harness-similarity
ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2 Recommender, §3.3 Drift Detection, §3.5 Plugin Compat. Pure-TS, no `@metaharness/*` dep — preserves ADR-150's four architectural constraints.
Profile →npx skills add ruvnet/ruflo --skill harness-similarity --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
Surfaces the production similarity function from [`scripts/_similarity.mjs`](../../scripts/_similarity.mjs) as a callable skill. Use when an agent needs to: - decide whether to fork an existing harness vs scaffold a new one - rank candidate templates against a target repo's genome - diff two harnesses produced by different teams to find duplicate work - generate the confidence number that ADR-151 §3.2's Recommender wraps ## Algorithm (from ADR-152 §Decision) ``` overall = 0.60·cosine + 0.25·categorical + 0.15·jaccard ``` - **cosine** — over a 9-dim numerical vector of normalized scorecard + genome dims - **categorical** — fraction of 4 enum fields that match (`repo_type`, `archetype`, `template`, `recommendedMode`) - **jaccard** — `|A ∩ B| / |A ∪ B|` over the `agent_topology[]` array The 3-component design is load-bearing: numerical cosine alone is too coarse (the iter-35 spike showed LEGAL vs DEVOPS at cosine=0.97 despite being unrelated verticals). Categorical + jaccard pull the composite to the correct ordering. ## Reference outputs (iter-35 spike fixtures) | Pair | overall | cosine | categorical | jaccard | |---|---:|---:|---:|---:| | `LEGAL` × `LEGAL` (self) | 1.0000 | 1.0000
- Algorithm (from ADR-152 §Decision)
- Reference outputs (iter-35 spike fixtures)
- Architectural constraint inheritance (ADR-150)
- Usage
- Implementation
File inputs npx ruflo metaharness similarity --a a.json --b b.json Memory inputs (records persisted by oia-audit.mjs) npx ruflo metaharness similarity --a-key harness-X --b-key harness-Y Per-dimension breakdown (used by ADR-151 §3.2 Recommender) npx ruflo metaharness similarity --a a.json --b b.json --per-dimension Alert when too-dissimilar (used by ADR-151 §3.3 Drift Detection) npx ruflo metaharness similarity --a a.json --b b.json --alert-below 0.5
What does the harness-similarity skill do?
ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2 Recommender, §3.3 Drift Detection, §3.5 Plugin Compat. Pure-TS, no `@metaharness/*` dep — preserves ADR-150's four architectural constraints.
How do I install it?
Run `npx skills add ruvnet/ruflo --skill harness-similarity --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.