{"id":"7dd27d48-55dd-45d0-a0fc-bb9635ef46c5","arxiv_id":"2608.04562","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"SkillSV estimates the value of each internal component of an agent skill using structure-aware Shapley rollouts, recovering aggregate skill lift and supporting safe pruning on four benchmarks.","lead":"SkillSV, a structure-aware Shapley framework, values the individual rules, examples, and scripts inside an LLM agent's skill file, scoring only structurally valid counterfactuals and separating content value from context cost. A generalist might read it because automated skill optimization is becoming common in agentic systems, and this offers a concrete way to see which parts of a skill actually matter.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim that only valid counterfactual skills are scored rests entirely on completeness of the compiled dependency graph D; missed semantic or heuristic dependencies silently turn deletion values into breakage values and change the estimand.","rationale":"The paper's central claim has several independent supports: synthetic known-value experiments (Fig. 4a), value closure (Table 2), and pruning utility (Figs. 5-6). The synthetic interaction recovery is genuine evidence for the method given a correct structure. The closure check is weaker than it first appears because, without truncation, the sum of chain-coupled marginals telescopes exactly to V(N)-V({m}) on each window in expectation; Table 2 therefore mostly validates truncation calibration rather than the truth of unit-level values. The truly independent correctness evidence, synthetic recovery and pruning utility, presupposes that the compiled dependency graph D faithfully represents the skill. That presupposition is the least secure part of the argument. A missed dependency changes not only which coalitions are scored but the support of the sampler and hence the estimand itself, so the failure mode is systematic rather than a wider confidence interval. The padding-neutrality assumption and the width of the compression confidence intervals are real but secondary: they affect the context-cost and 'lossless' subclaims, the paper already scopes rho_pad evidence to OfficeQA, and the pruning results use rho_del. The reader's weakest assumption identified the same dependency-compiler concern, and I agree with that identification. The proposed perturbation test is feasible, cheap relative to the paper's own rollout budget, and would either clear the concern or show that the abstract's 'only valid counterfactual skills are evaluated' claim requires explicit qualification. Because the paper is already CONDITIONAL, this concern does not change the recommended verdict; it sharpens the condition under which the central claim should be accepted.","tokens_in":21773,"tokens_out":5675,"duration_ms":66374,"concrete_test":"Build a controlled compiler-completeness probe: take one real optimized skill per benchmark and inject a semantic dependency that Table 5 cannot detect but that makes the surviving text invalid, for example unit A's code calls getattr(module, name) where name is defined only in unit B, or a lead-in phrase 'as shown in the following example' pointing to an example unit. Run SKILLSV with the original D and with the corrected edge added, using the same K=12, b=8, tau=0.05 configuration and the same task panel. If any unit's value changes by more than its 95% chain bootstrap confidence interval, or the pruning ranking of top/bottom units changes, the completeness assumption is consequential.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 3.1 defines feasible coalitions as downward-closed under the compiled dependency graph D, and the abstract and contribution (i) claim that 'only valid counterfactual skills are evaluated.' All downstream outputs, unit values, rankings, and pruning decisions are therefore conditional on D being complete. But Table 5's nine dependency rules are a fixed heuristic set: R3, R8, and R9 are explicitly labeled 'deterministic surface heuristic,' R4 (def-use) applies only to 'supported code blocks,' and Section 3.1 admits that semantic edges 'carry no syntactic marker and are recovered by analysing the code blocks themselves.' No completeness theorem, recall bound, or annotated validation of the compiler is reported. If an edge is missed, a coalition that removes a prerequisite while keeping the dependent unit is rendered and scored; its score drop is then read as a deletion effect rather than as breakage. Because D also defines the feasible-order sampler's support (Appendix D), a missing edge changes the estimand itself, not just the noise. The paper's own Table 5 note, stating that R3, R8, and R9 are ablated separately because their evidence is weaker, acknowledges the weak link, yet the main conclusions are not conditioned on any demonstrated recall of the compiler. This is the single most load-bearing assumption behind the paper's faithfulness, actionability, and explanation claims.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces SkillSV, a framework for assigning credit to the internal units (rules, examples, scripts, heuristics) of an agent skill. It compiles a skill into a dependency graph and hierarchy, defines a feasible-coalition game in which only dependency-closed subsets are scored, and estimates Shapley-style unit values via a budgeted chain-coupled estimator that pairs deletion with length-neutral padding to separate content effects from context-occupancy costs. On four agentic benchmarks, the paper reports that SkillSV recovers planted and real interaction patterns, that the sum of unit values approximately equals the measured content lift (0.95x-1.04x), and that attribution-guided pruning and compression retain performance while removing a substantial fraction of tokens.","tokens_in":22042,"tokens_out":8980,"duration_ms":91587,"significance":"If the results hold, SkillSV provides a practical method for intra-skill credit assignment that addresses a genuine gap: flat Shapley or ablation methods can score invalid or broken skill artifacts. The paper's formal setup is careful: the value is explicitly indexed by a declared feasible-order distribution mu, the sampler's full support is proved (Appendix D), and the estimator's unbiasedness without truncation is derived (Appendix E). The experimental design has strengths: disjoint attribution/confirmation panels, frozen agent snapshots, paired task windows for variance reduction, and explicit acknowledgment that the two-operator content/context decomposition is scoped to OfficeQA. However, the validity of the central counterfactual constraint depends on the unvalidated completeness of the dependency compiler, and the reported closure check and truncation audit have methodological gaps that need to be addressed before the faithfulness claims are fully supported.","major_comments":[{"comment":"The central claim that 'only valid counterfactual skills are evaluated' (abstract, contribution (i)) rests entirely on the completeness of the compiled dependency graph D. Table 5 lists nine fixed rules, three of which (R3, R8, R9) are labeled 'deterministic surface heuristic' and are only ablated separately, and R4 applies only to 'supported code blocks.' The paper provides no recall evaluation or completeness argument for D, and Appendix C explicitly admits that semantic edges 'carry no syntactic marker' and are recovered by code analysis. If a dependency is missed, the renderer scores an infeasible coalition (a dependent unit without its prerequisite) and attributes the resulting breakage to deletion; moreover, because D defines the support of the feasible-order sampler (Appendix D), the estimand itself changes. Please provide a validation of the compiler's recall, e.g., human-annotated dependency edges on a sample of skills, or a sensitivity analysis that adds/removes edges and reports the change in unit values and pruning decisions.","section":"3.1 / Table 5 / Appendix C"},{"comment":"The closure check compares the sum of estimated unit values, which are computed on Panel A, against the content lift Lambda = V(N) - V({m}) measured on a disjoint held-out panel (Table 2 note: 'we evaluated it on the disjoint panel of held outs for fair comparison'). However, Eqs. (2)-(3) define the value and the closure property with respect to the attribution panel T_A: the exact closure statement is sum_i phi_i(mu) = V_rho(N) - V_rho({m}) for the same panel. Using Panel B anchors means the check tests whether the random panel split made the two panels exchangeable, not whether the estimator preserves aggregate lift. The reported bootstrap CI for Lambda does not include the between-panel split variance, so the check marks are not evidence for the stated closure property. Please either compute Lambda on the same panel used for estimation or report a closure test that properly accounts for the split uncertainty.","section":"4.2 / Table 2 / Appendix F"},{"comment":"All reported unit values are produced with noise-gated truncation at tau = 0.05, which introduces bias that Appendix E.4 bounds only at the window-level aggregate suffix, not for individual marginals or for the final estimates. Appendix F states that the selected configuration was compared with an untruncated control run, but no results of that comparison are reported anywhere in the manuscript. Since the unbiasedness proof in Appendix E.2 holds only when truncation is disabled, the magnitude of the truncation bias in the reported unit values, rankings, and closure ratios is unquantified. Please report the control-run comparison (aggregate and per-unit discrepancies) for the four benchmarks, or at least for the synthetic planted games.","section":"3.3 / Algorithm 1 / Appendix F"}],"minor_comments":[{"comment":"The synthetic faithfulness experiment in Section 4.2 appears to use the same planted-value games on which the hyperparameters (b, tau) are calibrated (Appendix F). Please clarify whether the recovery shown in Fig. 4(a) is on the calibration set or on held-out synthetic skills, and if the latter, describe how the holdout was constructed.","section":"4.2 / Appendix F"},{"comment":"The check/cross marks in Table 2 are based solely on whether the bootstrap CI covers Lambda. For Spreadsheet, Closure-LOO has a point estimate of -1.00x (wrong sign) yet is marked with a check because its CI includes Lambda. CI coverage alone conflates statistical insignificance with calibration; please also report point-estimate closeness or a signed error to make the comparison transparent.","section":"Table 2"},{"comment":"The editor prompt and the revised skill artifacts used in the single attribution-guided refinement step (Table 3) are not provided. Including the full protocol, the exact editor instructions, and the revised skill files (or a repository link) would be necessary for reproducibility and for verifying that the compression followed the SkillSV report as claimed.","section":"4.3 / Table 3"}],"recommendation":"major_revision","confidential_remarks":"The formal parts of this paper are solid, and the proposed problem is well motivated, but the empirical validation needs substantial strengthening. The dependency-compiler recall is the main risk to the paper's central claim; if the authors cannot provide a recall validation, the abstract's wording 'only valid counterfactual skills are evaluated' should be softened to 'counterfactuals closed under the compiled dependencies.' The closure-check panel mismatch and the missing truncation control are fixable with additional analysis or reruns. I recommend major revision rather than rejection, because the core derivation is sound and the required additions are within the scope of a revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should read this one. Li et al. build a genuinely new valuation framework for agent skills: compile a skill into units, dependencies, and hierarchy, then define Shapley-style unit values over feasible insertion orders, and estimate them with paired deletion/length-neutral-padding rollouts under a strict budget. The formal core is clean—unbiased for the declared value, closure holds by construction, and the chain-coupled task windows are a neat variance-reduction trick. The appendix work is unusually thorough: they explicitly index the value by a non-uniform sampler, bound truncation error only at the window level, and scope the two-operator audit to one benchmark. That kind of honesty is rare.\n\nThe empirical story is plausible but not airtight. The planted-value experiments and the closure checks (0.95x–1.04x) support the method. The pruning curves show a real gain over LOO and the LLM judge. The weak link is the dependency compiler. The abstract and contribution (i) claim only valid counterfactuals are evaluated, but that rests entirely on the completeness of the compiled graph D. The nine rules include three explicit surface heuristics (R3, R8, R9), and semantic edges are recovered by analyzing code blocks with no reported recall or error bound. If an edge is missed, an infeasible coalition is scored and a deletion effect silently becomes a breakage effect. Worse, D defines the support of the feasible-order sampler, so a missing edge changes the estimand itself, not just the noise. The stress-test note is right on this point: it's the single most load-bearing assumption. The paper needs either a gold-standard validation of the compiler, a sensitivity analysis over plausible edge sets, or a softened claim.\n\nTwo smaller points. First, the placeholder-neutrality assumption behind content value vs. context cost is untested; the paper is honest about it but a simple probe (e.g., compare placeholders of different lengths) would help. Second, Table 3 calls the result 'lossless compression' while the 95% CIs include drops of 5–10 points on two benchmarks. 'No significant change' is defensible; 'lossless' is not. Also, no code or data link is provided; that should be fixed.\n\nOverall, this is a solid, well-scoped contribution to the LLM-agent subfield. It deserves a serious referee, but acceptance should be conditional on a dependency-completeness validation or explicit error bound, a placeholder-neutrality check, and softened claims. I'd bring it to reading group and would likely cite it once the compiler issues are resolved.","headline":"Worth reading and worth citing once the dependency compiler is validated: a clean Shapley framework for skill valuation with one load-bearing assumption.","tokens_in":22573,"tokens_out":3707,"would_cite":true,"duration_ms":76848,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"SkillSV prices every unit of an agent skill with structure-aware Shapley values","keywords":["agent skills","skill valuation","Shapley value","credit assignment","LLM agents","prompt compression","structure-aware valuation","cooperative games"],"falsifier":"Insert into a small, fully controlled skill a dependency of an uncatalogued kind—for example, a constant defined in one code block and used by name in another via string formatting—compile it, remove the defining unit alone, and run SkillSV; if the pruned skill still scores as if valid, the structure missed a real edge. Alternatively, enumerate all feasible orders of a small skill exhaustively, compute exact unit values, and compare with SkillSV's budgeted estimates: any systematic gap in the value-closure ratio beyond the stated truncation tolerance would falsify the estimator's unbiasedness.","tokens_in":1977,"feed_emoji":"🧩","tokens_out":2515,"duration_ms":111034,"temperature":0.7,"pith_summary":"Agent skills—the rules, examples, scripts, and heuristics an LLM agent carries into a task—are usually optimized as one opaque artifact, so nobody knows which internal piece earned the score. SkillSV treats this as a structure-aware cooperative game: it compiles a skill into units, dependencies, and a document hierarchy, evaluates only counterfactual skills that remain valid under those constraints, and pairs deletion with length-neutral padding to separate a unit's content value from its cost of occupying prompt context. On four agentic benchmarks the paper reports that summing SkillSV's unit values recovers the skill's true content lift (0.95 to 1.04 times), that the values identify truly redundant and complementary units where leave-one-out ablations fail, and that a single attribution-guided edit keeps 69% of tokens on average with no significant performance change. The intended upshot is that credit assignment for agent skills should be done at unit granularity and under structural validity, not by flat ablation.","feed_headline":"SkillSV prices every unit of an agent skill","feed_subtitle":"Structure-aware Shapley values add up to the real lift, letting a skill shrink to 69% of its tokens safely.","key_machinery":"The load-bearing object is the compiled skill graph $G=(N,D,H)$ with its feasibility family of coalitions downward-closed under $D$, together with the hierarchy $H$ that forces sibling units to be evaluated as contiguous blocks. Feasible insertion orders are linear extensions of this constrained structure; the sampler induces a declared distribution, so the value $\\phi_{i,\\rho}(\\mu)$ is a probabilistic value rather than a canonical uniform Shapley value. The paired operators make the renderer local—surviving units stay byte-for-byte unchanged—and the chain-coupled task window pairs every marginal with the same task list, cancelling task difficulty so small windows suffice. This is what lets the estimator run at roughly $\\gamma b/M$ of full cost while preserving tractable closure.","core_discovery":"The paper's central claim is that the value of a unit inside an agent skill is well-defined only once the artifact is compiled into a triple $G=(N,D,H)$—valuation units, dependency edges, and hierarchy—so that a coalition of kept units is feasible only if it is downward-closed in $D$ and evaluated in hierarchy-contiguous orders. Over that feasible-order space, a unit's value is its expected marginal contribution to the agent's verified held-out score, with a declared (generally nonuniform) distribution over orders; the trigger unit is valued separately. Two local rendering operators, one that deletes missing units and one that replaces them with length-matched neutral placeholders, separate content value from context-occupancy cost, and a chain-coupled task-window estimator computes these values under a fixed rollout budget with noise-gated truncation. Empirically the paper argues this recovers planted and real unit interactions, preserves aggregate skill lift, and supports pruning and compression that a flat leave-one-out or LLM-judge baseline cannot match.","pith_inferences":["Editorial inference: the compile-then-value recipe should transfer to any structured artifact whose parts refer to each other—code repositories, documentation sets, prompt libraries—as long as a deterministic compiler can enumerate dependencies; the nine rules here are tuned to Markdown plus scripts and would need extension.","Editorial inference: because the value is defined relative to the sampler's distribution, two implementations using different feasible-order samplers will report different unit prices for the same skill; the distribution is part of the estimand, so SkillSV reports should state it.","Editorial inference: the value-closure result suggests a cheap monitoring loop: periodically re-run SkillSV on a skill that is being auto-optimized, and compress or delete units that fall below the noise floor, rather than re-running full evaluation on every proposed edit.","Editorial inference: the context-cost decomposition rests on placeholders being truly neutral; if a masked span still leaks semantic cues, the reported content value and context cost would be entangled."],"forward_implications":["Summing SkillSV unit values recovers the measured content lift on all four benchmarks, with closure ratios between 0.95 and 1.04 times inside the bootstrap confidence intervals.","Redundant and complementary units are valued correctly: a redundant pair splits the shared credit, a complementary pair is rewarded for joint presence, while Closure-LOO assigns zero or double-counts.","Ranking units by ascending SkillSV value yields held-out pruning curves that sustain the full-skill score at higher pruning ratios than Closure-LOO, an LLM judge, or random deletion; pooled AUC gains are +0.026, +0.049, and +0.082 respectively, with no 95% confidence interval containing zero.","A single attribution-guided refinement step retains on average 69% of the original tokens with no significant performance change on any of the four benchmarks.","Value is concentrated: the top 10% of units account for 21% (OfficeQA), 35% (LiveMath), 60% (SpreadsheetBench), and 100% (ALFWorld) of the total value mass, which is why low-value pruning is safe."],"supporting_citations":[{"why":"Defines the Shapley value whose marginal-contribution form is the basis of unit value.","marker":"[Shapley, 1953]"},{"why":"Probabilistic values justify indexing the value by a declared order distribution.","marker":"[Weber, 1988]"},{"why":"Level-structured cooperation supplies the hierarchy-contiguity constraint.","marker":"[Winter, 1989]"},{"why":"PC-Winter is the closest prior combining precedence and hierarchy constraints, which SkillSV extends.","marker":"[Chi et al., 2025]"},{"why":"Data Shapley supplies permutation sampling and truncation ideas, plus the flat-valuation baseline the paper contrasts.","marker":"[Ghorbani and Zou, 2019]"},{"why":"Leave-one-out deletion, the baseline SkillSV shows is miscalibrated for redundant and complementary units.","marker":"[Cook, 1977]"},{"why":"LiveMath benchmark, one of four held-out evaluations.","marker":"[He et al., 2026]"},{"why":"OfficeQA benchmark, one of four held-out evaluations, and the two-operator audit.","marker":"[Opsahl-Ong et al., 2026]"},{"why":"SpreadsheetBench benchmark, one of four held-out evaluations.","marker":"[Ma et al., 2024]"},{"why":"ALFWorld benchmark, one of four held-out evaluations.","marker":"[Shridhar et al., 2021]"}],"fun_headline_variants":["SkillSV values agent skill parts by structure","Shapley values for agent skill units, structure-aware","Assigning credit to units in agent skills","SkillSV: fair prices for agent skill components","Structure-aware valuation of agent skill internals"],"cache_read_input_tokens":24704,"weakest_assumption_plain":"The compiler's nine dependency-extraction rules must recover every relation whose violation makes a counterfactual skill invalid; if a real dependency is missed, infeasible coalitions are scored and unit values silently mix deletion with breakage.","fun_headline_variants_meta":{"raw":{"variants":["SkillSV values agent skill parts by structure","Shapley values for agent skill units, structure-aware","Assigning credit to units in agent skills","SkillSV: fair prices for agent skill components","Structure-aware valuation of agent skill internals"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000546,"raw_usage":{"total_tokens":2605,"prompt_tokens":936,"completion_tokens":1669,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":552,"completion_tokens_details":{"reasoning_tokens":1599}},"tokens_in":552,"tokens_out":1669,"duration_ms":12439,"temperature":1.0,"reasoning_tokens":1599,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T21:59:23.206003+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Insert into a small, fully controlled skill a dependency of an uncatalogued kind—for example, a constant defined in one code block and used by name in another via string formatting—compile it, remove the defining unit alone, and run SkillSV; if the pruned skill still scores as if valid, the structure missed a real edge. Alternatively, enumerate all feasible orders of a small skill exhaustively, compute exact unit values, and compare with SkillSV's budgeted estimates: any systematic gap in the value-closure ratio beyond the stated truncation tolerance would falsify the estimator's unbiasedness.","supporting_citations":[],"review_version":1}