The supplied results describe emerging benchmarks and contrastive tests for agent “skills”: instruction packages that coding agents retrieve or load to guide task execution. Reported findings suggest skills can reduce accuracy, slow models, or fail under autonomous selection and distractors, while an automated triage workflow reportedly classifies functional failures and efficiency regressions with 93.6% and 79.7% accuracy respectively. However, the snippets do not establish what the Show HN project “Skill Grader” specifically does, who built it, or whether its own independent testing has produced results; they support the broader hypothesis rather than the particular launch.
2026-09-21T23:25:00Z
A practitioner supplies a concrete alternative explanation for improvement without Skill-tool invocation: startup descriptions can influence execution, and their own description-only edit reportedly raised selection recall from 46% to 67%. This sharpens the required evaluation controls but neither verifies exposure in the disputed run nor corroborates bloat-induced harm; cross-platform spread and the expanding mitigation ecosystem sustain medium attention without evidence of episode-dominating uptake.
2026-09-20T17:24:11Z
A new contrastive test reports its largest improvement for a skill never invoked through the Skill tool, making attribution—not just score calibration—a concrete concern for automated grading. This is a substantive result from an existing evaluator, not independent corroboration of bloat-induced harm; expanding mitigation tooling and cross-platform spread sustain medium attention without establishing episode-dominating uptake.
2026-09-20T17:23:40Z
evidence attached: reddit.post.1wlm16a — The controlled with-and-without test exposes attribution and grading confounds directly relevant to evaluating whether agent skills actually improve performance.
2026-09-19T19:29:11Z
Agent Dispatcher adds a reported implementation for selective routing of roles, skills, tools and context, expanding the mitigation ecosystem beyond linting and pruning without demonstrating that bloat causes harm or that grading predicts it. This new implementation, alongside the cross-platform spread reading, warrants medium attention; the supplied evidence does not show episode-dominating uptake or independent performance validation sufficient for high heat.
2026-09-19T19:21:48Z
evidence attached: reddit.post.1wku51x — The released dispatcher provides a concrete routing response to the open question of whether excessive skills and instructions harm agent selection and performance.
2026-09-19T17:25:20Z
The latest anecdote concerns generated prompts drifting into excessive orchestration, not a tested skill-selection or grading failure; it adds a possible confound rather than corroborating bloat-induced harm. Despite the magnitude-valve spread reading, the supplied delta shows repetitive discussion in an existing community, not new implementations, independent validation or widening consequential uptake, so attention remains low.
2026-09-19T17:23:17Z
evidence attached: reddit.post.1wkqgqo — The anecdote reports instruction bloat changing an agent's objective, relevant context for the open skill-bloat hypothesis.
2026-09-18T21:22:32Z
The latest synthesis points to potentially useful with/without-skill benchmarks, but its quoted gains are secondary claims without supplied methods or results to inspect. It reinforces the need to distinguish useful skill content from retrieval failures and instruction bloat, rather than establishing bloat-induced harm or validating automated grading.
2026-09-18T21:22:01Z
evidence attached: reddit.post.1wk1ry1 — The post synthesizes benchmark evidence that skills can help substantially but are often not retrieved, directly contextualizing the open question of skill selection and instruction bloat.
2026-09-17T19:22:48Z
The new public-repository lint survey claims widespread triggering problems, but the supplied evidence does not show runtime testing or validation that its flags predict failures. Its tool-permission findings concern a separate configuration risk, not evidence that instruction bloat impairs performance; the case remains uncorroborated.
2026-09-17T19:21:49Z
evidence attached: hn.story.49744398 — This provides a directly relevant independent audit of skill triggering, inherited tools, and least-privilege failures.
2026-09-16T11:27:28Z
The latest workflow account repeats the distinction between advisory instructions, explicit skill loading and CI-enforced requirements; it provides no comparative result linking instruction bloat to failures. It does not validate automated grading or justify treating a rule-heavy configuration as inherently harmful.
2026-09-16T11:22:12Z
evidence attached: reddit.post.1whttt3 — The setup offers workflow evidence that explicit skill loading, CI enforcement, and planning/review layers affect coding-agent reliability and context use.
2026-09-14T15:32:05Z
Skillzero adds a reported context-pruning tool and highlights the overhead of globally installed skill names and descriptions, but the supplied excerpt shows neither measured savings nor improved selection or task performance. This is another mitigation proposal, not independent validation of bloat-induced harm or automated grading.
2026-09-14T15:23:17Z
evidence attached: hn.story.49698184 — The released tool provides practical evidence that selectively omitting skills can reduce context overhead and improve skill selection.
2026-09-14T10:21:50Z
The latest proposal shifts attention from static lint scores toward field telemetry, but its author's command logging does not establish skill-level task success or causal benefit. Without validated attribution and with/without-skill comparisons, a field pass-rate badge would not resolve whether instruction bloat harms performance or whether grading detects it.
2026-09-14T10:21:26Z
evidence attached: reddit.post.1wfzaz5 — The proposed opt-in real-run pass-rate telemetry materially informs how agent skills could be evaluated beyond static linting and lab benchmarks.
2026-09-13T22:27:15Z
COBRA-Skills introduces a budget-allocation approach to skill optimization, but the supplied announcement contains neither inspectable implementation details nor comparative results. It broadens the adjacent tooling landscape without establishing bloat-induced harm or validating automated grading.
2026-09-13T22:21:38Z
evidence attached: reddit.post.1wf8b61 — The released COBRA-Skills artifact materially informs the open question of how agent skills should be selected and optimized under evaluation budgets.
2026-09-11T23:21:51Z
The reported Anthropic evaluation CLI adds a potentially consequential first-party entrant to skill testing, but the supplied evidence is only an HN title and a supportive comment, not documentation or results. It strengthens the prospect of practical evaluation tooling without establishing that instruction bloat causes regressions or that automated grading detects them reliably.
2026-09-11T23:21:27Z
evidence attached: hn.story.49666190 — Anthropic's first-party evaluation CLI is a concrete artifact for testing whether skills and plugins are reliable and safe to select.
2026-09-11T03:25:20Z
The reported 46.3% skill-selection recall adds a concrete measurement target, but the author's detector bug and unresolved cross-harness comparability make it evidence for auditing invocation telemetry, not proof that instruction bloat causes failures. Skillctl adds an audit-tool announcement, while the verbosity discussion supplies no test; neither validates automated grading against task regressions.
2026-09-10T22:22:56Z
evidence attached: reddit.post.1wcwzgy — Practitioner discussion directly bears on whether verbose skill instructions help or hinder agent behavior, though it is only anecdotal evidence.
2026-09-10T16:23:23Z
evidence attached: reddit.post.1wcml4h — The measured 46.3% skill recall and detector failure provide concrete evidence about skill selection and evaluation pitfalls.
2026-09-10T15:26:00Z
evidence attached: hn.story.49644606 — The released auditor directly bears on whether bloated or conflicting agent skills impose measurable context cost and selection problems.
2026-09-10T12:28:26Z
The refreshed discussion adds conflicting preferences about self-review, not evidence establishing the purported Anthropic recommendation or its scope. This remains repetitive amplification: neither causal harm from instruction bloat nor automated grading’s ability to predict that harm has gained validation.
2026-09-10T11:31:08Z
The refreshed discussion repeats disagreement about self-review and familiar harness-auditing advice, without establishing the purported Anthropic recommendation or adding measured results. It does not change the distinction between generic effort prompts, useful task-specific instructions, and externally enforced verification, nor validate automated detection of harmful bloat.
2026-09-10T10:28:12Z
Refreshed comments dispute the blanket anti-pattern framing and distinguish self-review, separate-agent review, and enforceable checks, but add no primary guidance or measured comparison. They do not establish that generic effort prompts cause harm, advance the instruction-bloat hypothesis, or validate automated grading.
2026-09-10T09:25:02Z
The new report suggests that generic effort prompts may become counterproductive as models change, but its unlinked paraphrase of Anthropic guidance establishes neither the scope nor measured effects. It sharpens a candidate ablation—effort nudges versus task-specific requirements and external verification—without corroborating instruction-bloat harm or validating automated grading.
2026-09-10T09:22:38Z
evidence attached: reddit.post.1wcdisq — The reported Anthropic guidance and concrete examples support the hypothesis that instruction-heavy agent configurations can impair quality and increase cost.
2026-09-08T23:25:52Z
Skillsaw’s expansion from claudelint adds a concrete cross-ecosystem linting tool, making automated context hygiene more implementable. Static structural and content checks are not evidence that a grader predicts task regressions, however; the announcement supplies neither demonstrated catches nor controlled tests of instruction bloat.
2026-09-08T19:24:37Z
evidence attached: hn.story.49613638 — Released linter provides a concrete artifact for detecting structural and content problems in agent skills and instructions.
2026-09-08T15:38:01Z
The frontend-design discussion raises model-version dependence as another reason to re-evaluate skills, but adds only a question and subjective reports of little benefit—not a measured regression. Refreshed comments do not advance the causal bloat claim or validate automated grading.
2026-09-08T15:23:05Z
evidence attached: reddit.post.1war282 — The question directly bears on whether increasingly detailed skills improve or impair newer agents, though it supplies no independent measurements.
2026-09-07T05:27:03Z
The refreshed video-editing discussion adds questions and enthusiasm, not independent usage results or measurements. It leaves intact the distinction between useful accumulated domain guidance and harmful instruction bloat, without advancing the causal claim or validating automated grading.
2026-09-06T18:29:43Z
The video-editing skill adds a concrete builder account of accumulated domain instructions encoding useful workflow judgment, reinforcing that instruction volume alone is not a reliable proxy for harm. It provides no controlled comparison, independent performance measurement, or grader validation, so the causal and automated-detection claims remain unsettled.
2026-09-06T18:22:44Z
evidence attached: reddit.post.1w935ob — The released Claude Code skill offers practical evidence that accumulated, domain-specific instructions can encode workflow quality, relevant to the open skill-bloat hypothesis.
2026-09-04T23:31:31Z
The new skill pack is another implementation of packaged context-engineering guidance, but exposes no controlled comparison, selection measurements, or grader validation. It reinforces ecosystem activity without advancing the causal claim that instruction bloat impairs performance or can be detected automatically.
2026-09-04T23:22:46Z
evidence attached: hn.story.49571131 — A concrete Claude Code skill pack bears on whether bundled context-engineering instructions improve agents or create instruction bloat.
2026-09-03T21:39:38Z
The new static-CLAUDE.md report and refreshed comments reinforce an already established practitioner preference for lean files, selective loading, and inspectable state, but add no controlled measurement or grader validation. This is repetitive implementation testimony rather than progress on the causal or automated-detection claims.
2026-09-03T17:24:35Z
evidence attached: reddit.post.1w6db0u — A firsthand coding-agent workflow report supports the open hypothesis that bloated static instructions degrade agent performance.
2026-09-03T05:23:05Z
The first hash-controlled rerun adds a useful counterweight to the accumulated anecdotes: under this small test, skills produced no measurable effect beyond noise. It still does not isolate instruction bloat, test skill-selection failures, or validate automated grading, so the central hypothesis remains unsettled.
2026-09-03T05:21:47Z
evidence attached: reddit.post.1w5x57x — A controlled rerun with fixed skill and suite hashes found no measurable skill effect, directly testing the case's degradation hypothesis.
2026-09-02T06:24:49Z
Refreshed comments are dismissive of the post’s framing and add no measurement, independent reproduction, controlled bloat ablation, or grader validation. The case remains a persistent practitioner concern with mitigation activity, but its core causal and automated-detection claims are still unsettled.
2026-09-02T03:24:23Z
The team-level sprawl report makes duplicate and malformed skills a more concrete maintenance problem, but its benchmark citation concerns curated-skill benefits rather than a controlled test of bloat. It adds no evidence that automated grading detects harmful patterns or predicts task regressions.
2026-09-02T03:21:59Z
evidence attached: reddit.post.1w4yc07 — The post adds team-level evidence about skill overlap and cites benchmark results linking curated skills to agent performance, though the source is commercially interested.
2026-09-01T21:53:51Z
New comments sharpen the practical response from trusting instruction recall to enforcing tool boundaries and output checks; one practitioner says a small CI rule caught four skipped steps. This adds a concrete guardrail pattern but remains unverified testimony, not the controlled ablation or grader validation needed to advance the hypothesis.
2026-09-01T20:55:28Z
A production-user report adds another concrete instance of skills and persistent instructions being skipped, but remains an uncontrolled anecdote consistent with evidence already accumulated. It does not establish causality, quantify performance harm, or validate automated grading.
2026-09-01T20:26:54Z
evidence attached: reddit.post.1w4nxxe — A concrete production user reports skills and persistent instructions being ignored unpredictably, providing contextual evidence for the open skill-selection and instruction-reliability hypothesis.
2026-09-01T19:01:31Z
A self-audit by the Driftproof/skill-grading author found that corrected, generation-sampled reruns erased previously claimed skill lifts and exposed a timeout bug — a concrete, self-critical evaluation-methodology finding, but still a small (3-cell, 21-case) self-run test that doesn't independently validate any grader or test instruction bloat directly. The case remains a large pile of anecdotal field reports plus scattered mitigation implementations, still awaiting controlled ablation on the actual bloat hypothesis.
2026-09-01T18:26:52Z
evidence attached: reddit.post.1w4j6hz — Independent remeasurement with public receipts materially supports the case that skill evaluations need robust grading and may not reproduce initial gains.
2026-09-01T06:30:19Z
The refreshed comments only continue the conceptual debate around prohibitions versus positive obligations; they add no raw data, controlled ablation, independent reproduction, or grader validation. This is repetitive amplification and does not change the case’s meaning.
2026-09-01T05:40:18Z
The refreshed discussion adds no linked first-party confirmation, controlled ablation, reproducible task result, or validation that automated grading identifies harmful instruction patterns. It is repetitive amplification of an already plausible but confounded field failure mode.
2026-09-01T03:32:40Z
The refreshed comments continue to interpret or question the claimed prohibition-versus-obligation asymmetry without adding methodology, raw data, reproduction, controlled ablation, or grader validation. This is repetitive amplification, so the case remains an unsettled field pattern awaiting independent testing.
2026-09-01T02:27:20Z
The refreshed comments only elaborate or question the claimed prohibition-versus-obligation asymmetry; they add no methodology, raw data, controlled ablation, reproduction, or grader validation. The case remains a plausible but confounded field pattern awaiting independent testing.
2026-09-01T01:29:35Z
The refreshed comments offer a conceptual explanation for why prohibitions may be easier to enforce than positive obligations, while also challenging the reported rates. They add no empirical validation, controlled ablation, or evidence that automated grading predicts harmful instruction patterns.
2026-09-01T00:37:42Z
The claimed prohibition-versus-obligation asymmetry suggests a more specific mechanism for instruction failures, but unsupported self-analysis and refreshed skepticism leave it anecdotal. The case still lacks controlled ablations, reproducible task results, or evidence that automated grading predicts harmful patterns.
2026-09-01T00:24:27Z
evidence attached: reddit.post.1w3vndn — The reported asymmetry between durable prohibitions and forgotten obligations materially contextualizes how instruction-heavy agent skills may affect compliance, though it is only anecdotal evidence.
2026-08-31T23:39:53Z
The refreshed discussion adds no linked first-party finding, controlled ablation, task-level result, or evidence that automated grading predicts harmful skill patterns. It is repetitive amplification of the existing anecdotal case, so the interpretation remains unchanged.
2026-08-31T20:45:07Z
The refreshed comments add no linked first-party confirmation, controlled ablation, task-level measurement, or grader-validation result. They repeat the established anecdotal attribution of agent misbehavior to overloaded instruction files, leaving the case’s meaning unchanged.
2026-08-31T19:41:07Z
The scope-creep report adds another practitioner complaint consistent with instruction overload, but its causal attribution remains unverified and the unsupported claim of Anthropic confirmation adds no weight. The case still lacks controlled ablation, task-level measurement, or evidence that automated grading predicts harmful patterns.
2026-08-31T19:24:37Z
evidence attached: reddit.post.1w3n44s — The report links excessive CLAUDE.md or memory.md content to scope creep, directly supporting the open hypothesis that instruction bloat impairs coding-agent behavior.
2026-08-31T17:37:02Z
Miko sharpens the field complaint from generic instruction bloat to an observable skill-invocation failure and a guardrail that blocks protected edits when required skills are skipped. Without an inspectable artifact, controlled ablation, task-level results, or validation of automated grading, it remains implementation testimony rather than independent confirmation.
2026-08-31T17:24:46Z
evidence attached: reddit.post.1w3j63x — A concrete local verifier reports that long sessions cause required skills to be skipped, materially supporting the open case about skill loading and instruction-bloat failures.
2026-08-31T16:40:54Z
Blume adds a concrete, reviewable workflow for converting recurring agent corrections into rules and skills while attempting to limit accumulation, showing that instruction drift is becoming a product-design concern. It supplies no comparative evaluation, independent use, or evidence that its grading and thresholds reduce drift or bloat, so the core hypothesis remains unvalidated.
2026-08-31T16:24:40Z
evidence attached: hn.story.49511274 — This is a directly relevant implementation of turning accumulated coding-agent corrections into rules and skills, bearing on whether instruction systems rot into bloat and drift.
2026-08-31T14:51:03Z
The JIT skill architecture adds another concrete implementation of selective loading, reinforcing progressive disclosure as the ecosystem’s preferred mitigation for context bloat. With no methodology, benchmarks, or independent results, it does not establish performance harm or validate automated grading.
2026-08-31T14:24:47Z
evidence attached: hn.story.49509511 — This released JIT skill architecture is directly relevant evidence for whether selective skill loading can reduce context decay and instruction bloat.
2026-08-31T02:27:30Z
The refreshed comments add only generic enthusiasm and a supply-chain security concern about the 7,000-skill catalog. They provide no independent ablation, task-performance measurement, selection result, or validation of automated grading, so the case’s meaning is unchanged.
2026-08-30T18:33:51Z
The 7,000-skill MCP catalog makes large-scale discovery and selective loading a concrete ecosystem pattern, increasing the practical importance of curation and selection. It provides no ablation, task-performance measurement, or validation that Skill Grader detects harmful instruction bloat, so the core hypothesis remains unsettled.
2026-08-30T18:23:28Z
evidence attached: hn.story.49501178 — The released MCP server’s catalog of roughly 7,000 searchable and installable skills materially sharpens the open case about skill-selection pressure and instruction bloat.
2026-08-30T17:29:30Z
The refreshed discussion adds practical advice about pruning, progressive disclosure, and moving strict rules into guardrails, but no controlled ablation or task-level measurement. It therefore reinforces known mitigation patterns without validating either the skill-bloat performance claim or automated grading.
2026-08-30T15:34:53Z
The programming-language composition item provides no visible methodology, findings, ablation, or grader-validation result, so it does not advance the case beyond plausible but confounded field reports. Independent performance testing and evidence that automated grading predicts harmful skill patterns remain absent.
2026-08-30T15:24:17Z
evidence attached: hn.story.49499145 — The research directly bears on how programming-language content in agent skills affects skill composition and evaluation.
2026-08-30T13:31:55Z
The newly attached post duplicates the same unmeasured domain-skill anecdote and adds no independent test, controlled ablation, selection metric, or grader validation. The distinction between useful selective context and harmful indiscriminate bloat remains plausible but unresolved.
2026-08-30T13:24:16Z
evidence attached: reddit.post.1w2gts4 — This provides usage evidence that automatically selected domain skills can materially change coding-agent behavior, contextualizing the open case on skill selection and instruction load.
2026-08-30T12:23:57Z
The new account sharpens the distinction between useful selectively loaded domain skills and indiscriminate instruction bloat, but remains an unmeasured self-report. It adds no controlled ablation, selection metric, or evidence that automated grading predicts performance harm.
2026-08-30T12:23:18Z
evidence attached: reddit.post.1w2fcfk — This user report is anecdotal but materially supports the open question of whether selectively loaded skills improve agent performance and context use.
2026-08-30T07:28:58Z
The new report reinforces the established practitioner pattern that growing instruction files consume context and motivate partitioning, but it remains anecdotal. No controlled ablation, task-performance result, skill-selection measurement, or validation of automated grading advances the core hypothesis.
2026-08-30T07:23:17Z
evidence attached: reddit.post.1w2a2w9 — Direct user experience supports the hypothesis that growing agent instructions consume context and motivate partitioning into smaller skills.
2026-08-29T14:25:34Z
The selective-loading registry adds a concrete mitigation pattern for oversized skill packages, but no inspectable ablation or task-level results show that it improves selection or performance. It also provides no validation that automated grading identifies harmful instructions, leaving the case’s core claims unsettled.
2026-08-29T14:23:35Z
evidence attached: reddit.post.1w1mofb — The registry directly bears on whether instruction-bloated skills waste context and whether selective loading can mitigate the problem.
2026-08-29T06:31:01Z
The refreshed comments only endorse prompt hygiene or describe additional memory organization; they add no controlled ablation, measured regression, or evidence that automated grading predicts harmful instruction patterns.
2026-08-29T05:29:32Z
The new CLAUDE.md pruning report adds another independent practitioner anecdote that stale instructions can compete and become harmful, but it provides no controlled ablation, measured regression, or validation of automated grading. The case remains a plausible field failure mode awaiting reproducible testing.
2026-08-29T05:23:27Z
evidence attached: reddit.post.1w1dawn — The firsthand report supports the open hypothesis that accumulated agent instructions can become harmful and require pruning or grading.
2026-08-28T08:38:58Z
The small self-run coverage comparison suggests skill structure can measurably affect task performance, strengthening the rationale for contrastive evaluation. It does not isolate instruction bloat, independently reproduce the result, or show that Skill Grader predicts harmful patterns.
2026-08-28T08:23:43Z
evidence attached: reddit.post.1w0jlo1 — The released skill and preliminary coverage comparison provide relevant evidence about whether structured skill decomposition improves agent task performance.
2026-08-28T04:27:41Z
The multi-skill comparison adds evidence that practitioners are building structured skill evaluations and cross-review workflows, but it does not isolate instruction bloat, report a performance regression, or validate Skill Grader. Refreshed discussion remains anecdotal and confounded, so the case is still awaiting controlled ablation and predictive grading results.
2026-08-28T04:22:39Z
evidence attached: reddit.post.1w0fhp7 — The multi-skill comparison and cross-review process provides limited practical evidence about skill composition and automated grading, though not a decisive result.
2026-08-27T18:57:53Z
A separate measurement effort moves the case beyond purely anecdotal complaints toward actual evaluation activity. However, the available evidence exposes only its title—not methodology, ablations, results, or grader validation—so it does not yet corroborate either performance impairment or automated detection.
2026-08-27T18:24:40Z
evidence attached: hn.story.49468945 — Independent measurement of Claude.md, skills, and hooks directly informs whether agent instructions materially improve or degrade performance and cost.
2026-08-27T05:31:14Z
The refreshed comments add no controlled ablation, reproducible measurement, or validation that Skill Grader predicts performance regressions. The case remains a plausible but confounded field failure mode awaiting independent testing.
2026-08-27T02:32:31Z
The 84-skill workplace report adds another independent field complaint, modestly strengthening the plausibility of skill bloat as a real failure mode. It remains unmeasured and confounded, with no controlled ablation or evidence that Skill Grader predicts performance regressions.
2026-08-27T02:23:30Z
evidence attached: reddit.post.1vzg2hx — Anecdotal field evidence that injecting dozens of skills can degrade an agentic workflow.
2026-08-27T01:34:25Z
The refreshed discussion adds only further anecdotal attribution and does not change the case’s meaning. Controlled ablation or evidence that Skill Grader predicts real task regressions is still missing.
2026-08-26T23:25:09Z
The refreshed discussion adds no independent test, controlled ablation, or grader-validation result; it remains repetitive anecdotal attribution rather than stronger evidence that skill bloat causes regressions or can be detected automatically.
2026-08-26T22:38:56Z
The refreshed comments continue to attribute deterioration to excess skills or instruction files but add no controlled ablation, reproducible result, or validation that Skill Grader predicts real task regressions. The case remains a plausible but confounded field observation awaiting independent testing.
2026-08-26T19:32:01Z
The refreshed comments merely repeat the existing anecdotal attribution of degraded performance to excess skills. No controlled ablation, reproducible result, or validation of Skill Grader changes the case’s meaning.
2026-08-26T17:43:54Z
Multiple user reports make skill bloat a plausible field failure mode worth watching, but they remain anecdotal and heavily confounded. No controlled comparison or independent evidence yet shows that the grader detects harmful patterns or predicts task performance.
2026-08-26T17:24:33Z
evidence attached: reddit.post.1vz26ik — A detailed long-running project report links performance deterioration to a large skill set and materially supports the open skill-bloat hypothesis.
2026-08-26T17:24:33Z
evidence attached: reddit.post.1vz2tbv — A user report directly supports the open hypothesis that excessive skills and instruction files degrade coding-agent behavior, though it is anecdotal.
2026-08-25T15:50:57Z
Reobservation adds no implementation details, independent tests, or performance results; the case remains an unvalidated grader launch rather than evidence that skill bloat can be reliably detected. With no discussion or follow-through, its near-term attention temperature has cooled.
2026-08-25T15:39:21Z
grounded: known/medium — Scott already holds the core position in “Fat AGENTS.md Anti-Pattern” and prescribes contrastive, repeatable gates in “Evaluation-Driven Development.” A credibl
2026-08-25T15:37:32Z
case created — The released grader operationalizes a specific context-competition failure mode reported across large collections of agent skills.