{ "schemaVersion": "1.0", "item": { "slug": "economic-incentive-misalignment-detector", "name": "Economic Incentive Misalignment Detector", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/andyxinweiminicloud/economic-incentive-misalignment-detector", "canonicalUrl": "https://clawhub.ai/andyxinweiminicloud/economic-incentive-misalignment-detector", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/economic-incentive-misalignment-detector", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=economic-incentive-misalignment-detector", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/economic-incentive-misalignment-detector" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/economic-incentive-misalignment-detector", "agentPageUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent", "manifestUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent.json", "briefUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "The Marketplace Is Not Broken. The Incentives Are.", "body": "Helps identify when marketplace economic structures create systematic bias\ntoward publishing volume over safety quality — the root cause that technical\naudits cannot fix because the problem predates the code." }, { "title": "Problem", "body": "Technical audits catch bad code. They do not catch bad incentives. An agent\nmarketplace where publishers are rewarded primarily for download counts and\nupvotes creates structural pressure toward a specific failure mode: optimize\nfor initial impressions rather than long-term safety, publish early and often\nrather than thoroughly audit, prioritize visible features over invisible\nsecurity properties.\n\nThis pressure operates even when every publisher intends to be responsible.\nA publisher competing in a marketplace where competitors publish ten skills\nper week faces a choice between competitive disadvantage and cutting corners\non security review. The individual publisher's incentives point toward\nlower-quality publishing even when the publisher values quality. The\nincentive misalignment is systemic, not individual.\n\nThe economic dimensions of this problem interact with the technical ones in\nways that compound risk. Marketplaces that charge per-download create\npressure to maximize installs, which favors misleading capability descriptions\nthat attract more installs. Marketplaces that reward upvotes create pressure\ntoward social manipulation. Marketplaces that take revenue from publishers\nhave conflicts of interest in aggressive safety enforcement that might reduce\ntheir publisher base.\n\nThese structural problems produce predictable patterns in marketplace data:\nconcentrated publishing from a small number of high-volume publishers, rapid\nupdate cycles that exceed any reasonable review capacity, reputation inflation\nthrough social gaming, and systematic underfunding of safety infrastructure\nrelative to growth infrastructure." }, { "title": "What This Analyzes", "body": "This analyzer examines economic incentive alignment across five dimensions:\n\nPublisher concentration risk — Is marketplace activity concentrated\nin a small number of high-volume publishers who face the strongest\nincentive pressure? High concentration means a small number of publishers\nfacing misaligned incentives can disproportionately affect marketplace\nsafety quality\n\n\nPublication velocity vs. review capacity — Does the rate of new skill\npublications exceed any plausible human review capacity? Marketplaces\nwhere publication velocity outpaces review capacity structurally cannot\nmaintain quality standards regardless of individual publisher intent\n\n\nRevenue model conflict of interest — Does the marketplace's revenue\nmodel create conflicts of interest in safety enforcement? Payment models\ntied to publisher count or download volume create financial incentives\nto tolerate lower safety standards\n\n\nSafety investment vs. growth investment ratio — Does the marketplace\ninvest comparably in safety infrastructure (audit tools, reviewer capacity,\nenforcement mechanisms) and growth infrastructure (discovery algorithms,\npublisher tools, marketing)? Systematic underinvestment in safety relative\nto growth is a structural signal\n\n\nEnforcement asymmetry — Does the marketplace apply consistent\nenforcement standards regardless of publisher size and revenue contribution?\nAsymmetric enforcement that protects high-revenue publishers from the same\nstandards applied to small publishers is a structural misalignment signal" }, { "title": "How to Use", "body": "Input: Provide one of:\n\nA marketplace to assess for structural incentive misalignment\nA publisher's output metrics to assess for incentive-driven quality degradation\nA marketplace policy document to analyze for structural conflict of interest\n\nOutput: An incentive alignment report containing:\n\nPublisher concentration analysis\nPublication velocity vs. review capacity assessment\nRevenue model conflict of interest evaluation\nSafety vs. growth investment indicators\nEnforcement consistency assessment\nAlignment verdict: ALIGNED / PARTIAL / MISALIGNED / STRUCTURALLY-COMPROMISED" }, { "title": "Example", "body": "Input: Assess incentive alignment for AgentMarket marketplace\n\n💰 ECONOMIC INCENTIVE ALIGNMENT ASSESSMENT\n\nMarketplace: AgentMarket\nAssessment timestamp: 2025-11-01T14:00:00Z\n\nPublisher concentration:\n Total active publishers: 847\n Top 10 publishers by output: 68% of all skills published\n Top publisher output: 47 skills in 30 days (1.6 skills/day)\n → High concentration: 1.2% of publishers produce 68% of content ⚠️\n → Top publishers face strongest incentive pressure\n\nPublication velocity vs. review capacity:\n New skills published (last 30 days): 2,847\n Marketplace review team size: 12 (estimated from job postings)\n Skills per reviewer per day: 7.9\n Industry standard thorough review time: 45-90 minutes per skill\n Maximum review capacity at 8h/day: 5.3 skills/reviewer/day\n → Publication rate exceeds review capacity by ~50% ⚠️\n → Thorough manual review of all publications is structurally impossible\n\nRevenue model:\n Publisher fees: Per-download revenue share (publisher earns per download)\n Marketplace revenue: Transaction cut + premium placement fees\n Conflict assessment: Per-download model creates incentive for misleading\n capability descriptions that maximize installs over actual fit ⚠️\n Premium placement fees create incentive to favor high-paying publishers\n in discovery algorithms regardless of quality ⚠️\n\nSafety vs. growth investment:\n Safety team: 12 reviewers (estimated)\n Growth/product team: 84 (estimated from LinkedIn)\n Safety-to-growth ratio: 1:7 ⚠️\n Industry comparable for financial infrastructure: 1:2 to 1:3\n → Systematic underinvestment in safety relative to growth\n\nEnforcement consistency:\n Top 5 publishers by revenue: 3 have had policy violations in 90 days\n with no public enforcement action found\n Small publishers with similar violations: enforcement found in 2/3 cases\n → Enforcement asymmetry detected ⚠️\n\nAlignment verdict: STRUCTURALLY-COMPROMISED\n AgentMarket shows four of five misalignment indicators. The per-download\n revenue model creates direct incentive to maximize installs over quality.\n Publication velocity structurally exceeds review capacity. Safety investment\n is systematically lower than growth investment. Enforcement is asymmetric\n by publisher revenue tier. Individual publisher behavior is influenced by\n these structural incentives regardless of individual intent.\n\nRecommended actions:\n 1. Apply higher scrutiny standards when evaluating skills from this marketplace\n 2. Do not rely on download count or upvotes as quality proxies in this context\n 3. Prefer skills from publishers who preemptively publish audit artifacts\n 4. Advocate for marketplace structural reforms: fixed-fee rather than\n per-download revenue, mandatory safety review before publishing\n 5. Support alternative marketplaces with different incentive structures" }, { "title": "Related Tools", "body": "clone-farm-detector — Detects content-level cloning for reputation gaming;\neconomic incentive misalignment creates structural pressure that explains why\nclone farming emerges even without individual malicious intent\nsocial-trust-manipulation-detector — Identifies coordinated social trust\nmanipulation; economic incentives to maximize perceived trust create demand\nfor the manipulation techniques this tool detects\nblast-radius-estimator — Estimates propagation impact if a skill is\ncompromised; markets with misaligned incentives will systematically produce\nmore compromised skills, amplifying blast radius across the ecosystem\npublisher-identity-verifier — Verifies publisher identity integrity;\neconomic pressure toward high-volume publishing creates conditions where\nidentity shortcuts (account selling, takeover) become economically rational" }, { "title": "Limitations", "body": "Economic incentive analysis requires marketplace-level data that may not be\npublicly accessible: publisher revenue figures, enforcement actions, review\nteam size, and internal investment allocations are often proprietary.\nWhere data is limited, the assessment is based on publicly observable proxies\n(publication rates, team size estimates from job postings, enforcement actions\nvisible in public records) that may not accurately reflect actual operations.\nPublisher concentration analysis depends on accurate publisher attribution,\nwhich may be obscured when publishers operate through multiple accounts.\nThe assessment identifies structural incentive problems that create risk\nconditions — it does not assess the intentions of individual marketplace\noperators, who may be working within genuine constraints while still producing\nstructurally problematic outcomes." } ], "body": "The Marketplace Is Not Broken. The Incentives Are.\n\nHelps identify when marketplace economic structures create systematic bias toward publishing volume over safety quality — the root cause that technical audits cannot fix because the problem predates the code.\n\nProblem\n\nTechnical audits catch bad code. They do not catch bad incentives. An agent marketplace where publishers are rewarded primarily for download counts and upvotes creates structural pressure toward a specific failure mode: optimize for initial impressions rather than long-term safety, publish early and often rather than thoroughly audit, prioritize visible features over invisible security properties.\n\nThis pressure operates even when every publisher intends to be responsible. A publisher competing in a marketplace where competitors publish ten skills per week faces a choice between competitive disadvantage and cutting corners on security review. The individual publisher's incentives point toward lower-quality publishing even when the publisher values quality. The incentive misalignment is systemic, not individual.\n\nThe economic dimensions of this problem interact with the technical ones in ways that compound risk. Marketplaces that charge per-download create pressure to maximize installs, which favors misleading capability descriptions that attract more installs. Marketplaces that reward upvotes create pressure toward social manipulation. Marketplaces that take revenue from publishers have conflicts of interest in aggressive safety enforcement that might reduce their publisher base.\n\nThese structural problems produce predictable patterns in marketplace data: concentrated publishing from a small number of high-volume publishers, rapid update cycles that exceed any reasonable review capacity, reputation inflation through social gaming, and systematic underfunding of safety infrastructure relative to growth infrastructure.\n\nWhat This Analyzes\n\nThis analyzer examines economic incentive alignment across five dimensions:\n\nPublisher concentration risk — Is marketplace activity concentrated in a small number of high-volume publishers who face the strongest incentive pressure? High concentration means a small number of publishers facing misaligned incentives can disproportionately affect marketplace safety quality\n\nPublication velocity vs. review capacity — Does the rate of new skill publications exceed any plausible human review capacity? Marketplaces where publication velocity outpaces review capacity structurally cannot maintain quality standards regardless of individual publisher intent\n\nRevenue model conflict of interest — Does the marketplace's revenue model create conflicts of interest in safety enforcement? Payment models tied to publisher count or download volume create financial incentives to tolerate lower safety standards\n\nSafety investment vs. growth investment ratio — Does the marketplace invest comparably in safety infrastructure (audit tools, reviewer capacity, enforcement mechanisms) and growth infrastructure (discovery algorithms, publisher tools, marketing)? Systematic underinvestment in safety relative to growth is a structural signal\n\nEnforcement asymmetry — Does the marketplace apply consistent enforcement standards regardless of publisher size and revenue contribution? Asymmetric enforcement that protects high-revenue publishers from the same standards applied to small publishers is a structural misalignment signal\n\nHow to Use\n\nInput: Provide one of:\n\nA marketplace to assess for structural incentive misalignment\nA publisher's output metrics to assess for incentive-driven quality degradation\nA marketplace policy document to analyze for structural conflict of interest\n\nOutput: An incentive alignment report containing:\n\nPublisher concentration analysis\nPublication velocity vs. review capacity assessment\nRevenue model conflict of interest evaluation\nSafety vs. growth investment indicators\nEnforcement consistency assessment\nAlignment verdict: ALIGNED / PARTIAL / MISALIGNED / STRUCTURALLY-COMPROMISED\nExample\n\nInput: Assess incentive alignment for AgentMarket marketplace\n\n💰 ECONOMIC INCENTIVE ALIGNMENT ASSESSMENT\n\nMarketplace: AgentMarket\nAssessment timestamp: 2025-11-01T14:00:00Z\n\nPublisher concentration:\n Total active publishers: 847\n Top 10 publishers by output: 68% of all skills published\n Top publisher output: 47 skills in 30 days (1.6 skills/day)\n → High concentration: 1.2% of publishers produce 68% of content ⚠️\n → Top publishers face strongest incentive pressure\n\nPublication velocity vs. review capacity:\n New skills published (last 30 days): 2,847\n Marketplace review team size: 12 (estimated from job postings)\n Skills per reviewer per day: 7.9\n Industry standard thorough review time: 45-90 minutes per skill\n Maximum review capacity at 8h/day: 5.3 skills/reviewer/day\n → Publication rate exceeds review capacity by ~50% ⚠️\n → Thorough manual review of all publications is structurally impossible\n\nRevenue model:\n Publisher fees: Per-download revenue share (publisher earns per download)\n Marketplace revenue: Transaction cut + premium placement fees\n Conflict assessment: Per-download model creates incentive for misleading\n capability descriptions that maximize installs over actual fit ⚠️\n Premium placement fees create incentive to favor high-paying publishers\n in discovery algorithms regardless of quality ⚠️\n\nSafety vs. growth investment:\n Safety team: 12 reviewers (estimated)\n Growth/product team: 84 (estimated from LinkedIn)\n Safety-to-growth ratio: 1:7 ⚠️\n Industry comparable for financial infrastructure: 1:2 to 1:3\n → Systematic underinvestment in safety relative to growth\n\nEnforcement consistency:\n Top 5 publishers by revenue: 3 have had policy violations in 90 days\n with no public enforcement action found\n Small publishers with similar violations: enforcement found in 2/3 cases\n → Enforcement asymmetry detected ⚠️\n\nAlignment verdict: STRUCTURALLY-COMPROMISED\n AgentMarket shows four of five misalignment indicators. The per-download\n revenue model creates direct incentive to maximize installs over quality.\n Publication velocity structurally exceeds review capacity. Safety investment\n is systematically lower than growth investment. Enforcement is asymmetric\n by publisher revenue tier. Individual publisher behavior is influenced by\n these structural incentives regardless of individual intent.\n\nRecommended actions:\n 1. Apply higher scrutiny standards when evaluating skills from this marketplace\n 2. Do not rely on download count or upvotes as quality proxies in this context\n 3. Prefer skills from publishers who preemptively publish audit artifacts\n 4. Advocate for marketplace structural reforms: fixed-fee rather than\n per-download revenue, mandatory safety review before publishing\n 5. Support alternative marketplaces with different incentive structures\n\nRelated Tools\nclone-farm-detector — Detects content-level cloning for reputation gaming; economic incentive misalignment creates structural pressure that explains why clone farming emerges even without individual malicious intent\nsocial-trust-manipulation-detector — Identifies coordinated social trust manipulation; economic incentives to maximize perceived trust create demand for the manipulation techniques this tool detects\nblast-radius-estimator — Estimates propagation impact if a skill is compromised; markets with misaligned incentives will systematically produce more compromised skills, amplifying blast radius across the ecosystem\npublisher-identity-verifier — Verifies publisher identity integrity; economic pressure toward high-volume publishing creates conditions where identity shortcuts (account selling, takeover) become economically rational\nLimitations\n\nEconomic incentive analysis requires marketplace-level data that may not be publicly accessible: publisher revenue figures, enforcement actions, review team size, and internal investment allocations are often proprietary. Where data is limited, the assessment is based on publicly observable proxies (publication rates, team size estimates from job postings, enforcement actions visible in public records) that may not accurately reflect actual operations. Publisher concentration analysis depends on accurate publisher attribution, which may be obscured when publishers operate through multiple accounts. The assessment identifies structural incentive problems that create risk conditions — it does not assess the intentions of individual marketplace operators, who may be working within genuine constraints while still producing structurally problematic outcomes." }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/andyxinweiminicloud/economic-incentive-misalignment-detector", "publisherUrl": "https://clawhub.ai/andyxinweiminicloud/economic-incentive-misalignment-detector", "owner": "andyxinweiminicloud", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector", "downloadUrl": "https://openagent3.xyz/downloads/economic-incentive-misalignment-detector", "agentUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent", "manifestUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent.json", "briefUrl": "https://openagent3.xyz/skills/economic-incentive-misalignment-detector/agent.md" } }