{"slug":"indranilbanerjee-budget-optimizer","name":"budget-optimizer","description":"Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.","long_description":"---\nname: budget-optimizer\ndescription: \"Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.\"\nargument-hint: \"[total-budget]\"\n---\n\n# /digital-marketing-pro:budget-optimizer\n\n## Purpose\n\nData-driven marketing budget optimization across channels using performance data and industry benchmarks. Analyzes current spend efficiency, models diminishing returns per channel, and produces an optimized allocation with projected ROI improvement and a phased reallocation timeline.\n\n## Input Required\n\nThe user must provide (or will be prompted for):\n\n- **Current budget by channel**: How spend is distributed today (e.g., paid search, paid social, SEO, email, content, display, affiliate, events, etc.)\n- **Performance data by channel**: Key metrics per channel — spend, revenue or conversions, CPA, ROAS, and conversion volume over the measurement period\n- **Total budget available**: Overall marketing budget for the optimization period (monthly, quarterly, or annual)\n- **Business goals**: Primary objective — maximize revenue, minimize CPA, hit a specific lead or revenue target, balance growth with efficiency\n- **Constraints**: Minimum spend requirements, channel mandates from leadership, seasonal considerations, contractual commitments, or platform minimums\n- **Measurement period**: Timeframe the performance data covers (last 30, 60, 90 days, or custom range)\n- **Attribution model**: How conversions are currently attributed (last-click, first-click, linear, data-driven, or unknown)\n- **Seasonality factors**: Upcoming seasonal peaks, promotional periods, or industry events that affect channel performance\n- **Historical context**: Whether performance data reflects a typical period or was influenced by one-time events (product launch, viral moment, outage)\n\n## Process\n\n1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. **Also check for guidelines** at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files. Check for custom templates at `~/.claude-marketing/brands/{slug}/templates/`. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: \"Set up a brand first (/digital-marketing-pro:brand-setup)?\" — or proceed with defaults.\n2. **Run budget-optimizer.py script**: Execute `python \"${CLAUDE_PLUGIN_ROOT}/scripts/budget-optimizer.py\" --channels '[{\"name\":\"google_ads\",\"spend\":10000,\"roas\":4.2}]' --total-budget {amount}` (`--total-budget` is required; pass channel data via `--channels` JSON or `--file`) to compute baseline efficiency metrics and generate optimization scenarios\n3. **Calculate efficiency metrics per channel**: Compute ROAS, CPA, cost per lead, revenue per dollar, contribution margin, and marginal cost of acquisition for each channel\n4. **Rank channels by marginal efficiency**: Order channels by incremental return per additional dollar spent, accounting for current saturation levels and historical performance trends\n5. **Apply diminishing returns model**: Model how each channel's efficiency degrades as spend increases — identify the inflection point and saturation ceiling for each channel\n6. **Generate optimized allocation**: Redistribute budget to maximize the stated objective while respecting all constraints and minimum viable spend thresholds\n7. **Compare current vs optimized**: Build a side-by-side comparison showing spend shifts, projected metric changes, and net improvement across all KPIs\n8. **Project ROI improvement**: Estimate total revenue, conversion volume, ROAS, and CPA gains from the reallocation with confidence intervals\n9. **Account for minimum viable spend thresholds**: Ensure no channel drops below the minimum spend needed to generate meaningful data, maintain auction competitiveness, or fulfill contractual obligations\n10. **Include testing budget**: Reserve 10-15% of total budget for experimentation — new channels, creative testing, audience expansion, or emerging platforms\n11. **Flag attribution caveats**: Note where attribution model limitations may skew efficiency calculations and recommend adjustments\n12. **Create reallocation timeline**: Phase budget shifts over 4-8 weeks to avoid performance disruption — gradual ramp-up and ramp-down with weekly checkpoints and rollback triggers\n\n## Output\n\nA structured budget optimization plan containing:\n\n- **Current vs optimized allocation table**: Side-by-side channel budgets with dollar amounts, percentage of total, and change from current\n- **Projected ROI improvement**: Expected gains in revenue, conversions, ROAS, and CPA with confidence ranges\n- **Channel efficiency ranking**: Channels ordered by marginal return with diminishing returns curves and saturation indicators\n- **Reallocation recommendations**: Specific dollar shifts with clear rationale for each increase, decrease, or hold\n- **Scenario comparison**: Best-case, expected, and conservative projections for the optimized allocation\n- **Implementation timeline**: Phased reallocation schedule with weekly checkpoints, performance triggers, and rollback criteria\n- **Risk assessment**: Potential downsides of each shift, minimum viable spend warnings, attribution blind spots, and mitigation strategies\n- **Testing budget plan**: Recommended experiments with allocated budget, hypotheses, success criteria, and measurement approach\n- **Attribution notes**: Caveats on how the current attribution model may over- or under-credit specific channels\n- **Executive summary**: 1-page overview of key findings and recommended actions for stakeholder presentation\n\n## Agents Used\n\n- **analytics-analyst** — Performance data analysis, efficiency calculations, diminishing returns modeling, ROI projections, attribution assessment\n- **media-buyer** — Channel-level budget strategy, spend threshold expertise, reallocation sequencing, platform-specific benchmarks, auction dynamics\n","tagline":"Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. 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Creators can claim the listing to update ownership signals."},"stats":{"stars":787,"forks":132,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":43.38},"quality":{"score":76,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"787","tone":"positive"},{"label":"Freshness","value":"19d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":74,"base_score":82,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["74/100 Trust Score v5","82/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":76,"weight":0.13,"status":"info","detail":"787 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":71,"weight":0.08,"status":"info","detail":"787 stars, 132 forks; 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issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"19d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"filesystem or document access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Meaningful GitHub adoption signal","Install command has no obvious high-risk pattern"],"warnings":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"],"evidence":{"stars":"787 GitHub stars","repoActivity":"787 stars, 132 forks","lastPushed":"19d since push","license":"MIT","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","install":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","installSafety":"standard package or runtime install path","permissionSurface":"filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","19d since push","Financial domain: human review is required before use in a live investment workflow."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Human review or sandbox validation is required before automatic installation."},"bestFor":["design-creative","agent-skill"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"outcome_stats":null,"safety":{"score":69,"level":"review_before_install","label":"Review before install","safety_tier":{"tier":"reviewed","label":"Reviewed with permission notes","badge":"REVIEWED","summary":"Usable candidate, but the agent should surface permission and audit notes before installation.","recommended_action":"Require human approval before installing into a real workspace.","auto_install_policy":"review","reasons":["Financial research output is not financial advice; require human review before any live investment decision","69/100 agent safety score"]},"auto_install_allowed":false,"human_review_required":true,"blocked":false,"audit_risk":"needs_review","permission_hints":[{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"}],"policy_warnings":["Financial research output is not financial advice; require human review before any live investment decision"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","badge":"REVIEWED","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Require human approval before installing into a real workspace.","reasons":["Financial research output is not financial advice; require human review before any live investment decision","69/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"review","score":77,"risk_level":"medium","decision":{"recommendation":"manual_review","reason":"Require human approval before installing into a real workspace.","auto_install_allowed":false,"policy":"review","human_review_required":true},"blockers":[],"warnings":["Task fit: Task fit is weak; compare alternatives before selecting.","Audit score: Needs review","Agent safety gate: Usable candidate, but the agent should surface permission and audit notes before installation.","README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"warn","score":70,"required_for_auto_install":true,"detail":"Task fit is weak; compare alternatives before selecting.","evidence":["Evaluate budget-optimizer before installing it in an agent workflow","design-creative","Research agents workflows; Claude Code teams; teams that value GitHub adoption signals"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer"]},{"id":"trust_score","label":"Trust score","status":"pass","score":82,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","787 GitHub stars","MIT"]},{"id":"audit_score","label":"Audit score","status":"warn","score":85,"required_for_auto_install":true,"detail":"Needs review","evidence":["Financial research output is not financial advice; require human review before any live investment decision"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":69,"required_for_auto_install":true,"detail":"Usable candidate, but the agent should surface permission and audit notes before installation.","evidence":["Require human approval before installing into a real workspace.","Financial research output is not financial advice; require human review before any live investment decision"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"warn","score":76,"required_for_auto_install":false,"detail":"Public metadata needs stronger README/SKILL.md context","evidence":["Usable metadata, review docs"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"MIT","evidence":["MIT"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"19d since push","evidence":["19d since push"]},{"id":"permission_surface","label":"Permission surface","status":"pass","score":86,"required_for_auto_install":true,"detail":"filesystem or document access","evidence":["Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer/evals","api":"/api/agent/evals?slug=indranilbanerjee-budget-optimizer","text":"/api/agent/evals?slug=indranilbanerjee-budget-optimizer&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"indranilbanerjee-budget-optimizer","name":"budget-optimizer","description":"Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.","category":"design-creative","url":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","github_repo":"indranilbanerjee/digital-marketing-pro"},"suited_tasks":["Research agents workflows","Claude Code teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Collect channel signals","Prioritize opportunities"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add indranilbanerjee-budget-optimizer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"budget-optimizer\" agent skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"budget-optimizer\" as a Claude Code skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"budget-optimizer\" from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/indranilbanerjee-budget-optimizer/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/indranilbanerjee-budget-optimizer"},"trust":{"score":82,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"787 GitHub stars","repoActivity":"787 stars, 132 forks","lastPushed":"19d since push","license":"MIT","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","install":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","installSafety":"standard package or runtime install path","permissionSurface":"filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["design-creative","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":85,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":76,"label":"Strong"},"supply":{"track":"Marketing and growth automation","scenario":"Marketing and growth","maintenance":"19d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface"],"agent_contract":{"task_input":"Use budget-optimizer in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 82/100 Strong shortlist","Audit: 85/100 Needs review","Safety: 69/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"indranilbanerjee-budget-optimizer (budget-optimizer)","install_command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","risk_summary":"Needs review; Reviewed with permission notes; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"indranilbanerjee-budget-optimizer","task":"Use budget-optimizer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer","api":"https://www.openagentskill.com/api/agent/skills/indranilbanerjee-budget-optimizer","audit":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=indranilbanerjee-budget-optimizer&task=Use%20budget-optimizer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20budget-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20budget-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/indranilbanerjee-budget-optimizer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/indranilbanerjee-budget-optimizer"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"indranilbanerjee-budget-optimizer","name":"budget-optimizer","description":"Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing.","category":"design-creative","url":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","github_repo":"indranilbanerjee/digital-marketing-pro"},"suited_tasks":["Research agents workflows","Claude Code teams","teams that value GitHub adoption signals","Search sources","Extract claims","Synthesize findings","Collect channel signals","Prioritize opportunities"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add indranilbanerjee-budget-optimizer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"budget-optimizer\" agent skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"budget-optimizer\" as a Claude Code skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"budget-optimizer\" from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/indranilbanerjee-budget-optimizer/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/indranilbanerjee-budget-optimizer"},"trust":{"score":82,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"787 GitHub stars","repoActivity":"787 stars, 132 forks","lastPushed":"19d since push","license":"MIT","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","install":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","installSafety":"standard package or runtime install path","permissionSurface":"filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["design-creative","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":85,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":76,"label":"Strong"},"supply":{"track":"Marketing and growth automation","scenario":"Marketing and growth","maintenance":"19d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface"],"agent_contract":{"task_input":"Use budget-optimizer in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 82/100 Strong shortlist","Audit: 85/100 Needs review","Safety: 69/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"indranilbanerjee-budget-optimizer (budget-optimizer)","install_command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","risk_summary":"Needs review; Reviewed with permission notes; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"indranilbanerjee-budget-optimizer","task":"Use budget-optimizer in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer","api":"https://www.openagentskill.com/api/agent/skills/indranilbanerjee-budget-optimizer","audit":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=indranilbanerjee-budget-optimizer&task=Use%20budget-optimizer%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20budget-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20budget-optimizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/indranilbanerjee-budget-optimizer/install","manifest":"https://www.openagentskill.com/api/registry/manifest/indranilbanerjee-budget-optimizer"}},"supply_profile":{"track":{"slug":"marketing","label":"Marketing and growth automation","shortLabel":"Marketing","description":"SEO, content operations, lead generation, CRM, email automation, analytics, and growth workflows."},"scenario":{"label":"Marketing and growth","description":"I need my agent to research keywords, improve SEO, analyze growth channels, and prepare marketing workflows.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"marketing-growth","title":"Marketing and growth"},{"slug":"data-analysis","title":"Data analysis"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":787,"starsLabel":"787","forks":132,"license":"MIT","qualityScore":76,"trustScore":82,"auditScore":85},"maintenance":{"status":"fresh","label":"19d since push","daysSincePush":19,"lastPushedAt":"2026-08-17T10:50:14+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Needs review"]},"coverageTags":["Marketing","Marketing and growth","design-creative","agent-skill"]},"audit":{"audit_score":85,"risk_level":"needs_review","risk_label":"Needs review","quality_score":76,"trust_score":82,"maintenance_score":100,"security_score":88,"install_score":92,"warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"quality_signals":{"model":"v2","star_score":20.28,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"marketing-growth","title":"Marketing and growth","url":"https://www.openagentskill.com/use-cases/marketing-growth"},{"slug":"data-analysis","title":"Data analysis","url":"https://www.openagentskill.com/use-cases/data-analysis"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"}],"install":"npx skills add indranilbanerjee/digital-marketing-pro --skill budget-optimizer","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add indranilbanerjee-budget-optimizer","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"budget-optimizer\" agent skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"budget-optimizer\" as a Claude Code skill from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"budget-optimizer\" from https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Reallocate marketing spend across channels using performance data and diminishing-returns modeling — produces a current-vs-optimized allocation table, projected ROI ranges with confidence intervals, a phased 4-8 week reallocation timeline, and a 10-15% testing reserve. Recommends shifts only; it never changes spend on any platform. Triggers on \\\"/digital-marketing-pro:budget-optimizer\\\", \\\"optimize my marketing budget\\\", \\\"which channels should get more spend\\\", \\\"reallocate budget based on ROAS\\\", \\\"is our channel split right\\\". Reads the brand profile and guidelines, runs scripts/budget-optimizer.py, and pairs with /digital-marketing-pro:budget-tracker for in-flight pacing. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"indranilbanerjee-budget-optimizer\",\"task\":\"Install budget-optimizer\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","github_repo":"indranilbanerjee/digital-marketing-pro","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/indranilbanerjee-budget-optimizer","repository":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/budget-optimizer","api":"/api/agent/skills/indranilbanerjee-budget-optimizer","install_api":"/api/skills/indranilbanerjee-budget-optimizer/install"},"meta":{"created_at":"2026-09-02T18:42:22.247828+00:00","updated_at":"2026-09-02T18:42:22.339768+00:00","agent_friendly":true}}