change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

What it checks first

Literature Evidence Table Builder converts papers and reports into a structured evidence table covering question, population, method, intervention, outcome, limitations, effect direction, and applicability. Use it when the work involves Study characteristic extraction, Evidence quality comparison, Applicability assessment.

  1. Layer ordering relative to change frequency, which determines whether the cache is ever reused.
  2. Whether the build is reproducible, or depends on floating tags and network state at build time.
  3. Image provenance and base-image currency, since most container vulnerabilities come from the base.
  4. Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
  5. The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

  • Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
  • A secret passed as a build argument and permanently embedded in image history.
  • A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
  • Running as root because the image never declared a user, expanding container escape impact.
  • A cache key that includes a timestamp, so the cache never hits.
  • Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

  • Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
  • Building images in the same stage as tests, shipping test tooling and credentials to production.
  • Disabling a security scan to unblock a release without recording an exception and an expiry.
  • Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

  • Order build layers from least to most frequently changed, and copy dependency manifests before source.
  • Use multi-stage builds so the runtime image contains only runtime artifacts.
  • Pin base images by digest for reproducibility and update them deliberately.
  • Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

  • Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
  • Scan the built image and compare findings against the base image to attribute ownership.
  • Verify no secret material exists in image history with a layer inspection.

The method inside

  1. Define the research question and unit of analysis
  2. Create a transparent coding or extraction framework
  3. Preserve source traceability and negative evidence
  4. Separate findings, interpretation, limitations, and applicability

Deliverables

  • Study characteristic extraction evidence table
  • Evidence quality comparison findings with negative cases
  • Applicability assessment limitations and next-research plan

Evidence requirements

  • Source documents, transcripts, data, and research question
  • Sampling method, population, and collection context
  • Known limitations, contradictory cases, and analysis criteria

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Build an evidence table from these studies on remote onboarding and identify where conclusions disagree.

Expected output

The strongest studies support structured manager check-ins, while claims about virtual social events rely on small self-selected samples. Results differ mainly by employee tenure and outcome definition...

Boundaries and compatibility

Ideal for

  • Study characteristic extraction: produce a decision or artifact grounded in supplied evidence.
  • Evidence quality comparison: produce a decision or artifact grounded in supplied evidence.
  • Applicability assessment: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Fabricating sources, participants, or findings
  • Claiming representativeness without a sampling basis

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.