---
name: evidence-ledger-research-5316b97096fe
description: Research and answer evidence-sensitive questions with source, date, unit, and operand discipline. Use for web research, PDF/document QA, policy or finance facts, tables, time series, calculations from retrieved evidence, exact-answer extraction, and any task where wrong source selection, stale facts, nearby table confusion, or formatting errors would matter.
license: MIT
metadata:
  abvx_status: experimental
  abvx_origin: original
---

# Evidence Ledger Research

Use this skill when the answer must be tied to exact evidence rather than memory or broad summaries.

## Source Discipline

- Prefer primary sources, official data pages, original PDFs, repository files, or provided/oracle documents.
- If a guessed exact-date search fails, broaden to the official series, table name, dataset code, or download page.
- Read only the narrow span needed after locating a promising source, then verify entity, period, basis, unit, and version.
- If a provided document contains the answer, extract from it before searching elsewhere.
- For volatile facts, verify the latest source before answering.
- If evaluating Doc-to-LoRA or another document-memory method, pair this with `doc-to-lora-evaluator` and keep the baseline questions, source answers, and negative controls in the ledger.

## Evidence Ledger

Maintain a compact ledger for multi-step answers:

```text
Claim/operand:
Source:
Date/period:
Unit/scale:
Role:
Value/span:
Notes:
```

Use the ledger before arithmetic, comparisons, ranking, or final formatting. Do not compute from partial evidence unless the user explicitly accepts an estimate.

## Table And Document Rules

- Match row labels and exact column headers, not visual proximity alone.
- Watch for repeated month rows, fiscal vs calendar sections, continuation tables, estimates, totals, and footnotes.
- For forms and PDFs, locate the exact field/anchor in the question, then read the adjacent filled value from the same row, box, block, or table cell.
- For visual/document QA, prefer the smallest exact text span that answers the question.
- Preserve spelling, punctuation, capitalization, and numeric formatting when exact extraction matters.

## Calculation Rules

- Confirm every operand before calculating.
- Track scale and direction: thousands/millions/billions, currency, exchange-rate orientation, sign, percent vs percentage points.
- Preserve relation direction: "change from A to B" means `B - A`; "share accounted for by X" means `X / total`.
- For time windows, enumerate expected periods first and verify endpoint inclusion.
- Do not round intermediate values unless the source requires it.

## Final Answer

- Match the requested format exactly: option label, numeric value, list, date, or concise phrase.
- Include unit words only when requested or necessary for clarity.
- When citing sources, link the source and summarize the evidence without long quotations.
- Copy the final answer from the checked ledger value, not from an unverified intermediate.


---
Source attribution: markoblogo
Original project: https://github.com/markoblogo/abvx-agent-skills/blob/37a7146d940e6720a89e3281b0ddd9a0fcaf1f62/skills/evidence-ledger-research/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Anton Biletskiy-Volokh

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: markoblogo
Original project: https://github.com/markoblogo/abvx-agent-skills/blob/37a7146d940e6720a89e3281b0ddd9a0fcaf1f62/skills/evidence-ledger-research/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research
Roles: Data Analyst, Market Research Analyst, Researcher
Specialties: API
Tools: Pdf
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d58d493ee766dbd189aa950737b26954046359d008bf580744d9c8fa149e2cad