[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73GD39VFQQCVBBCEQ3XADTE":3,"skill-same:01M73GD39VFQQCVBBCEQ3XADTE":49},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":30,"ownerHtmlUrl":31,"ownerBlogUrl":32,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M73GD39VFQQCVBBCEQ3XADTE","token-budget-advisor",null,"Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: \"token budget\", \"token count\", \"token usage\", \"token limit\", \"response length\", \"answer depth\", \"short version\", \"brief answer\", \"detailed answer\", \"exhaustive answer\", \"respuesta corta vs larga\", \"cuántos tokens\", \"ahorrar tokens\", \"responde al 50%\", \"dame la versión corta\", \"quiero controlar cuánto usas\", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or \"token\" refers to auth\u002Fsession\u002Fpayment tokens rather than response size.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Ftoken-budget-advisor\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: token-budget-advisor\ndescription: >-\n  Offers the user an informed choice about how much response depth to\n  consume before answering. Use this skill when the user explicitly\n  wants to control response length, depth, or token budget.\n  TRIGGER when: \"token budget\", \"token count\", \"token usage\", \"token limit\",\n  \"response length\", \"answer depth\", \"short version\", \"brief answer\",\n  \"detailed answer\", \"exhaustive answer\", \"respuesta corta vs larga\",\n  \"cuántos tokens\", \"ahorrar tokens\", \"responde al 50%\", \"dame la versión\n  corta\", \"quiero controlar cuánto usas\", or clear variants where the\n  user is explicitly asking to control answer size or depth.\n  DO NOT TRIGGER when: user has already specified a level in the current\n  session (maintain it), the request is clearly a one-word answer, or\n  \"token\" refers to auth\u002Fsession\u002Fpayment tokens rather than response size.\norigin: community\n---\n\n# Token Budget Advisor (TBA)\n\nIntercept the response flow to offer the user a choice about response depth **before** Claude answers.\n\n## When to Use\n\n- User wants to control how long or detailed a response is\n- User mentions tokens, budget, depth, or response length\n- User says \"short version\", \"tldr\", \"brief\", \"al 25%\", \"exhaustive\", etc.\n- Any time the user wants to choose depth\u002Fdetail level upfront\n\n**Do not trigger** when: user already set a level this session (maintain it silently), or the answer is trivially one line.\n\n## How It Works\n\n### Step 1 — Estimate input tokens\n\nUse the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.\n\nUse the same calibration guidance as [context-budget](..\u002Fcontext-budget\u002FSKILL.md):\n\n- prose: `words × 1.3`\n- code-heavy or mixed\u002Fcode blocks: `chars \u002F 4`\n\nFor mixed content, use the dominant content type and keep the estimate heuristic.\n\n### Step 2 — Estimate response size by complexity\n\nClassify the prompt, then apply the multiplier range to get the full response window:\n\n| Complexity   | Multiplier range | Example prompts                                      |\n|--------------|------------------|------------------------------------------------------|\n| Simple       | 3× – 8×          | \"What is X?\", yes\u002Fno, single fact                   |\n| Medium       | 8× – 20×         | \"How does X work?\"                                  |\n| Medium-High  | 10× – 25×        | Code request with context                           |\n| Complex      | 15× – 40×        | Multi-part analysis, comparisons, architecture      |\n| Creative     | 10× – 30×        | Stories, essays, narrative writing                  |\n\nResponse window = `input_tokens × mult_min` to `input_tokens × mult_max` (but don’t exceed your model’s configured output-token limit).\n\n### Step 3 — Present depth options\n\nPresent this block **before** answering, using the actual estimated numbers:\n\n```\nAnalyzing your prompt...\n\nInput: ~[N] tokens  |  Type: [type]  |  Complexity: [level]  |  Language: [lang]\n\nChoose your depth level:\n\n[1] Essential   (25%)  ->  ~[tokens]   Direct answer only, no preamble\n[2] Moderate    (50%)  ->  ~[tokens]   Answer + context + 1 example\n[3] Detailed    (75%)  ->  ~[tokens]   Full answer with alternatives\n[4] Exhaustive (100%)  ->  ~[tokens]   Everything, no limits\n\nWhich level? (1-4 or say \"25% depth\", \"50% depth\", \"75% depth\", \"100% depth\")\n\nPrecision: heuristic estimate ~85-90% accuracy (±15%).\n```\n\nLevel token estimates (within the response window):\n- 25%  → `min + (max - min) × 0.25`\n- 50%  → `min + (max - min) × 0.50`\n- 75%  → `min + (max - min) × 0.75`\n- 100% → `max`\n\n### Step 4 — Respond at the chosen level\n\n| Level            | Target length       | Include                                             | Omit                                              |\n|------------------|---------------------|-----------------------------------------------------|---------------------------------------------------|\n| 25% Essential    | 2-4 sentences max   | Direct answer, key conclusion                       | Context, examples, nuance, alternatives           |\n| 50% Moderate     | 1-3 paragraphs      | Answer + necessary context + 1 example              | Deep analysis, edge cases, references             |\n| 75% Detailed     | Structured response | Multiple examples, pros\u002Fcons, alternatives          | Extreme edge cases, exhaustive references         |\n| 100% Exhaustive  | No restriction      | Everything — full analysis, all code, all perspectives | Nothing                                        |\n\n## Shortcuts — skip the question\n\nIf the user already signals a level, respond at that level immediately without asking:\n\n| What they say                                      | Level |\n|----------------------------------------------------|-------|\n| \"1\" \u002F \"25% depth\" \u002F \"short version\" \u002F \"brief answer\" \u002F \"tldr\"  | 25%   |\n| \"2\" \u002F \"50% depth\" \u002F \"moderate depth\" \u002F \"balanced answer\"        | 50%   |\n| \"3\" \u002F \"75% depth\" \u002F \"detailed answer\" \u002F \"thorough answer\"       | 75%   |\n| \"4\" \u002F \"100% depth\" \u002F \"exhaustive answer\" \u002F \"full deep dive\"     | 100%  |\n\nIf the user set a level earlier in the session, **maintain it silently** for subsequent responses unless they change it.\n\n## Precision note\n\nThis skill uses heuristic estimation — no real tokenizer. Accuracy ~85-90%, variance ±15%. Always show the disclaimer.\n\n## Examples\n\n### Triggers\n\n- \"Give me the short version first.\"\n- \"How many tokens will your answer use?\"\n- \"Respond at 50% depth.\"\n- \"I want the exhaustive answer, not the summary.\"\n- \"Dame la version corta y luego la detallada.\"\n\n### Does Not Trigger\n\n- \"What is a JWT token?\"\n- \"The checkout flow uses a payment token.\"\n- \"Is this normal?\"\n- \"Complete the refactor.\"\n- Follow-up questions after the user already chose a depth for the session\n\n## Source\n\nStandalone skill from [TBA — Token Budget Advisor for Claude Code](https:\u002F\u002Fgithub.com\u002FXabilimon1\u002FToken-Budget-Advisor-Claude-Code-).\nOriginal project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.\n","# Token Budget Advisor (TBA)\n\nIntercept the response flow to offer the user a choice about response depth **before** Claude answers.\n\n## When to Use\n\n- User wants to control how long or detailed a response is\n- User mentions tokens, budget, depth, or response length\n- User says \"short version\", \"tldr\", \"brief\", \"al 25%\", \"exhaustive\", etc.\n- Any time the user wants to choose depth\u002Fdetail level upfront\n\n**Do not trigger** when: user already set a level this session (maintain it silently), or the answer is trivially one line.\n\n## How It Works\n\n### Step 1 — Estimate input tokens\n\nUse the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.\n\nUse the same calibration guidance as [context-budget](..\u002Fcontext-budget\u002FSKILL.md):\n\n- prose: `words × 1.3`\n- code-heavy or mixed\u002Fcode blocks: `chars \u002F 4`\n\nFor mixed content, use the dominant content type and keep the estimate heuristic.\n\n### Step 2 — Estimate response size by complexity\n\nClassify the prompt, then apply the multiplier range to get the full response window:\n\n| Complexity   | Multiplier range | Example prompts                                      |\n|--------------|------------------|------------------------------------------------------|\n| Simple       | 3× – 8×          | \"What is X?\", yes\u002Fno, single fact                   |\n| Medium       | 8× – 20×         | \"How does X work?\"                                  |\n| Medium-High  | 10× – 25×        | Code request with context                           |\n| Complex      | 15× – 40×        | Multi-part analysis, comparisons, architecture      |\n| Creative     | 10× – 30×        | Stories, essays, narrative writing                  |\n\nResponse window = `input_tokens × mult_min` to `input_tokens × mult_max` (but don’t exceed your model’s configured output-token limit).\n\n### Step 3 — Present depth options\n\nPresent this block **before** answering, using the actual estimated numbers:\n\n```\nAnalyzing your prompt...\n\nInput: ~[N] tokens  |  Type: [type]  |  Complexity: [level]  |  Language: [lang]\n\nChoose your depth level:\n\n[1] Essential   (25%)  ->  ~[tokens]   Direct answer only, no preamble\n[2] Moderate    (50%)  ->  ~[tokens]   Answer + context + 1 example\n[3] Detailed    (75%)  ->  ~[tokens]   Full answer with alternatives\n[4] Exhaustive (100%)  ->  ~[tokens]   Everything, no limits\n\nWhich level? (1-4 or say \"25% depth\", \"50% depth\", \"75% depth\", \"100% depth\")\n\nPrecision: heuristic estimate ~85-90% accuracy (±15%).\n```\n\nLevel token estimates (within the response window):\n- 25%  → `min + (max - min) × 0.25`\n- 50%  → `min + (max - min) × 0.50`\n- 75%  → `min + (max - min) × 0.75`\n- 100% → `max`\n\n### Step 4 — Respond at the chosen level\n\n| Level            | Target length       | Include                                             | Omit                                              |\n|------------------|---------------------|-----------------------------------------------------|---------------------------------------------------|\n| 25% Essential    | 2-4 sentences max   | Direct answer, key conclusion                       | Context, examples, nuance, alternatives           |\n| 50% Moderate     | 1-3 paragraphs      | Answer + necessary context + 1 example              | Deep analysis, edge cases, references             |\n| 75% Detailed     | Structured response | Multiple examples, pros\u002Fcons, alternatives          | Extreme edge cases, exhaustive references         |\n| 100% Exhaustive  | No restriction      | Everything — full analysis, all code, all perspectives | Nothing                                        |\n\n## Shortcuts — skip the question\n\nIf the user already signals a level, respond at that level immediately without asking:\n\n| What they say                                      | Level |\n|----------------------------------------------------|-------|\n| \"1\" \u002F \"25% depth\" \u002F \"short version\" \u002F \"brief answer\" \u002F \"tldr\"  | 25%   |\n| \"2\" \u002F \"50% depth\" \u002F \"moderate depth\" \u002F \"balanced answer\"        | 50%   |\n| \"3\" \u002F \"75% depth\" \u002F \"detailed answer\" \u002F \"thorough answer\"       | 75%   |\n| \"4\" \u002F \"100% depth\" \u002F \"exhaustive answer\" \u002F \"full deep dive\"     | 100%  |\n\nIf the user set a level earlier in the session, **maintain it silently** for subsequent responses unless they change it.\n\n## Precision note\n\nThis skill uses heuristic estimation — no real tokenizer. Accuracy ~85-90%, variance ±15%. Always show the disclaimer.\n\n## Examples\n\n### Triggers\n\n- \"Give me the short version first.\"\n- \"How many tokens will your answer use?\"\n- \"Respond at 50% depth.\"\n- \"I want the exhaustive answer, not the summary.\"\n- \"Dame la version corta y luego la detallada.\"\n\n### Does Not Trigger\n\n- \"What is a JWT token?\"\n- \"The checkout flow uses a payment token.\"\n- \"Is this normal?\"\n- \"Complete the refactor.\"\n- Follow-up questions after the user already chose a depth for the session\n\n## Source\n\nStandalone skill from [TBA — Token Budget Advisor for Claude Code](https:\u002F\u002Fgithub.com\u002FXabilimon1\u002FToken-Budget-Advisor-Claude-Code-).\nOriginal project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.\n",{"name":5,"origin":24,"description":7},"community","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Ftoken-budget-advisor",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill token-budget-advisor",{"tool":41,"command":42},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill token-budget-advisor",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill token-budget-advisor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Ftoken-budget-advisor\u002FSKILL.md",true,[],[50,58,65,73,81,89],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":54,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":69,"sourceHtmlUrl":70,"categories":71,"keywords":72,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":85,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":90,"name":91,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]