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Evidence Reconciliation

Community
TheGoat395
evidence-reconciliation

Resolve stale facts and authority conflicts.

Overview

PublisherTheGoat395
RepositoryCodex-Skills
Skill nameevidence-reconciliation
Stars
136
Forks
48
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TheGoat395 on GitHub. Read the source before you install it.

Installation

Install the Evidence Reconciliation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/TheGoat395/Codex-Skills/tree/main/skills/evidence-reconciliation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TheGoat395/Codex-Skills.git /tmp/Codex-Skills
mkdir -p .claude/skills
cp -r /tmp/Codex-Skills/skills/evidence-reconciliation .claude/skills/evidence-reconciliation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evidence Reconciliation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evidence Reconciliation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evidence Reconciliation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Evidence Reconciliation

Use this skill when a conclusion, plan, document update, vendor comparison, operational rule, price, timeline, limit, or capability depends on a material source conflict, authority ambiguity, stale state or mismatched scope—or when unusually high factual integrity calls for that reconciliation. Multiple consistent sources alone do not require a separate workflow.

Do not use it for simple writing that has no factual or source-conflict component.

Outcome

Produce a conclusion or revision that is traceable to the right source, preserves the existing capability floor, and never turns a related fact into a different fact merely because it supports an appealing answer.

First classify the request

Before answering or editing, separate each material statement into one of these states:

  • Governing current rule — the newest controlling instruction, contract, configuration, or approved decision.
  • Verified current fact — directly observed account, runtime, checkout, source, or official documentation state.
  • Historical source — prior plan, old document, archived observation, or superseded draft.
  • Source-supported interpretation — a conclusion reasonably drawn from evidence.
  • Recommendation — a proposed choice, explicitly not yet adopted.
  • Assumption or unknown — a gap that needs a source, test, or a visible decision.

Never collapse these categories. A trial credit is not automatically a send limit; a vendor marketing capacity is not a chosen operating capacity; a minimum is not a recommended target; an old plan is not the current rule.

For capacity or time claims, record the relevant units, population/system boundary, limiting dimensions, operating assumptions and clock anchor. Apply sender, allowance, trial and warmup dimensions only to commercial/outbound claims, using the capacity/timeline sub-ledger in the protocol. A conclusion must satisfy every applicable limit and prerequisite.

Establish authority before synthesis

For every material conflict, identify:

  1. the exact claim at issue;
  2. the source that controls it today;
  3. the source that conflicts or appears to conflict;
  4. whether the difference is factual, temporal, semantic, or a decision change;
  5. the least disruptive correction.

Within user-owned guidance and subject to higher-priority platform instructions, use this default authority order for operating decisions unless the project defines another. For empirical truth, direct evidence, method, scope and freshness control; user or document authority cannot make a false claim true:

  1. the user's current explicit decision;
  2. newest applicable governing source;
  3. verified current live/account state;
  4. primary official documentation;
  5. validated historical plans and research;
  6. secondary commentary, creator content, remembered context, and marketing claims.

If a newer source is less authoritative, do not silently let recency overrule control.

When a governing source contains both operating policy and dated vendor facts, follow the policy as controlling but recheck the dated vendor fact at an official or authenticated current surface before calling it current.

Build a claim ledger for consequential work

For a wide, high-stakes, or document-revision task, read the contradiction protocol completely and maintain a compact ledger before drafting. The ledger must cover every claim that can change scope, money, capacity, ownership, safety, timeline, outcome, or truthfulness.

Each claim ends in exactly one state: confirmed, conflict unresolved, superseded, proposed, conditional, or unknown. Do not write final language until each material claim has a state.

For commercial capacity, outbound, trial, or deadline claims, also maintain the capacity/timeline sub-ledger in the protocol. Do not combine mutually exclusive operating paths (for example, fresh versus pre-warmed) into one timeline or capacity statement.

Answer without compliance bias

Do not agree just because the user proposes a number, interpretation, or solution. Do not reject it reflexively either.

When the user's statement is correct, say why and cite the controlling evidence. When it is inaccurate, state the exact correction plainly, including whether the error came from a prior assistant response. When evidence is mixed, state the controlling rule and the decision needed to change it.

Use short, direct wording:

The current rule is X. Y is a different metric/source. Z can be proposed later, but it is not established yet.

Never use vague agreement such as “yes, exactly” when the evidence supports only part of the statement.

Revision discipline

Before revising a standing plan, policy, or operating library:

  1. inventory the affected source documents and their unique jobs; expand to the full library only when the requested revision or its dependencies span it;
  2. map source sections to destination sections;
  3. preserve all useful detail unless a deliberate removal is recorded as superseded, duplicated, wrong, or out of scope;
  4. keep legal, quick-reference, template, and long-form operating documents separate when they have separate use cases;
  5. label future architecture as future rather than silently importing it into current policy;
  6. recheck drift-prone vendor facts at the current official/account surface before including them as operating rules.

A rewrite must preserve or improve useful decision content, exceptions and evidence. Shorter wording is acceptable when it loses no required substance; record source-to-destination mapping for substantive relocation. Do not replace requested complete content with an unrequested summary.

Challenge pass

Before concluding, run these checks:

  • Semantic: Did two similar numbers or terms refer to different things?
  • Temporal: Is an older fact being used as current state?
  • Authority: Did a noncontrolling source override the governing one?
  • Scope: Did a proposed future capability become a current promise?
  • Economics: Did a vendor price, usage charge, labor cost, or client-owned cost disappear?
  • Lifecycle/timeline: Does the claimed date name its clock anchor and satisfy applicable dependencies? For outbound/commercial capacity, include trial expiry, upgrade, authentication, warmup, ramp and reply-operation gates only where relevant.
  • Ownership: Did the revision weaken client control, isolation, recovery, or handoff?
  • Completeness: Did any document, source, exception, or user requirement get silently dropped?
  • Truth: Does every strong claim have direct evidence at the claimed verification layer?

Repair the issue or label it unresolved. Do not move forward by averaging the conflicting claims.

Report with a source map

For material work, report:

  1. the controlling conclusion;
  2. exact corrections and their source class;
  3. documents/systems changed or intentionally retained;
  4. unverified or approval-gated items;
  5. the source-to-output mapping for any rewritten library.

Do not claim a source was read, a condition was verified, or a conflict was resolved unless that happened.

Instruction authority sets objectives and operating rules; it does not establish empirical truth. Evaluate factual claims using direct evidence, method, source quality, scope and freshness even when a claim comes from the user or a governing file.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evidence Reconciliation AI skill do?

Resolve stale facts and authority conflicts.

Why use Evidence Reconciliation on TypingMind?

Because you install it once and use it with any model. Evidence Reconciliation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evidence Reconciliation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheGoat395/Codex-Skills/tree/main/skills/evidence-reconciliation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evidence Reconciliation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evidence Reconciliation?

As many as you like. As long as a model supports skills, you can use Evidence Reconciliation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evidence Reconciliation AI skill free?

Yes. It is published on GitHub by TheGoat395 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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