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Conversation-to-Source

Extract reusable insights, workflows, templates, and decisions from conversation material without confusing a candidate source with publishable content.

beta v0.1.0 either invocation verified 2026-08-08
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The copied instruction points your AI to a self-contained public Skill Bundle. You can stay on this human-readable page; opening raw source is optional.

When to reach for it

  • A long AI conversation contains reusable insights, decisions, workflows, or templates.
  • The conversation needs to be separated from candidate public source material.
  • Ambiguous or sensitive context must remain visible for human review.

Do not use when

  • The source contains information that has not been cleared for the intended audience.
  • The goal is to publish a transcript or preserve private context verbatim.

Inputs

  • Conversation export or working notes
  • Intended audience and publishing boundary
  • Candidate asset types to look for

Outputs

  • One candidate source per reusable asset
  • Sensitivity and review classification
  • Evidence pointer back to the source material
  • Unresolved items requiring human review
Compatible agents
ChatGPTClaude CodeCodexHermes Agent

What it does

Conversation-to-Source separates reusable public-source candidates from the private conversation that produced them. It treats extraction as a classification and review step, not as automatic publishing.

When to reach for it

Use it after a long AI-assisted working session when decisions, insights, workflows, or templates are useful beyond the original context and need a safer source boundary.

What one run does

  1. Identify reusable assets and describe their intended audience.
  2. Create one candidate source per asset instead of copying the transcript.
  3. Attach sensitivity and review status to every candidate.
  4. Preserve a source pointer and unresolved questions.
  5. Hand candidates to a scoped review or publishing step.

Common failure modes

  • Treating a coherent conversation as automatically public.
  • Copying names, internal systems, credentials, or personal details into a candidate.
  • Losing provenance so a reviewer cannot re-check an extraction.
  • Publishing the candidate before a human boundary check.

It’s working if

A reviewer can understand each candidate without the private transcript, see its sensitivity classification, and trace it back to evidence when needed.

Where it fits

This is an Execute-phase extraction skill. It sits before Scope & Acceptance Criteria and Public-safe Publishing, which decide whether a candidate is ready for a specific public outcome.

Verification

  • Every candidate has a declared sensitivity state.
  • A reviewer can trace the candidate to its source location.
  • Private or ambiguous material is excluded from public output.

Guardrails

  • Generalize or omit private, client, company, and personal details.
  • Classify ambiguity as review rather than silently publishing it.
  • Preserve enough provenance to re-check the extracted candidate.
  • Never treat extraction as publication approval.