MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Build-Evidence-Map

Build-Evidence-Map is an design AI skill with a core value of Build an auditable evidence map for a contested technical choice, research synthesis, proposal review, or consequential decision. It helps developers solve real-world problems in the design domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

Build an auditable evidence map for a contested technical choice, research synthesis, proposal review, or consequential decision. Use when Copilot must preserve supporting, contradicting, qualifying,

Last verified on: 2026-10-06

Quick Facts

Category design
Works With GitHub Copilot, Claude
Source github/awesome-copilot
Stars ⭐ 34.1k
Last Verified 2026-10-06
Risk Level Low
mkdir -p ./skills/build-evidence-map && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/build-evidence-map/SKILL.md -o ./skills/build-evidence-map/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Build Evidence Map


Turn one contested question into a portable decision artifact that shows what

supports the current position, what pushes against it, and what remains unknown.

Do not use a graph to decorate an answer that has not been sourced.


For a simple factual claim or a general fact-checking request, use a verification

workflow such as `doublecheck` instead. Use this skill when the relationships

between evidence, intermediate claims, trade-offs, and missing facts matter.


Workflow


1. **Frame one decision.** Write one falsifiable question and one provisional

position. Narrow the question until a reader can identify what action or

belief the map is testing.

2. **Collect bounded source regions.** Prefer direct observations and primary

sources. Record the URL or absolute local path, publisher, publication date,

retrieval date, section/page/line/timestamp locator, and a short checkable

excerpt. Read [references/evidence-ladder.md](references/evidence-ladder.md)

when source quality is disputed.

3. **Atomize the reasoning.** Create only four node types:

- `position`: the single current verdict;

- `claim`: an intermediate proposition;

- `evidence`: a faithful statement of one source region;

- `unknown`: a specific missing fact that could change the verdict.

4. **Type every edge.** Use `supports`, `contradicts`, `qualifies`, or

`missing`. Add a plain-language note explaining why the source node bears on

the target. Topical similarity is not support. Different scope, date, or

population is not automatically a contradiction.

5. **Preserve counterevidence.** Do not delete contrary evidence because the

provisional verdict survives it. Represent scope differences with

`qualifies` edges.

6. **Express uncertainty structurally.** Do not invent confidence percentages.

Add an `unknown`, narrow the position, or qualify a claim.

7. **Write UTF-8 JSON** with a `.doubt.json` suffix. Follow

[references/map-schema.md](references/map-schema.md). Keep IDs short,

stable, and semantic.

8. **Validate fail-closed.** Resolve

`scripts/validate.mjs` relative to this `SKILL.md`, then run it with Node.js

18 or newer:


```bash

node <skill-directory>/scripts/validate.mjs decision.doubt.json

```


The bundled validator uses only Node.js built-ins and does not require npm or

network access. Fix every finding before reporting success. Only say the map

is valid when the command exits `0` and prints `VALID` followed by a

64-character receipt. A file hash, node count, JSON parse, or manual schema

review is not a Doubt receipt. If deterministic validation cannot run, report

that block instead of inventing success.


Render the validated map only when the user has already installed

`doubt-ai@0.8.0`; do not install or execute a remote package implicitly:


```bash

doubt map decision.doubt.json --out decision.html

```

9. **Verify source snapshots only with explicit network permission.** The

following command retrieves each recorded HTTP(S) source and fails closed if

an excerpt cannot be matched:


```bash

doubt verify decision.doubt.json \

--out decision.verified.doubt.json

```


Never run this command implicitly. Local file verification does not use the

network. Do not write a `verification` object by hand or hide a mismatch.

10. **Inspect the deliverable.** Confirm that the question, verdict,

counterevidence, unknowns, edge notes, and exact source regions remain

readable. Treat JSON as the canonical editable artifact; HTML is a

shareable view.


Quality gates


A finished map must satisfy all of these:


- exactly one `position` has incoming reasoning;

- every evidence node names one source and participates in an edge;

- every source is used and has dates, a bounded locator, and a substantive

excerpt;

- every non-position node has a directed path to the position;

- the reasoning graph has no duplicate edges or directed cyc

🎯 Best For

  • Engineering teams doing code reviews
  • Open source maintainers
  • QA engineers
  • Developers writing unit tests
  • UI designers

💡 Use Cases

  • Reviewing pull requests for security vulnerabilities
  • Checking code style consistency
  • Generating test cases for edge conditions
  • Writing integration test suites

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open GitHub Copilot or Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Build-Evidence-Map to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this skill check for OWASP Top 10?

Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.

Does this generate test mocks?

Many testing skills include mock generation. Check the install command and skill content for details.

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

Does Build-Evidence-Map generate production-ready design specs?

It generates detailed specifications that developers can use directly. Review and adjust for your specific design system.

How do I install Build-Evidence-Map?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/build-evidence-map/SKILL.md, ready to use.

⚠️ Common Mistakes to Avoid

Blindly accepting AI suggestions

Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.

Not testing edge cases

AI tends to generate happy-path tests. Manually review for boundary conditions.

Skipping usability testing

AI-generated designs should be validated with real users before development.

Not reading the full skill

Skills contain important context and edge cases beyond the quick start.

🔗 Related Skills