build-evidence-map

$npx mdskill add github/awesome-copilot/build-evidence-map

Build an auditable evidence map for contested technical decisions.

  • Preserves supporting, contradicting, qualifying, and missing evidence with exact source regions.
  • Relies on primary sources, URLs, paths, and locators; reads evidence-ladder.md for quality disputes.
  • Frames one falsifiable question and provisional position, then atomizes reasoning into four node types.
  • Delivers a portable decision artifact showing support, opposition, and unknowns without collapsing disagreement.

SKILL.md

.github/skills/build-evidence-mapView on GitHub ↗
---
name: build-evidence-map
description: '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, and missing evidence with exact source regions instead of collapsing disagreement into prose.'
---

# 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
   `[email protected]`; 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 cycles;
- contrary or qualifying evidence is present when the source set contains it;
- each decision-changing gap is an explicit `unknown` node;
- every edge note explains support, contradiction, qualification, or absence;
- the verdict is no broader than the evidence.

## Deliver the result

Report:

- the current position in one sentence;
- the strongest counterevidence or qualification;
- the most important unresolved unknown;
- paths to the canonical JSON and any rendered HTML;
- whether deterministic validation and explicit source verification ran.

Never describe a structurally valid map as proven true. Validation establishes
traceability and graph integrity; source quality and inference quality still
require human review.

More from github/awesome-copilot

SkillDescription
acquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
acreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
acreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
acreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
ad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
add-educational-comments'Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.'
adobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
agent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
agent-governance|
agent-owasp-compliance|