Proposal: Five-Gate Governance Protocol as a quality layer before Gene submission

[191341025]

Hi EvoMap team,

I've been building a design pattern called Self-Evolving Skill for Claude Code, which focuses on how domain knowledge should be governed before it gets persisted — not just how it gets collected.

The core is a Five-Gate protocol (Value → Alignment → Redundancy → Freshness → Placement) that controls what's worth writing into a knowledge base. The most common outcome is: do nothing. Admission is harder than deletion.

I think this could complement GEP — specifically as a quality layer before a Gene Capsule is submitted to the network. Right now GEP defines how genes propagate; this pattern addresses how to decide whether something is worth turning into a gene in the first place.

GitHub: https://github.com/191341025/Self-Evolving-Skill

Would love to hear your thoughts on whether this aligns with where EvoMap is heading.

[autogame-17]

Thank you @191341025 for sharing this proposal. The Five-Gate Governance Protocol is an interesting approach to quality control at the knowledge admission stage.

You raise a valid point -- GEP currently focuses on how genes propagate and evolve, but does not explicitly define a "worthiness" filter before submission. In practice, evolver nodes do apply heuristics (e.g. fitness scoring, redundancy checks via content hashing), but there is no formalized governance protocol for this.

We will review your Self-Evolving-Skill repository in more detail. If there are specific integration points you envision with the GEP protocol, feel free to elaborate here or open a follow-up with a more concrete proposal.

[191341025]

Thanks for the response. Since you mentioned there's no formalized
governance protocol before Gene submission, I ran a first round of
empirical testing to see if my Five-Gate design actually holds up.

Experiment #1 — Smart Building Management Database (MySQL, 29 tables,
590MB), 5 rounds of progressive tasks:

• Five-Gate rejection rate: 63.6% (21/33 candidates rejected)
• Incremental convergence: +75 → +46 → +12 → +21 → +1
• Gate 2 self-correction: 2 occurrences (detected and fixed erroneous
  rules the Skill had written in earlier rounds)
• Round 5 repeated tasks: zero exploration steps, direct template reuse
• Accuracy: 100% (0 incorrect knowledge entries survived)

One unexpected finding: tool usage pitfalls (e.g. != → <> syntax,
encoding issues) emerged as high-value byproducts — things I didn't
anticipate the Five Gates would capture.

Full experiment data with per-round snapshots (diffable) is in the repo:
https://github.com/191341025/Self-Evolving-Skill/tree/main/experiments

A second experiment on a larger telecom billing database is in progress
for cross-domain reproducibility validation.

Not proposing a formal integration yet — just sharing the data in case
it's useful context for where GEP's governance layer might go.