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Kang Ruiyuan

Publications and source records attributed to Kang Ruiyuan.

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Memory-Skill Isomorphism: One Skill Carrier, Two Native Uses

Memory and skills improve agents without changing weights: memory carries prior experience, skills carry reusable procedures. Wrapping both in stores, routers, retrieval, reflection, and update paths makes reuse machinery grow with accumulation. Part of this duplication need not be rebuilt: a Skill is already a natural carrier for distilled memory. Here the memory component is a Skill: resident description holds hot cues, on-demand SKILL.md a colder index and curation policy, and reference/*.md files detailed memories (levels L0 -> L1 -> L2). A governed 1,024-character description budget keeps the resident index compressed. Memory and capability thus share one progressive-disclosure carrier; reflection evolves either Skill. Writes fork: history appended, current state rewritten and revalidated. In one deployed system, the sharpest identification is governance: 4 write entries exist, but only 1/4 reaches the settlement ledger. At the priced operating point, one L1 lesson-1 point adds 1,313 first-turn tokens against a 1,462-token baseline--tied at k=1 (1,313 versus 1,365), 4,949 at k=5; session totals differ by only 1.18x with overlapping last-turn ranges; price decomposition is unavailable. On one selected task with one model, exposing the lesson means fewer failures (0/8 or 1/8 with the lesson versus a shared non-concurrent 6/6 historical floor, unadjusted for multiplicity). The task was selected on prior floor evidence, so this is a selected-task post-selection existence signal, not a confirmatory rate. Resident and BM25 show no detected difference in two small comparisons. This motivates a candidate RSI design rule: one Skill carrier family, a shared read side, governed distinct writes. Body delivery h := P(D|A) is uninstrumented in RQ1--RQ2; this evaluates a resident-index implementation.

cs.SE

SkillSeam: Six Principles for Auditing Agent Skill Collections

A folder of competent skills is not yet a reliable system. Skills rarely fail alone; they fail at the seams of a collection. As an agent's skill library grows, procedures compete for attention, aliases double-load, boundaries blur, and poorly sized skills turn routing errors into task failures. We introduce SkillSeam, a method that audits the relationships through which individual skill files become a system. It maps each collection-level principle to a failure mechanism, its strongest observable, and a controlled perturbation test. From one sealed skill system, SkillSeam perturbs six design principles: persistence gradient, system coherence, regime gating, orthogonal coverage, flow, and granularity discipline. Crucially, each principle is evaluated through the channel its failure mechanism predicts rather than through accuracy alone. Flattening the persistence hierarchy increases loaded-skill tokens by 60%; a dangling anchor raises total tokens by 64% and shifts accuracy by -3.1pp; with skill count and context size held fixed, replacing an unrelated control with a synonymous alias raises noncanonical routes from 0/32 to 15/32 and flips half of matched paraphrase pairs; in a candidate-ownership audit, overlapping lanes raise reported ownership conflicts from 0/16 to 14/16; bland triggers drive routing conflicts from 3/32 to 30/32 and inflate loaded-skill tokens 3.7x; and one granularity mis-mix produces the largest accuracy drop, -12.5pp. These outcomes turn six pieces of authoring advice into testable system properties without treating every probe as confirmation. We release the byte-differenced variants, task slices, rollups, and a one-screen design checklist so that other skill systems can measure the same failure channels.

cs.SE