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/build-evidence-map

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

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awesome-copilot
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Install
$ npx -y skills add github/awesome-copilot --skill build-evidence-map --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/build-evidence-map

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

build-evidence-map.SKILL.md
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:

   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:

   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:

   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.

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