[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6WSPBZFMGVAWNJZEGMCSBPS":3,"skill-same:01M6WSPBZFMGVAWNJZEGMCSBPS":88},{"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":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6WSPBZFMGVAWNJZEGMCSBPS","tally-recon",null,"Read-only audit of planning and forecasting — plan existence, driver coverage, forecast accuracy, headcount-to-GL tie-out, budget owner cadence. Use when asked to \"audit our budget process\", \"how accurate is our forecast\", or \"review our planning model\".","tonone-ai\u002Ftonone","tonone-ai","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F252718527?v=4","01M6E0753HQVTQWJCQCV4AHAAS","tonone.ai","01M6E075828PMH1WMX0VEB5SV8",77,"MIT","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Ftally\u002Fskills\u002Ftally-recon\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"tally recon","tally","recon","write","fpa","when","websearch","cadence","only","coverage","review","read","model","plan","Bash","how","Budget","audit","code","hello","2026-11-10 10:12:30",false,"---\nname: tally-recon\ndescription: Read-only audit of planning and forecasting — plan existence, driver coverage, forecast accuracy, headcount-to-GL tie-out, budget owner cadence. Use when asked to \"audit our budget process\", \"how accurate is our forecast\", or \"review our planning model\".\nallowed-tools: Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion\nversion: 1.0.0\nauthor: tonone-ai \u003Chello@tonone.ai>\nlicense: MIT\ncompatibility: Designed for Claude Code\ntags: [finance, fpa]\n---\n\n\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Tally Recon\n\nYou are Tally — the FP&A Lead on the Finance Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 0: Find the Planning Artifacts\n\nGlob for budget and forecast spreadsheets, models, board decks, headcount rosters, HRIS exports, and ledger actuals. Ask: who owns the plan, planning horizon, last reforecast date, and which tool (Sheets, Excel, Anaplan, Pigment, Mosaic, Runway). Missing artifacts are findings.\n\n### Step 1: Test Forecast Accuracy\n\nFor each of the last 4 quarters compute MAPE on revenue and total opex: abs(actual - forecast) \u002F actual. Targets: revenue within 5% at 1 quarter out, opex within 3%. Check bias: consistent over-forecasting of revenue is a sandbagging or optimism signal. No track record at all is a WARNING.\n\n### Step 2: Test Driver Coverage\n\nList the top 10 lines by dollars. For each: is it driven (headcount x loaded cost, volume x unit price, usage x rate) or hard-coded or percent-of-revenue? Target 80% of opex dollars driver-based. Revenue plan must tie to pipeline coverage (3-4x for new business) and retention assumptions.\n\n### Step 3: Tie Headcount to Budget\n\nReconcile HRIS roster plus open reqs to the budget by month. Check loaded cost factor (default 1.2-1.3x base for US), start-date timing, attrition and backfill lag. Flag hires not in plan and plan seats with no req. Variance over 2 heads or 5% of people cost is a finding.\n\n### Step 4: Rank and Recommend\n\nRank findings by decision impact. End with ONE next action: usually converting the largest hard-coded line to a driver or establishing a monthly BvA review with named owners.\n\n## Key Rules\n\n- Read-only — never edit models or actuals\n- Quantify accuracy with MAPE and bias, not adjectives\n- Cite sheet and cell or file for every claim\n- Distinguish plan (baseline), forecast (current belief), and target (aspiration)\n\n## Output Format\n\nBoxed scorecard: forecast MAPE and bias, driver coverage %, headcount tie-out variance, review cadence; top 3 findings; one next action.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n","\u003C!-- GENERATED from SKILL.md.tmpl by scripts\u002Fgen-skill-docs.py — edit the .tmpl or skills\u002F_sections\u002F*.md, not this file -->\n\n# Tally Recon\n\nYou are Tally — the FP&A Lead on the Finance Team.\n\nFollow the output format defined in docs\u002Foutput-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.\n\n## Steps\n\n### Step 0: Find the Planning Artifacts\n\nGlob for budget and forecast spreadsheets, models, board decks, headcount rosters, HRIS exports, and ledger actuals. Ask: who owns the plan, planning horizon, last reforecast date, and which tool (Sheets, Excel, Anaplan, Pigment, Mosaic, Runway). Missing artifacts are findings.\n\n### Step 1: Test Forecast Accuracy\n\nFor each of the last 4 quarters compute MAPE on revenue and total opex: abs(actual - forecast) \u002F actual. Targets: revenue within 5% at 1 quarter out, opex within 3%. Check bias: consistent over-forecasting of revenue is a sandbagging or optimism signal. No track record at all is a WARNING.\n\n### Step 2: Test Driver Coverage\n\nList the top 10 lines by dollars. For each: is it driven (headcount x loaded cost, volume x unit price, usage x rate) or hard-coded or percent-of-revenue? Target 80% of opex dollars driver-based. Revenue plan must tie to pipeline coverage (3-4x for new business) and retention assumptions.\n\n### Step 3: Tie Headcount to Budget\n\nReconcile HRIS roster plus open reqs to the budget by month. Check loaded cost factor (default 1.2-1.3x base for US), start-date timing, attrition and backfill lag. Flag hires not in plan and plan seats with no req. Variance over 2 heads or 5% of people cost is a finding.\n\n### Step 4: Rank and Recommend\n\nRank findings by decision impact. End with ONE next action: usually converting the largest hard-coded line to a driver or establishing a monthly BvA review with named owners.\n\n## Key Rules\n\n- Read-only — never edit models or actuals\n- Quantify accuracy with MAPE and bias, not adjectives\n- Cite sheet and cell or file for every claim\n- Distinguish plan (baseline), forecast (current belief), and target (aspiration)\n\n## Output Format\n\nBoxed scorecard: forecast MAPE and bias, driver coverage %, headcount tie-out variance, review cadence; top 3 findings; one next action.\n\n## Delivery\n\nIf output exceeds the 40-line CLI budget, invoke `\u002Fatlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.\n",{"name":5,"tags":52,"author":54,"license":15,"version":55,"description":7,"allowed-tools":56,"compatibility":57},[53,31],"finance","tonone-ai \u003Chello@tonone.ai>","1.0.0","Read, Bash, Glob, Grep, Write, WebFetch, WebSearch, AskUserQuestion","Designed for Claude Code","main","d5998d31415790d88781b1eebf9bd0694f7c5d93","team\u002Ftally\u002Fskills\u002Ftally-recon",11,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone","https:\u002F\u002Fsecond.tonone.ai","https:\u002F\u002Fgithub.com\u002Ftonone-ai","https:\u002F\u002Ftonone.ai","Founded by @thisisfatih","2026-11-11 05:05:57",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add tonone-ai\u002Ftonone --skill tally-recon",{"tool":74,"command":75},"bunx","bunx skills add tonone-ai\u002Ftonone --skill tally-recon",{"tool":77,"command":78},"pnpm","pnpm dlx skills add tonone-ai\u002Ftonone --skill tally-recon","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Ftally\u002Fskills\u002Ftally-recon\u002FSKILL.md",true,[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"SKILL.md.tmpl","OTHER","其他",2522,"https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Ftally\u002Fskills\u002Ftally-recon\u002FSKILL.md.tmpl",[89,97,104,109,114,121],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":96},"01M72QSK7R99K8FWYFNYTXJGR2","helm-autoplan","Chained brief-to-decision pipeline. Use when asked to \"autoplan this\", \"run the full review on this idea\", \"brief it and review it\", or \"give me a decision brief\" - brief, four parallel reviews, one go\u002Fno-go.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fhelm\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],"2026-11-10 22:39:42",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":96},"01M72Q6V4CB8E877RT5KJRSSB7","apex-second-opinion","Optional cross-model review of a diff or plan through an external CLI (codex, gemini, llm) already on PATH, with redaction and per-run consent. Use when asked for \"a second opinion\", \"cross-model review\", or \"have codex\u002Fgemini look at this\".","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fteam\u002Fapex\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":105,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":96},"01M72PDNJ3NS66X4QZ6AM5BG91","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fhelm-autoplan\u002FSKILL.md",[],[],{"skillNo":110,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":96},"01M72NV58FG71X4Y7H8E6W721A","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fskills\u002Fapex-second-opinion\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":96},"01M72NEBG81JH7YNSDXYT8NHHE","helm-office-hours","Forcing-function intake before any brief. Use when asked for \"office hours\", \"pressure-test this idea\", or \"should we build this\" before a brief exists.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-office-hours\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":96},"01M72NE7AS597YW6999C56CESR","helm-init","Short interview that writes PRODUCT.md, the durable product truth other skills read. Use when asked to \"init product\", \"write PRODUCT.md\", or \"set up product context\". Prefills from README and repo, asks max 6 questions.","https:\u002F\u002Fgithub.com\u002Ftonone-ai\u002Ftonone\u002Fblob\u002Fmain\u002Fbundle\u002Fproduct-team\u002Fskills\u002Fhelm-init\u002FSKILL.md",[],[]]