aif-grounded

Reliability gate for answers. Forces evidence-based reasoning, explicit uncertainty, and “insufficient information” instead of guesses. Use when user says “be 100% sure”, “no hallucinations”, “only if verified”, “grounded answer”, or when stakes are high.

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Install skill "aif-grounded" with this command: npx skills add lee-to/ai-factory/lee-to-ai-factory-aif-grounded

Grounded - Reliability Gate (No Guessing)

This skill minimizes random / fabricated answers by enforcing a strict rule:

Only provide the final answer if confidence is 100/100 based on evidence available.

If confidence is not 100, do not guess and do not implement. Output a short “what’s missing” checklist that explains what would be required to reach 100.

When to use

Use when:

  • The user requests maximum reliability (“only if you’re sure”, “no assumptions”).
  • The request includes changeable facts (versions, “latest”, policies, prices, schedules).
  • The request is security/finance/legal/medical adjacent (high stakes).
  • You’re resuming after context loss and need to avoid accidental assumptions.

Workflow

Step 0: Load Skill Context

Read .ai-factory/skill-context/aif-grounded/SKILL.md — MANDATORY if the file exists.

This file contains project-specific rules accumulated by /aif-evolve from patches, codebase conventions, and tech-stack analysis. These rules are tailored to the current project.

How to apply skill-context rules:

  • Treat them as project-level overrides for this skill's general instructions
  • When a skill-context rule conflicts with a general rule written in this SKILL.md, the skill-context rule wins (more specific context takes priority — same principle as nested CLAUDE.md files)
  • When there is no conflict, apply both: general rules from SKILL.md + project rules from skill-context
  • Do NOT ignore skill-context rules even if they seem to contradict this skill's defaults — they exist because the project's experience proved the default insufficient
  • CRITICAL: skill-context rules apply to ALL outputs of this skill — including the response format, evidence requirements, and confidence assessment. If a skill-context rule says "analysis MUST include X" or "confidence MUST account for Y" — you MUST comply. Producing an analysis that ignores skill-context rules is a bug.

Enforcement: After generating any output artifact, verify it against all skill-context rules. If any rule is violated — fix the output before presenting it to the user.

Step 1: Classify the request

Classify into one of:

  1. Repo-grounded — can be answered purely from the local codebase and command outputs.
  2. Doc-grounded — requires authoritative docs/specs/logs provided by the user or accessible tooling.
  3. External-facts — depends on changeable facts outside the repo (must be verified, otherwise refuse).

Step 2: Define evidence and unknowns

Before answering, list:

  • Evidence sources you will use (files, command outputs, provided docs).
  • Unknowns (anything not present in evidence).

Hard rule:

  • If a claim is not supported by evidence, it becomes an unknown (not an assumption).

Step 3: Mandatory verification for changeable facts

If the request contains any changeable fact (“latest”, “current”, “today”, “default in vX”, “does library Y support Z now”):

  • Verify via authoritative docs/specs, release notes, or logs.
  • If verification is not possible with available tools/context, return INSUFFICIENT INFORMATION and ask for the needed source (link excerpt, version, log output).

Step 4: Confidence gate

Compute a confidence score 0–100:

  • 100 only if every factual claim is supported by evidence you can point to (repo files, command outputs, provided docs), and there are no open unknowns.
  • If any unknown remains → confidence < 100 → do not answer/implement.

Step 5: Output format (strict)

If confidence is 100:

Answer:
<final answer or patch summary>

Confidence: 100/100
Evidence:
- <file/command/doc used>

Checks:
- <3 concrete checks someone can run/inspect to confirm>

If confidence is < 100:

Result: INSUFFICIENT INFORMATION (no guessing)
Current confidence: <N>/100
Why not 100:
- <top reasons>

Missing evidence:
- <what exact file/output/doc is needed>

To reach 100:
- <1–3 concrete asks or commands for the user to run and paste output>

Implementation guardrail

If the user asks for code changes:

  • You may explore the repo and propose what evidence is needed.
  • Only apply patches once confidence can be 100 (e.g., requirements are precise + you can verify build/tests or equivalent checks).
  • If the repo lacks a verification path (no build/tests and behavior can’t be validated), do not claim 100; return INSUFFICIENT INFORMATION and propose the minimal validation needed.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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