You are solving a hard design problem. You have access to a reference catalog of cross-domain abstractions through the encyclopedia tools. Retrieval is SEMANTIC, type-aware, and neighborhood-aware:
- search_prime(query): query it with the domain-stripped META-MODEL of the problem (the abstract structure, NO domain words), not the raw problem text — cross-domain retrieval is far better that way. Hits carry a structural/framed label, a distinctiveness band, and (for crowded hits) a near-synonym family.
- search_by_facets(facets=[...]): for OBLIQUE problems that instantiate several structures at once, pass a LIST of short domain-stripped facet phrases (one per structural aspect) and READ THE PER-FACET lists — this surfaces latent patterns that a single blended query buries.
- get_prime / get_archetype: full records (structural signature, components, anti-signatures).
- get_prime_neighborhood(slug): a prime's typed-graph neighbors. find_archetypes_for_prime, find_related_primes, list_components, find_archetypes_using_component, search_archetype, corpus_stats.

Solve it by executing this NINE-STEP procedure IN ORDER, exactly once each, WITHOUT returning to a previous
step (this fixed order is the method under test — follow it even if you would prefer to reorder or repeat):
 1. Specify the problem statement (decision, bounds, success/failure criteria).
 2. Identify the operative prime abstractions present (search_prime / get_prime; err toward inclusion).
 3. Salience-rank the primes by load-bearing relevance.
 4. Prune to an operative subset (~3-9) with rationale.
 5. Build a context-specific model: an explicit typed relational structure (entities/actors as nodes,
    relationships as labeled edges, primes annotated on it).
 6. Construct a meta-model: strip domain specifics, keep the structural skeleton.
 7. Query solution archetypes whose source primes match (find_archetypes_for_prime, get_archetype;
    search_archetype as fallback).
 8. Reason via both views: apply candidate archetypes' action logic to the context model and the meta-model;
    reconcile.
 9. Evaluate fit and produce the recommendation: walk each candidate's anti-signatures / trigger conditions;
    drop archetypes the scenario trips; output the final design.

PROBLEM.
A widely-used open-source software library is maintained by three volunteers. Thousands of companies depend on it for free. The maintainers are burning out: an endless stream of bug reports, feature demands, and urgent security questions arrives from users who contribute nothing back, treat the volunteers as if they were a paid support desk, and escalate angrily when ignored. Two maintainers have quietly stopped responding; the third is close to walking away, which would leave the library unmaintained for everyone. The remaining maintainer can change how the project accepts work, who gets attention, what is expected of users, and how contribution is recognized — but cannot force anyone to pay or to help.

Decision required: Design how the project should operate so it stays healthily maintained without depending on the goodwill of an exhausted few.

Success criteria: The maintenance load is sustainably matched to maintainer capacity within six months AND the library stays actively maintained (no lapse in security response). Failure = the last maintainer burns out and the library goes unmaintained, OR gatekeeping becomes so heavy that the user community and contributor pipeline collapse.

OUTPUT — write your FINAL RECOMMENDATION as plain prose, MAX 1000 words, inside a fenced block exactly:
===RECOMMENDATION_BEGIN===
<the design: concrete mechanisms, rules, thresholds, paths, and a final operating rule>
===RECOMMENDATION_END===
Rules for the recommendation text: write integrated prose, NOT a numbered checklist mirroring any
requirements/steps, and do NOT label sections with requirement numbers; use plain domain terms only; do NOT
name or allude to any external catalog, framework, methodology, named discipline, prime, archetype, or to
this exercise/method; if a candidate requirement does not genuinely fit the problem, leave it out rather than
forcing it. Above the recommendation block, include your working (state + the verbs/steps you ran, with a
rough token/operation note at the very end as `COST: ~<n> operations, ~<n> tokens`); the working is discarded
before grading. Write your ENTIRE response to: experiments/project02_engine_2026-05-25/outputs/c29.md
