{"id":"d6fb048c-2bb1-4fc2-ae40-4eba53071aec","arxiv_id":"2608.08793","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Three coding-agent adapters reconstruct the same 126 controlled Skill executions with incompatible semantics, so Agent-Skill observability must report evidence grades, clean specificity, and boundary attribution separately from event presence.","lead":"This paper presents Skill Runtime Intelligence, a passive observability system that reconstructs how AI coding agents load and run Agent Skills while separating verified facts from model guesses. A 126-run controlled benchmark shows that the same Skill executions appear clean, invisible, or failing depending on which agent adapter is used, so runtime logs alone cannot certify that a Skill worked.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Oracle-boundary convention: the 0/18 Panorama clean Exact score depends on the gold convention that a clean boundary is 'none', not 'outcome'; the central 'exactness' comparisons are convention-sensitive.","rationale":"The reader's weakest_assumption identified the gold-oracle boundary convention as the central premise, citing Sections 4.2 and 4.3. I agree, and I locate the exact point of failure: the clean-row convention for the Exact metric, Section 5.2 and Table 5. The concern is not that the authors are wrong about clean specificity—Panorama's 0/18 clean failure-status false positives versus Raw's 18/18 is a robust observational difference. The concern is narrower: the paper's headline comparison of composite Exact scores across views is sensitive to the label convention the authors chose for clean executions, and because the known-rule graph shares the same contract as the gold labels, the Exact metric is partly circular as a measure of diagnostic quality. The paper does hedge repeatedly and retains failed gates, and the 126/126 conformance caveat is explicit in Section 5.2 and Section 8. The single-run-per-cell and 228/378 incompleteness are acknowledged limits, not hidden flaws. The missing public artifact locator in the text is a real reproducibility concern but secondary to the convention sensitivity. On balance, conditional acceptance remains appropriate; the alternative-convention ablation is a modest but decisive check that should be requested before publication.","tokens_in":10396,"tokens_out":1879,"duration_ms":15797,"concrete_test":"Re-score the 18 clean cases under an alternative gold convention that treats a clean verified-success run as a valid 'outcome' boundary (or equivalently, drop the boundary distinction from Exact and compare only status+entailment). If Panorama's Exact becomes 82+18=100 while Raw and Semantics-matched Raw remain at 72 and 49 on clean cases, the exactness advantage is partly convention-relative. The decisive check is a second, pre-registered clean-boundary convention ('none' vs 'outcome') applied to the same frozen probe outputs, reporting the four-view exact counts under both conventions.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central RQ2 claim that Panorama is more exact than Raw/Semantics-matched Raw (82/126 vs 72/126 and 49/126) is driven by the clean-row convention. Section 5.2 states that Panorama, on all 18 clean cases, emits boundary 'outcome' while the gold convention is 'none', so Panorama scores 0/18 Exact on clean cases but 100/126 Status; Raw views emit a failure status on all 18 clean cases and therefore also 0/18 Exact. The 'Exact' metric therefore rewards whichever view happens to match the oracle's particular boundary convention for clean executions, not an independent ground truth about whether a clean run has a boundary. The authors partially acknowledge this in the conclusion-validity section, but the overall headline 'composite exact scores mask distinct errors' and the 82-vs-72/49 comparison in Table 5 are still presented as a diagnostic-quality difference, while a different plausible clean convention (e.g., treating 'verified success' as an explicit outcome boundary, or treating 'none' and 'outcome' as equivalent noise at the root) would change the exact-count ordering. The load-bearing assumption is in Section 4.3: 'Gold labels come from the frozen fault manifest and deterministic probe, not a model or human adjudicator.' The frozen probe is a nonce-bound JSON oracle; it verifies success or subprocess failure, but it does not by itself determine the lifecycle-boundary convention for a clean, successful run. That convention is imposed by the authors' rule set. Since the rule set and the gold labels share the same frozen fault contract (the known-rule graph scores 126/126 by construction), the degree to which Panorama's exactness advantage reflects the rule-set's own convention rather than an external diagnostic standard remains untested.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces Skill Runtime Intelligence, a passive observability system that reconstructs Skill lifecycle stages from agent telemetry using versioned adapters, a deterministic evidence graph, and four evidence grades. It reports a 126-cell benchmark across six frozen repositories, three coding-agent CLIs (Codex, OpenCode, Qoder), and seven clean or fault-injected conditions. The central empirical findings are: the three adapters expose three distinct semantics (no Skill runs; full run coverage but no failure-like events; failure-like events in every operational-failure and clean cell); in a seven-template diagnostic study, a normalized Panorama view achieves higher exact/status counts than raw model views, while a known-rule graph conforms to 126/126 frozen contracts by construction; an independent DeepSeek backend fails its completion gate; and secondary stress tests bound the role of model-generated diagnoses. The paper repeatedly and explicitly cautions that these are controlled mechanism observations, not estimates of population rates, natural incident prevalence, or human diagnostic benefit.","tokens_in":10805,"tokens_out":11490,"duration_ms":120720,"significance":"If the adapter-semantics finding holds, it is a concrete, controlled demonstration that the presence or absence of failure-like events in agent telemetry is not by itself evidence of Skill boundary fidelity, and it motivates per-version adapter qualification. The four-grade evidence contract and the separation of a deterministic graph layer from an Inferred model layer are useful design contributions for runtime observability of agent skills. The paper is unusually transparent: it retains failed gates instead of retrying them, binds fixtures and reports by digest, and explicitly states the circularity of the known-rule conformance result and the single-run-per-cell limitation. The existence-level claims, such as Qoder emitting failure-like events in clean cells, are credible from the reported frozen executions. The quantitative diagnostic comparisons, however, are convention-sensitive and rest on single executions, so the headline exactness claims need additional robustness analysis before the conclusions can be taken as general statements about diagnostic quality.","major_comments":[{"comment":"The exactness advantage attributed to Panorama (82 vs 72 Raw and 49 Semantics-matched Raw) is partly determined by the gold-label boundary convention for clean executions. The frozen nonce-bound verifier establishes success or subprocess failure, but it does not by itself fix whether a clean execution's lifecycle boundary should be labeled 'none' or 'outcome'; this convention is imposed by the author rule set described in §4.3. The text acknowledges that Panorama scores 0/18 Exact on clean cases only because it emits 'outcome' rather than the frozen 'none' convention. Because all compared views score 0/18 Exact on clean cases, the convention does not change the sign of the Raw-versus-Panorama ordering, but it changes the magnitude of the reported differences and makes the '10 more exact diagnoses' statement in §5.2 convention-relative. Please report clean and non-clean exact counts separately and provide a sensitivity analysis that treats 'none' and 'outcome' as distinct or equivalent, so readers can see the extent to which the headline exact-count comparisons are artifacts of the clean-boundary convention.","section":"§5.2, Table 5"},{"comment":"The central taxonomy of 'three distinct adapter semantics' is built from exactly one execution per agent-repository-condition cell. The counts 0/24, 24/24, and 6/6 are observed frequencies over single samples, not estimates of adapter capability under repeated executions; if agent behavior is stochastic, repeated runs could change the taxonomy at least in degree. The paper's 'mechanism coverage' framing is legitimate for the existence side of the claim, since a single clean-case false positive suffices to show that event presence is not boundary fidelity, but the universal statements about OpenCode and Qoder in §5.1 ('no failure-like events in the 24 operational failure cells,' 'all six clean cells') are stronger than the data support. Please either add repeated executions for a subset of cells, such as the six clean Qoder cells and a sample of operational-failure cells, or explicitly rephrase the findings as observations about these 126 sessions rather than about the adapters' semantics in general.","section":"§4.2, Table 4"},{"comment":"The Raw-versus-Panorama comparison varies both the representation and the prompt or rendering supplied to the model. The Semantics-matched Raw control addresses named lifecycle aliases, but it does not control for overall prompt structure, compactness, or formatting. The paper's own retained prompt-legend pilot reaching only 26/126 exact shows that superficially equivalent natural-language scaffolding changes exactness substantially. Consequently, the differences in exact and status counts between Raw and Panorama could be due in part to prompt formatting rather than to the evidence-grade model itself. Please state this residual confound explicitly in the RQ2 discussion, or add a control that presents the same lifecycle and evidence-grade facts with minimal formatting variation while varying only the presence of evidence-grade annotations.","section":"§4.3, §5.2"}],"minor_comments":[{"comment":"The sentence 'Gold labels come from the frozen fault manifest and deterministic probe, not a model or human adjudicator' should be expanded to state that the lifecycle-boundary convention for clean cases, such as 'none' rather than 'outcome', is part of the author-imposed manifest; otherwise readers may overestimate the independence of the gold labels.","section":"§4.3"},{"comment":"The Clean row contains '0:0' entries for Exact and Boundary that are easy to misread as a comparison of zeros; add a footnote explaining that both views fail the exact metric on clean cases for different reasons, namely failure status versus outcome boundary.","section":"Table 6(b)"},{"comment":"The first paragraph states that Panorama has '10 more exact diagnoses and 36 more correct boundaries than Raw' before the later caveat that the 126 rows instantiate seven dependent templates; consider moving the descriptive-counts caveat to the first mention of these totals.","section":"§5.2"},{"comment":"In the blinded v2 adjudication, the 19/19 Codex match and 11/19 Qwen match are reported without variance or alternative-rubric analysis; given the absence of human ground truth, add a sentence reiterating that these are consensus-support observations only and not independent validation.","section":"§5.4"},{"comment":"The abstract's sentence 'A known-rule graph conforms to 126/126 frozen contracts' could be read as validation; consider adding 'by construction' or 'as expected conformance' to the abstract, as the body already does.","section":"Abstract"},{"comment":"The statement 'The response gate fails: 122/126 responses match the oracle' is slightly confusing because it is immediately followed by the per-agent counts; clarify that the gate is defined as all 126 responses matching, so the four OpenCode failures cause the gate to fail.","section":"§4.2"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is transparent and methodical, and the core existence-level results are credible. The main risks are over-interpretation of the RQ2 exactness counts, which are convention-sensitive, and generalization of the adapter taxonomy from single-run cells; both can be addressed with sensitivity analyses and more careful language. The retention of failed gates, frozen manifests, and explicit circularity caveats are strengths. Scope-wise, the paper sits at the boundary of empirical software engineering, agent observability, and applied language-model evaluation; it may be a better fit for an SE or systems venue than for a core CL journal, but that is an editorial judgment rather than a technical defect."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a solid empirical systems paper, and the reader's conditional verdict is roughly right. The genuinely new result is that three versioned adapters reconstruct the same controlled Skill executions with three incompatible semantics—no Skill runs, complete runs with no failure events, failure-like events everywhere—and event presence does not tell you where a boundary is. That holds up. The benchmark is carefully built: 126 cells, frozen repositories, nonce-bound verifier, failed gates retained, deterministic graph separated from model inference. The 126/126 known-rule result is correctly labeled as conformance in Sections 5.2 and 8; only the abstract is slightly loose about it.\n\nWhat I credit: the RQ2 design with Raw and Panorama, plus template-stratified reporting instead of treating 126 rows as independent. The paper repeatedly says there is one execution per cell and no case-level significance, and it means it. The secondary stress tests are scoped as stress tests, not as real-world accuracy. The discussion of outcome and telemetry as two lanes is a genuinely useful conceptual point.\n\nSoft spots, in proportion. One run per cell means no variance estimates; the authors say so, but it caps how much weight the adapter-semantics typology can carry. The public artifact is claimed in Section 9 but I could not find a link or identifier in the text; for a paper whose selling point is frozen, auditable evaluation, that is a real but fixable reproducibility gap. The clean-boundary convention (gold says 'none', Panorama says 'outcome') is arbitrary and does affect status and clean-FP counts. The stress-test claim that it drives the exactness ordering is wrong: the clean row is 0/0 exact for both Raw and Panorama, and the 82-vs-72 difference comes from artifact/verifier versus execution/instruction conditions. So treat the clean-convention issue as minor, not load-bearing.\n\nWho it is for: people building agent observability, skill evaluation, or adapter qualification. This is a useful contribution to that subfield and worth a serious referee. I would send it to review. With a linkable artifact and a slightly sharper abstract distinction between conformance and accuracy, it would be close to accept.","headline":"A careful, well-scoped systems paper: the adapter-semantics finding is real and the benchmark is honestly hedged; the clean-run convention worry is real but does not drive the exactness comparison.","tokens_in":926,"tokens_out":861,"would_cite":true,"duration_ms":47200,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper shows that for Agent Skills, the presence or absence of a telemetry event cannot be read as proof that a Skill ran or failed, and proposes an evidence-graded reconstruction system to separate what is observed, derived, inferred…","keywords":["Agent Skills","runtime observability","Skill lifecycle","evidence provenance","coding agents","adapter qualification","event presence","deterministic reconstruction"],"falsifier":"Reconstruct the same frozen runs with the gold clean-case convention switched from 'none' to 'outcome' and check whether the Raw views' 18/18 clean failure-status false positives collapse to 0/18; if they do, the reported Panorama advantage is partly label convention. Alternatively, inspect real (non-injected) agent runs with human-annotated boundary status: if the adapter that emitted failure-like events in every clean injected case shows clean-case specificity on real runs, the three-semantics generalization fails.","tokens_in":10179,"feed_emoji":"🧩","tokens_out":6773,"duration_ms":61961,"temperature":0.7,"pith_summary":"This paper argues that existing traces of coding-agent runs—session-, model-, or tool-centric—cannot tell you whether a dynamically loaded Agent Skill actually ran as intended, and proposes a passive runtime-intelligence system that reconstructs Skill-lifecycle stages while tagging unsupported stages as unknown. To test it, the author froze six repository profiles, three coding agents, and seven clean or fault-injected conditions, giving 126 controlled executions. The central finding is that three versioned telemetry adapters expose three qualitatively different semantics: one reconstructs no Skill runs at all, one reconstructs complete runs but no failure-like events even when faults are injected, and one emits failure-like events in every cell, including clean ones. From this the author concludes that event presence is not boundary fidelity: coverage, clean specificity, attribution, and localization must be reported separately, and deterministic graph facts must not be overwritten by model explanations. A sympathetic reader would care because Skills are becoming a standard packaging unit for agent capabilities, and knowing whether a Skill 'ran' is a prerequisite for trusting or fixing it.","feed_headline":"Three agent telemetry adapters give three different Skill-run semantics","feed_subtitle":"A 126-run frozen benchmark shows event presence is not faithful boundary fidelity.","key_machinery":"The Run Panorama: a normalized view in which each lifecycle stage (Request, Discovery, Activation, Instructions, Resources, Execution, Artifacts, Outcome) is reconstructed from a four-grade evidence contract—Observed (directly present in a source record or external verifier), Derived (deterministic transformation), Inferred (uncertain model or heuristic), Experimental (controlled-trial estimate). The Panorama is carried by a deterministic evidence graph that assigns edges under fixed precedence (source parent/child ID, explicit Skill attribution, active Skill scope, exact artifact path, temporal adjacency, then model suggestion), keeps conflicting equal-priority relations as ambiguous, and emits a finding only when an observed failure or an evaluable expected signal establishes a boundary. Unsupported stages stay unknown. The machinery's job is to make the difference between 'an event happened,' 'an event was assigned to a Skill,' and 'the Skill caused the outcome' explicit, so that absence of telemetry is not read as failure and an agent assertion is not read as a verified result.","core_discovery":"On the paper's own terms, the discovery is a measurement failure made visible by controlled instrumentation. Across 126 frozen executions, an adapter for one agent (Codex) reconstructs no Skill occurrences (0/42) despite the agent returning exact nonce-bound responses, an adapter for a second agent (OpenCode) reconstructs all 42 Skill runs but emits no failure-like events in any of the 24 injected operational-failure cells, and an adapter for a third agent (Qoder) emits at least one failure-like event in all 24 failure cells and in all six clean cells, while localizing only six of the 24 injected boundaries. Because the events are not nonce-attributed, the 24/24 is a co-occurrence count, not injected-failure detection. The author generalizes this to the claim that event presence cannot be interpreted as faithful boundary semantics; that verified outcomes and runtime lifecycle evidence are two independent lanes; and that composite exact scores conceal distinct error types. The design corollary is that each adapter–agent release should be qualified by a frozen lifecycle matrix covering coverage, clean specificity, attribution, and exact-boundary accuracy.","pith_inferences":["The three-semantics result is a statement about these three adapter versions, not about the agents generically; a natural follow-up would be to rerun the same frozen matrix after a version upgrade of any one adapter to see whether the semantics are stable or drift.","The clean-case gap between Raw and Panorama may be partly a label-convention artifact: if the gold convention for clean cases were 'outcome' rather than 'none', the Raw views' 18/18 clean failure-status prediction could drop to 0/18, shrinking the reported panorama advantage.","The single-run-per-cell design leaves open whether the third adapter's 24/24 co-occurrence is a stable instrumentation bias or an artifact of an eventful environment; a repeated-run extension would separate those.","The 126/126 known-rule-graph conformance is expected by construction, so the productive comparison is not graph-vs-gold but how quickly a reviewed Inferred pattern can be graduated into a versioned rule and then verified on fresh frozen contracts."],"forward_implications":["Telemetry adapters must be qualified per agent version against a frozen lifecycle matrix; an untested version should start as 'unsupported' rather than inheriting an older capability badge.","Diagnosis should rely on a deterministic graph baseline, with model explanations stored as Inferred records that cannot promote or overwrite deterministic facts.","Verified outcomes and runtime lifecycle evidence should be displayed side by side, with no backfilling of an observed outcome as a lifecycle event or vice versa.","Reports should show coverage, clean specificity, attribution, and boundary localization separately, because a single coverage percentage or exact score hides the three distinct error modes.","A reviewed, recurring Inferred pattern should be promoted into a versioned deterministic rule with a regression fixture."],"supporting_citations":[{"why":"Defines Agent Skills directory structure, SKILL.md metadata, and progressive disclosure that the paper's lifecycle targets.","marker":"[1]"},{"why":"Provides the executable frozen-repository plus external-oracle methodology the 126-cell benchmark adapts from patch correctness to lifecycle evidence.","marker":"[7]"},{"why":"Supplies the provenance vocabulary (entities, activities, derivations) that motivates the epistemic separation of evidence grades.","marker":"[11]"},{"why":"Defines the agent/tool span conventions that the system extends with a Skill-domain layer and interoperates with via OTLP/HTTP export.","marker":"[12]"},{"why":"Uses an LLM judge as the final localizer, the design the paper explicitly contrasts with its deterministic graph baseline.","marker":"[2]"},{"why":"Measures whether trajectories cover Skill instructions, the complementary unit the paper distinguishes from a single Skill occurrence.","marker":"[14]"}],"fun_headline_variants":["Agent telemetry adapters disagree on Skill runs in all 126 tests","Three adapters, three Skill-lifecycle semantics across 126 frozen runs","Event presence isn't boundary fidelity: 126 runs show adapter mismatch","Skill-run semantics vary wildly across three agents in 126 controlled tests","Adapter semantics for Skill runs vary across 126 frozen executions"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark assumes that the frozen fault manifest plus the nonce-bound external verifier is a valid gold oracle for when a lifecycle boundary occurred and what status convention applies, and that one execution per agent–repository–condition cell is enough to characterize adapter semantics.","fun_headline_variants_meta":{"raw":{"variants":["Agent telemetry adapters disagree on Skill runs in all 126 tests","Three adapters, three Skill-lifecycle semantics across 126 frozen runs","Event presence isn't boundary fidelity: 126 runs show adapter mismatch","Skill-run semantics vary wildly across three agents in 126 controlled tests","Adapter semantics for Skill runs vary across 126 frozen executions"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000745,"raw_usage":{"total_tokens":3370,"prompt_tokens":1044,"completion_tokens":2326,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":660,"completion_tokens_details":{"reasoning_tokens":2235}},"tokens_in":660,"tokens_out":2326,"duration_ms":17634,"temperature":1.0,"reasoning_tokens":2235,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-14T04:23:07.867010+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Reconstruct the same frozen runs with the gold clean-case convention switched from 'none' to 'outcome' and check whether the Raw views' 18/18 clean failure-status false positives collapse to 0/18; if they do, the reported Panorama advantage is partly label convention. Alternatively, inspect real (non-injected) agent runs with human-annotated boundary status: if the adapter that emitted failure-like events in every clean injected case shows clean-case specificity on real runs, the three-semantics generalization fails.","supporting_citations":[{"cited_title":"https://agentskills.io/specification (2026), accessed 2026-08-01","cited_arxiv_id":null,"evidence_quote":"Defines Agent Skills directory structure, SKILL.md metadata, and progressive disclosure that the paper's lifecycle targets."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the executable frozen-repository plus external-oracle methodology the 126-cell benchmark adapts from patch correctness to lifecycle evidence."},{"cited_title":"W3c recommendation, World Wide Web Consortium (2013),https://www.w3.org/TR/prov-dm/","cited_arxiv_id":null,"evidence_quote":"Supplies the provenance vocabulary (entities, activities, derivations) that motivates the epistemic separation of evidence grades."},{"cited_title":"https: //github.com/open-telemetry/semantic-conventions-genai (2026), accessed 2026-08-01","cited_arxiv_id":null,"evidence_quote":"Defines the agent/tool span conventions that the system extends with a Skill-domain layer and interoperates with via OTLP/HTTP export."}],"review_version":1}