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mindbomber

AANA Evidence First Answering Skill

by mindbomber · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install aana-evidence-first-answering
Description
Guides answering by separating known facts, assumptions, missing evidence, and retrieval steps to ensure evidence-based, cautious conclusions.
README (SKILL.md)

AANA Evidence First Answering Skill

Use this skill when an OpenClaw-style agent may answer a question, draft a recommendation, summarize a situation, explain a decision, or act on incomplete evidence.

This is an instruction-only skill. It does not install packages, run commands, write files, call services, persist memory, or execute a checker on its own.

Core Principle

Answer drafts should separate known facts, assumptions, missing evidence, and next retrieval steps before presenting conclusions.

The agent should separate:

  • facts directly provided by the user,
  • facts verified from available tools or sources,
  • reasonable assumptions,
  • uncertain claims,
  • missing evidence,
  • retrieval or clarification steps needed before confidence,
  • conclusions that should be revised, deferred, or refused.

When To Use

Use this skill before:

  • answering fact-sensitive questions,
  • making recommendations,
  • summarizing evidence,
  • explaining why an action is safe or unsafe,
  • drafting reports, emails, research notes, support replies, legal/medical/financial caveats, or code reviews,
  • acting when source evidence, tool results, logs, tests, policies, records, or user intent are incomplete,
  • turning a partial observation into a confident conclusion.

Evidence Classes

Classify each important claim as:

  • known_user_provided: stated by the user,
  • known_tool_verified: observed from tools, files, tests, logs, sources, or systems,
  • known_policy_or_instruction: explicit policy, instruction, schema, or contract,
  • assumption: plausible but not verified,
  • inference: reasoned conclusion from known evidence,
  • missing_evidence: needed but not available,
  • unsupported: should be removed, revised, asked about, or retrieved.

AANA Evidence Loop

  1. Identify the answer or action the agent is about to produce.
  2. List the important claims that support the answer.
  3. Classify each claim as known, assumed, inferred, missing, or unsupported.
  4. Check whether any conclusion depends on missing or unsupported evidence.
  5. Decide whether to answer, revise, ask, retrieve, defer, or refuse.
  6. If answering, label uncertainty and avoid overclaiming.
  7. If retrieving, name the next source, file, tool, person, or record needed.

Required Pre-Answer Checks

Before finalizing an answer, verify:

  • what is actually known,
  • what was inferred,
  • what is assumed,
  • what evidence is missing,
  • whether missing evidence affects the conclusion,
  • what retrieval step would reduce uncertainty,
  • whether the answer should be narrower, conditional, or deferred.

Known Fact Rules

Only mark a fact as known when it is:

  • directly stated by the user,
  • observed in a file, tool output, log, test result, source, system record, or policy,
  • part of an explicit instruction or schema,
  • common stable context that does not need retrieval.

Do not mark as known:

  • likely intent,
  • guessed dates, prices, policy terms, legal rules, medical facts, account states, or test outcomes,
  • model memory when current verification is required,
  • claims from unavailable or unreviewed sources.

Assumption Rules

Assumptions are allowed only when they are:

  • low risk,
  • explicitly labeled,
  • easy for the user to correct,
  • not the basis for irreversible, high-impact, private, legal, medical, financial, code, file, or external-send actions.

If an assumption controls the answer, ask or retrieve instead.

Missing Evidence Rules

Use ask when the missing evidence is held by the user.

Use retrieve when the missing evidence is likely in:

  • files,
  • logs,
  • tests,
  • policy documents,
  • source links,
  • account records,
  • support tickets,
  • medical, legal, financial, or purchase records,
  • current external information.

Use defer when the evidence requires a qualified professional, unavailable system, human review, or approved tool.

Unsupported Claim Rules

Revise or remove claims that:

  • invent facts,
  • cite unavailable sources,
  • imply tests ran when they did not,
  • overstate certainty,
  • turn examples into proof,
  • generalize from insufficient evidence,
  • claim safety, legality, medical accuracy, financial benefit, or policy compliance without support.

Review Payload

When using a configured AANA checker, send only a minimal redacted review payload:

  • task_summary
  • answer_summary
  • known_facts
  • assumptions
  • missing_evidence
  • unsupported_claims
  • next_retrieval_steps
  • recommended_action

Do not include raw secrets, private records, full logs, full transcripts, full account records, or unrelated private data when a redacted summary is enough.

Decision Rule

  • If the answer rests on known facts and uncertainty is handled, accept.
  • If the answer contains unsupported claims, revise.
  • If the user can supply missing evidence, ask.
  • If evidence can be obtained from an approved source or tool, retrieve.
  • If evidence requires human, professional, or unavailable-system review, defer.
  • If the request asks for confident claims despite missing evidence in a high-risk setting, refuse unsafe certainty and explain the boundary.
  • If a checker is unavailable or untrusted, use manual evidence-first review.

Output Pattern

For evidence-sensitive work, prefer:

Evidence map:
- Known facts: ...
- Assumptions: ...
- Missing evidence: ...
- Next retrieval: ...
- Answer boundary: ...

For user-facing answers, keep this concise unless the user asks for the full audit trail.

Usage Guidance
This skill appears safe to install as an instruction-only aid for more cautious answers. If you use it with retrieval tools or an AANA checker, make sure those tools are approved and avoid sending raw private records, secrets, full logs, or sensitive account information.
Capability Analysis
Type: OpenClaw Skill Name: aana-evidence-first-answering Version: 1.0.0 The skill is a set of meta-instructions designed to improve agent reliability by enforcing an 'Evidence First' reasoning framework. It contains no executable code, scripts, or dependencies, and its instructions (SKILL.md) explicitly mandate the redaction of sensitive data and the separation of facts from assumptions. The manifest.json and schema files further reinforce these safety boundaries by forbidding the inclusion of secrets or private records in review payloads.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
The stated purpose is evidence-first answering, and the artifacts consistently provide instructions for separating facts, assumptions, missing evidence, and uncertainty.
Instruction Scope
The skill may prompt the agent to retrieve evidence from files, logs, records, or external sources, but it frames retrieval as evidence-gathering from approved tools/sources and includes deferral and redaction guidance.
Install Mechanism
There is no install spec, bundled code, dependency installation, command execution, or required binary.
Credentials
No environment variables, credentials, OS-specific access, or local configuration paths are required. Capability signals for crypto or purchases are not supported by actual code or authority in the provided artifacts.
Persistence & Privilege
The manifest and README state that the skill does not write files, persist memory, call services by itself, or store payloads by default.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install aana-evidence-first-answering
  3. After installation, invoke the skill by name or use /aana-evidence-first-answering
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the AANA Evidence First Answering skill: - Introduces evidence-first answer drafting, requiring separation of known facts, assumptions, missing evidence, and next retrieval steps. - Defines evidence classification categories for each claim (e.g., user-provided, tool-verified, assumption, inference, missing, unsupported). - Outlines a structured "Evidence Loop" process for checking claims before making answers or recommendations. - Establishes rules for marking facts as known, making assumptions, and handling missing or unsupported evidence. - Specifies pre-answer validation steps and decision rules to manage uncertainty and risk. - Provides a clear output pattern for evidence-sensitive responses, supporting transparency and auditability.
Metadata
Slug aana-evidence-first-answering
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is AANA Evidence First Answering Skill?

Guides answering by separating known facts, assumptions, missing evidence, and retrieval steps to ensure evidence-based, cautious conclusions. It is an AI Agent Skill for Claude Code / OpenClaw, with 67 downloads so far.

How do I install AANA Evidence First Answering Skill?

Run "/install aana-evidence-first-answering" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is AANA Evidence First Answering Skill free?

Yes, AANA Evidence First Answering Skill is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does AANA Evidence First Answering Skill support?

AANA Evidence First Answering Skill is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created AANA Evidence First Answering Skill?

It is built and maintained by mindbomber (@mindbomber); the current version is v1.0.0.

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