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Community Processes

Overview

This document describes the concrete workflows for participating in the aDNA community. Each process maps to a level on the participation ladder — you only need the processes relevant to your level of engagement.

Process 1: Upstream Contribution (Level 1+)

How framework-level improvements flow from individual vaults to the shared standard.

Trigger

An AI agent working in any aDNA vault notices a gap during normal work — a missing template field, an undocumented naming pattern, a workflow that could be smoother. The agent mentions the finding at a natural pause point (end of task, SITREP).

Steps

  1. Agent surfaces finding — “I noticed that template_session.md doesn’t include a field for estimated duration. This would help with token budget planning.”
  2. User evaluates — Is this a framework-level improvement (helps all vaults) or project-specific?
  3. User approves — Agent creates how/backlog/idea_upstream_{slug}.md with the structured proposal
  4. Optional: open upstream issue — If the user has GitHub CLI configured, the agent opens an issue on LatticeProtocol/Agentic-DNA
  5. Community review — Maintainers and Stewards evaluate the proposal
  6. Merge or defer — Accepted improvements enter the next standard version

Full protocol: how/skills/skill_upstream_contribution.md

Self-reference: This vault itself has surfaced upstream improvements during its build — the 10 ontology extensions added here (concepts, tutorials, patterns, etc.) informed extensions to the base template.

Process 2: Side-Quests (Level 2+)

How structured community experiments generate evidence for standard decisions.

Trigger

A question arises that needs data from multiple environments before the right answer is clear. A Steward designs a quest; community members run it.

Steps

  1. Browse how/quests/ for available quests
  2. Read the quest spec — procedure, expected output format, estimated cost
  3. Run the procedure in your own vault with spare agent tokens
  4. Record results in the required format
  5. Submit as a PR to the quest’s results/ directory
  6. Aggregation — what/lattices/tools/aggregate_results.py combines all submissions
  7. Decision — Maintainers use aggregated data to make evidence-based standard choices

Quest Lifecycle

draft → open → running → analyzing → decided → archived

Quests are not permanent. Once enough data is collected and a decision is made, the quest moves to archived status with its conclusion documented.

Process 3: Version Migration (Level 1+)

How vaults stay current as the standard evolves.

Trigger

A new version of the aDNA standard is released with improvements from community contributions.

Steps

  1. Check for available migrations in how/migrations/
  2. Read the migration guide for the target version
  3. Create a git tag as a rollback point (safety)
  4. Run the migration prompt — the agent walks through upgrading governance files, templates, and structure
  5. Verify — check that the vault still validates
  6. Commit the upgraded vault

Process 4: Content Review (Level 3)

How Stewards review community contributions.

Review Checklist

  1. Quality gates — does the contribution pass all gates from Contribution Standards?
  2. Standard alignment — is it consistent with the normative spec (adna_standard.md)?
  3. Scope — is it framework-level (helps all vaults) or project-specific?
  4. Backward compatibility — does it break existing vaults?
  5. Migration path — if it changes structure, is there a migration prompt?

Feedback

Reviews use constructive challenge, evidence-based reasoning, and clear outcomes. Reviewers state opinions, not options — “Merge because X” or “Revise because Y.”