{"slug":"appautomaton-consultant","name":"consultant","description":"Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets.","long_description":"---\nname: consultant\ndescription: >\n Think and deliver like a management consultant from McKinsey, BCG, or Bain.\n Use when the user wants to: (1) Structure a business problem with\n hypothesis-driven decomposition, (2) Run strategy analysis with professional\n frameworks: market sizing, competitive landscape, financial modeling,\n SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries,\n strategy deck outlines, decision memos, (4) Apply firm-specific methodology:\n McKinsey verdict-first, BCG framework-first, or Bain decision-first,\n (5) Package analysis for non-consulting audiences: investor pitches,\n board presentations, conference talks.\n Produces structured analysis and deliverable CONTENT. For visual\n production, hand off to a delivery skill for slides, documents,\n or spreadsheets.\nmetadata:\n short-description: MBB-grade strategy analysis, problem solving, and executive deliverables\n---\n\n# Consultant Skill\n\n## 1. What This Skill Does\n\n- **Input**: Business problem, strategic question, or analysis request.\n- **Output**: Structured analysis, recommendations, and deliverable content (markdown).\n- This skill produces **thinking**: analytical structure, argument logic, and content.\n- Does NOT produce visuals or specify visualization types. Hand off to a delivery skill for slides, documents, or spreadsheets.\n- Composition model: consultant provides what-to-say and what-to-prove. Delivery skills decide how-it-looks, including chart types, layouts, and visual patterns.\n\n---\n\n## 2. Behavioral Instincts\n\n**1. Hypothesis first.** If you can't state what you're testing, you're browsing, not analyzing.\n\n**2. Answer first.** State the recommendation before the evidence. The decision-maker reads slide 3, not slide 30. Pyramid Principle: conclusion → supporting arguments → data. If the reader stops after one sentence, they should have your answer.\n\n**3. So what?** Every finding must answer \"so what does this mean for the decision?\" \"Revenue grew 8%\" is data. \"Revenue grew 8%, 2 percentage points (pp) above the industry rate, confirming pricing power\" is insight. Facts without implications are noise. (\"pp\" = percentage points: a 10% margin declining to 8% is a 2 pp drop, not a 2% drop.)\n\n**4. One message per unit.** Each slide/section/paragraph: ONE message. Test: can you say it in one sentence? If not, split.\n\n**5. Quantify everything.** Attach a number, range, or confidence level to every claim. \"Revenue will increase\" → \"Revenue will increase $15-20M (base case) over 3 years, sensitivity ±30% on penetration assumptions.\" Unquantified claims erode credibility.\n\n**6. Three options maximum for executive decisions.** During analysis, a wider set is acceptable before narrowing.\n\n---\n\n## 3. Evidence Policy\n\n- **Source + year.** Every external data point gets a source citation and date. \"The US healthcare market is $4.3T (CMS, 2024)\", not just \"$4.3T.\"\n- **Show ranges, not points.** Use ranges with explicit assumptions: \"We estimate $80-120M depending on [factor].\"\n- **Confidence labels.** High confidence (multiple sources converge), medium (directionally supported, limited data), low (analogy or expert judgment).\n- Never generate fictional benchmarks or statistics. Mark every assumption that could change the conclusion.\n\n---\n\n## 4. Execution Algorithm\n\nThe default sequence for any consulting task. If a firm process file is loaded in step 2, it REPLACES steps 3-5. Steps 1 (INTAKE), 2 (ROUTE), and 6 (DELIVER) always apply.\n\n**Steps 3-5 are iterative, not linear.** The first pass produces a hypothesis-driven outline (v1). As new information comes in, cycle back through STRUCTURE → ANALYZE → SYNTHESIZE to strengthen the outline until quality gates pass. Then DELIVER. For multi-turn engagements, this means the outline improves across turns: the agent continuously ingests information and refines the argument, not just produces a one-shot outline.\n\n```\n1. INTAKE Clarify the question. Confirm problem understanding.\n → Actions: Ask 1-3 clarifying questions to form a problem statement.\n What decision is this analysis meant to inform?\n What constraints exist (time, data, scope)?\n → Complete when: Problem statement is confirmed by user.\n → A brief is complete when it contains: problem statement,\n scope/constraints, the decision it informs, and the client's\n specific situation (names, numbers, competitive context).\n If complete: skip to ROUTE.\n → If context is insufficient: ask the minimum questions needed\n to form a problem statement. Do not over-interview.\n\n2. ROUTE Select mode based on problem structure (see §7).\n Classify engagement type if applicable (see §8 engagement row).\n Load appropriate reference files per routing table (see §8).\n → Actions: Read routing table, select firm mode or generic mode,\n load reference files. If the task matches one of 8 engagement\n archetypes (cost, growth, M&A, pricing, digital, org, commercial,\n market entry), load engagements.md for pillar architecture and\n kill conditions.\n → Complete when: Mode is selected and stated. References are loaded.\n → If no firm mode is specified and no strong signal exists:\n default to the shared method (thinking.md + communication.md)\n without firm overlay. State this choice.\n → If two modes seem equally applicable: pause and present\n both options with trade-offs. Let the user choose.\n\n3. STRUCTURE Decompose the problem (issue tree, option map, or prism lenses).\n Form hypotheses at each branch.\n → Actions: Build decomposition per thinking.md methodology.\n Produce a problem structure artifact.\n → Complete when: MECE decomposition exists with hypotheses at leaves.\n → Forcing test: Name one real-world case that doesn't fit cleanly\n into your decomposition. If everything fits, you likely have\n overlapping categories.\n → If problem is high-stakes or novel: present decomposition\n for user review before proceeding.\n\n4. ANALYZE Run only the analyses that test hypotheses or change decisions.\n Prioritize by confidence: lowest-confidence hypotheses first,\n highest-confidence last. Stop when confidence is sufficient.\n → Actions: Before executing, scan the hypotheses from\n STRUCTURE and identify what data would resolve each.\n Group independent questions. They can be investigated\n concurrently rather than sequentially.\n Use web search for external data when relevant.\n Use user's provided data when available. Apply domain\n reference files loaded in ROUTE. Persist each research\n finding to `analysis/` as you go. Don't wait until done.\n → Complete when: Each hypothesis is supported, refuted, or\n explicitly marked inconclusive with stated reason.\n → Research priority: Hypotheses <50% confidence → analyze first.\n Hypotheses >80% confidence → analyze last (or skip if\n low-confidence findings haven't changed the structure).\n → Kill at 30%: If 30% of evidence contradicts a hypothesis,\n kill it and replace. Don't accumulate confirming evidence.\n Update the outline immediately when a hypothesis dies.\n → Forcing test: Before each analysis, ask: \"If this confirms\n my hypothesis, does it change the recommendation? If it\n disconfirms, does it change the recommendation?\"\n If neither → skip it.\n → If data is unavailable: state assumptions explicitly,\n mark confidence as low, and proceed.\n → If data is contradictory: flag the contradiction,\n explain which source you weight more and why.\n\n5. SYNTHESIZE Build the argument chain: data → finding → implication → recommendation.\n Resolve contradictions and flag remaining uncertainty.\n Update the outline with confirmed findings.\n → Actions: Build the evidence chain per frameworks.md §3.\n Test against quality gates (§14).\n Update outline artifact: replace hypothesis titles with\n confirmed findings. Save updated version.\n → Complete when: Governing thought is formed and every\n recommendation traces to data. Quality gates (§14) pass.\n → If quality gates fail: cycle back.\n - Helicopter test fails → STRUCTURE (pillar architecture wrong)\n - Fragility test fails → ANALYZE (weak finding needs more data)\n - Specificity test fails → ANALYZE (need client-specific data)\n - Skeptic test fails → SYNTHESIZE (counterargument not addressed)\n → Forcing test: Remove your strongest finding. Does the\n recommendation change? If not, that finding isn't load-bearing.\n Find the one that is.\n → What is the one thing you did NOT analyze that could flip\n the answer? If something exists, flag it as a risk.\n → If findings contradict the user's original framing:\n pause, present the contradiction, let the user decide\n whether to revise the framing.\n\n6. DELIVER Format per output contract (§13).\n Run quality gates (§14) before presenting.\n If handing off to a delivery skill, produce the handoff artifact (§10).\n For multi-turn engagements, persist artifacts per §11.\n → Actions: Select output format, apply quality gates,\n present to user.\n → Complete when: Output meets the relevant output contract.\n```\n\n---\n\n## 5. Interaction Protocol\n\nWhen to pause for user input vs. proceed autonomously.\n\n| Step | Default behavior | Pause when |\n|---|---|---|\n| INTAKE | Ask 1-3 clarifying questions | Always, unless complete brief provided (skip to ROUTE) |\n| ROUTE | State suggested mode, proceed | Two modes seem equally applicable |\n| STRUCTURE | Present decomposition, proceed | Problem is high-stakes or novel |\n| ANALYZE | Proceed autonomously | Data is missing or contradictory |\n| SYNTHESIZE | Proceed autonomously | Findings contradict user's framing |\n| DELIVER | Present output | Always (final quality gate) |\n\n**Single-turn tasks** (narrow scope, clear question): compress INTAKE through DELIVER into one response. Don't ceremony-pad a simple question.\n\n**Multi-turn engagements** (broad scope, iterative): checkpoint after STRUCTURE and again after SYNTHESIZE. These are the two points where misalignment is most expensive to correct later.\n\n---\n\n## 6. Agent Anti-Patterns\n\nLLM-specific failure modes to avoid.\n\n1. **Framework tourism.** Don't present a framework because it exists in references. Only use frameworks that test a hypothesis or change a decision.\n2. **Instinct recitation.** Don't enumerate the behavioral instincts as a preamble to analysis. They're for internal governance, not output decoration.\n3. **Overlay stacking.** Don't apply all three firm overlays when the user asked for one. One firm mode per engagement unless explicitly requested.\n4. **Hedge paralysis.** Don't over-qualify every claim to the point of analysis paralysis. State the answer, then caveat. The recomme","tagline":"Think and deliver like a management consultant from McKinsey, BCG, or Bain. 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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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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 \"consultant\" as a Claude Code skill from https://github.com/appautomaton/presentation/tree/main/consultant. 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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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 \"consultant\" from https://github.com/appautomaton/presentation/tree/main/consultant 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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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/appautomaton-consultant/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/appautomaton-consultant"},"trust":{"score":70,"label":"Manual review","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"54 GitHub stars","repoActivity":"54 stars, 4 forks","lastPushed":"3d since push","license":"Unknown","repository":"https://github.com/appautomaton/presentation/tree/main/consultant","install":"npx skills add appautomaton/presentation --skill consultant","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":["research","agent-skill"],"known_risks":["Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.","Financial research output is not financial advice; require human review before any live investment decision.","License is unclear","Quality score needs review","GitHub adoption: 54 GitHub stars","Stars/forks activity: 54 stars, 4 forks; issue activity unavailable in current metadata","License clarity: Unknown"]},"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":74,"risk_level":"needs_review","risk_label":"Needs review","warnings":["License is unclear","Financial research output is not financial advice; require human review before any live investment decision","Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","GitHub adoption: 54 GitHub stars","Stars/forks activity: 54 stars, 4 forks; issue activity unavailable in current metadata","License clarity: Unknown"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":59,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"3d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.","License is unclear","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","GitHub adoption: 54 GitHub stars"],"agent_contract":{"task_input":"Use consultant in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 70/100 Manual review","Audit: 74/100 Needs review","Safety: 54/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"appautomaton-consultant (consultant)","install_command":"npx skills add appautomaton/presentation --skill consultant","risk_summary":"Needs review; Experimental; 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":"","skill_slug":"appautomaton-consultant","task":"Use consultant 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/appautomaton-consultant","api":"https://www.openagentskill.com/api/agent/skills/appautomaton-consultant","audit":"https://www.openagentskill.com/skills/appautomaton-consultant/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=appautomaton-consultant&task=Use%20consultant%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20consultant%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20consultant%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/appautomaton-consultant/install","manifest":"https://www.openagentskill.com/api/registry/manifest/appautomaton-consultant"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"Research agents","description":"I need my agent to research a topic, compare sources, and produce a concise report.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"finance-quant","title":"Finance and quant"},{"slug":"github-automation","title":"GitHub automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add appautomaton/presentation --skill consultant","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":54,"starsLabel":"54","forks":4,"license":"Unknown","qualityScore":59,"trustScore":70,"auditScore":74},"maintenance":{"status":"fresh","label":"3d since push","daysSincePush":3,"lastPushedAt":"2026-08-20T05:02:54+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["License is unclear","Financial research output is not financial advice; require human review before any live investment decision","Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"coverageTags":["Research","Research agents","agent-skill"]},"audit":{"audit_score":74,"risk_level":"needs_review","risk_label":"Needs review","quality_score":59,"trust_score":70,"maintenance_score":100,"security_score":75,"install_score":92,"warnings":["License is unclear","Financial research output is not financial advice; require human review before any live investment decision","Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","GitHub adoption: 54 GitHub stars","Stars/forks activity: 54 stars, 4 forks; issue activity unavailable in current metadata","License clarity: Unknown"]},"quality_signals":{"model":"v2","star_score":12.18,"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":"finance-quant","title":"Finance and quant","url":"https://www.openagentskill.com/use-cases/finance-quant"},{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"},{"slug":"document-processing","title":"Document processing","url":"https://www.openagentskill.com/use-cases/document-processing"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"}],"install":"npx skills add appautomaton/presentation --skill consultant","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install appautomaton-consultant","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 \"consultant\" agent skill from https://github.com/appautomaton/presentation/tree/main/consultant. 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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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 \"consultant\" as a Claude Code skill from https://github.com/appautomaton/presentation/tree/main/consultant. 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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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 \"consultant\" from https://github.com/appautomaton/presentation/tree/main/consultant 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: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user wants to: (1) Structure a business problem with hypothesis-driven decomposition, (2) Run strategy analysis with professional frameworks: market sizing, competitive landscape, financial modeling, SWOT, Porter's, (3) Build MBB-quality deliverables: executive summaries, strategy deck outlines, decision memos, (4) Apply firm-specific methodology: McKinsey verdict-first, BCG framework-first, or Bain decision-first, (5) Package analysis for non-consulting audiences: investor pitches, board presentations, conference talks. Produces structured analysis and deliverable CONTENT. For visual production, hand off to a delivery skill for slides, documents, or spreadsheets. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_\",\"skill_slug\":\"appautomaton-consultant\",\"task\":\"Install consultant\",\"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/appautomaton/presentation/tree/main/consultant","github_repo":"appautomaton/presentation","version":"1.0.0","license":"Unknown","urls":{"web":"https://www.openagentskill.com/skills/appautomaton-consultant","repository":"https://github.com/appautomaton/presentation/tree/main/consultant","api":"/api/agent/skills/appautomaton-consultant","install_api":"/api/skills/appautomaton-consultant/install"},"meta":{"created_at":"2026-08-21T14:35:30.503328+00:00","updated_at":"2026-08-21T14:35:30.503328+00:00","agent_friendly":true}}