consultant
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 m
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add appautomaton/presentation --skill consultant
Maintenance
fresh
2d since push
Risk
Needs review
License is unclear
GitHub quality
54
59/100 Quality · 70/100 Trust
Coverage tags
Review notes
License is unclear · Financial research output is not financial advice; require human review before any live investment decision
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
54 GitHub stars
Repo activity
54 stars, 4 forks
Maintenance
2d since push
License
Unknown
Install
npx skills add appautomaton/presentation --skill consultant
Install safety
standard package or runtime install path
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Review before production
- 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
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is unclear
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add appautomaton/presentation --skill consultant
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 62/100
- Audit
- 74/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add appautomaton/presentation --skill consultantDo 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
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Agent safety v2
54/100 · Avoid automatic install
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
medium
Browser automation
Skill may drive a browser or interact with web pages.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- License is unclear
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install appautomaton-consultantAgent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20consultant%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20consultant%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/appautomaton-consultant/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use consultant in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20consultant%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/appautomaton-consultant/install
Install command: npx skills add appautomaton/presentation --skill consultant
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/appautomaton-consultant/install
LLM text format
/api/skills/appautomaton-consultant/install?format=text
Find alternatives
/api/skills/search?q=consultant&limit=3
Agent prompt
Use consultant for this task. Review https://www.openagentskill.com/api/skills/appautomaton-consultant/install, then install with: npx skills add appautomaton/presentation --skill consultantRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/appautomaton-consultant
LLM text
/api/registry/manifest/appautomaton-consultant?format=text
Install alias
/api/registry/install/appautomaton-consultant
Recommend
/api/registry/recommend?task=Use%20consultant%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 74/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 59/100 quality profile
- 4 OpenAgentSkill engagement events
review first
- Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Sandbox only
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
CHECK54 GitHub stars
Stars/forks activity
CHECK54 stars, 4 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
CHECKUnknown
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- 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
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Analyze markets
Finance and quant
I need my agent to analyze markets, financial data, filings, portfolios, and quant strategies.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
Last30days Skill
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Academic Research Skills
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GPT Researcher
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DeepResearch
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Overview
--- 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. metadata: short-description: MBB-grade strategy analysis, problem solving, and executive deliverables ---
# Consultant Skill
## 1. What This Skill Does
- **Input**: Business problem, strategic question, or analysis request. - **Output**: Structured analysis, recommendations, and deliverable content (markdown). - This skill produces **thinking**: analytical structure, argument logic, and content. - Does NOT produce visuals or specify visualization types. Hand off to a delivery skill for slides, documents, or spreadsheets. - Composition model: consultant provides what-to-say and what-to-prove. Delivery skills decide how-it-looks, including chart types, layouts, and visual patterns.
---
## 2. Behavioral Instincts
**1. Hypothesis first.** If you can't state what you're testing, you're browsing, not analyzing.
**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.
**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.)
**4. One message per unit.** Each slide/section/paragraph: ONE message. Test: can you say it in one sentence? If not, split.
**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.
**6. Three options maximum for executive decisions.** During analysis, a wider set is acceptable before narrowing.
---
## 3. Evidence Policy
- **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." - **Show ranges, not points.** Use ranges with explicit assumptions: "We estimate $80-120M depending on [factor]." - **Confidence labels.** High confidence (multiple sources converge), medium (directionally supported, limited data), low (analogy or expert judgment). - Never generate fictional benchmarks or statistics. Mark every assumption that could change the conclusion.
---
## 4. Execution Algorithm
The 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.
**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.
``` 1. INTAKE Clarify the question. Confirm problem understanding. → Actions: Ask 1-3 clarifying questions to form a problem statement. What decision is this analysis meant to inform? What constraints exist (time, data, scope)? → Complete when: Problem statement is confirmed by user. → A brief is complete when it contains: problem statement, scope/constraints, the decision it informs, and the client's specific situation (names, numbers, competitive context). If complete: skip to ROUTE. → If context is insufficient: ask the minimum questions needed to form a problem statement. Do not over-interview.
2. ROUTE Select mode based on problem structure (see §7). Classify engagement type if applicable (see §8 engagement row). Load appropriate reference files per routing table (see §8). → Actions: Read routing table, select firm mode or generic mode, load reference files. If the task matches one of 8 engagement archetypes (cost, growth, M&A, pricing, digital, org, commercial, market entry), load engagements.md for pillar architecture and kill conditions. → Complete when: Mode is selected and stated. References are loaded. → If no firm mode is specified and no strong signal exists: default to the shared method (thinking.md + communication.md) without firm overlay. State this choice. → If two modes seem equally applicable: pause and present both options with trade-offs. Let the user choose.
3. STRUCTURE Decompose the problem (issue tree, option map, or prism lenses). Form hypotheses at each branch. → Actions: Build decomposition per thinking.md methodology. Produce a problem structure artifact. → Complete when: MECE decomposition exists with hypotheses at leaves. → Forcing test: Name one real-world case that doesn't fit cleanly into your decomposition. If everything fits, you likely have overlapping categories. → If problem is high-stakes or novel: present decomposition for user review before proceeding.
4. ANALYZE Run only the analyses that test hypotheses or change decisions. Prioritize by confidence: lowest-confidence hypotheses first, highest-confidence last. Stop when confidence is sufficient. → Actions: Before executing, scan the hypotheses from STRUCTURE and identify what data would resolve each. Group independent questions. They can be investigated concurrently rather than sequentially. Use web search for external data when relevant. Use user's provided data when available. Apply domain reference files loaded in ROUTE. Persist each research finding to `analysis/` as you go. Don't wait until done. → Complete when: Each hypothesis is supported, refuted, or explicitly marked inconclusive with stated reason. → Research priority: Hypotheses <50% confidence → analyze first. Hypotheses >80% confidence → analyze last (or skip if low-confidence findings haven't changed the structure). → Kill at 30%: If 30% of evidence contradicts a hypothesis, kill it and replace. Don't accumulate confirming evidence. Update the outline immediately when a hypothesis dies. → Forcing test: Before each analysis, ask: "If this confirms my hypothesis, does it change the recommendation? If it disconfirms, does it change the recommendation?" If neither → skip it. → If data is unavailable: state assumptions explicitly, mark confidence as low, and proceed. → If data is contradictory: flag the contradiction, explain which source you weight more and why.
5. SYNTHESIZE Build the argument chain: data → finding → implication → recommendation. Resolve contradictions and flag remaining uncertainty. Update the outline with confirmed findings. → Actions: Build the evidence chain per frameworks.md §3. Test against quality gates (§14). Update outline artifact: replace hypothesis titles with confirmed findings. Save updated version. → Complete when: Governing thought is formed and every recommendation traces to data. Quality gates (§14) pass. → If quality gates fail: cycle back. - Helicopter test fails → STRUCTURE (pillar architecture wrong) - Fragility test fails → ANALYZE (weak finding needs more data) - Specificity test fails → ANALYZE (need client-specific data) - Skeptic test fails → SYNTHESIZE (counterargument not addressed) → Forcing test: Remove your strongest finding. Does the recommendation change? If not, that finding isn't load-bearing. Find the one that is. → What is the one thing you did NOT analyze that could flip the answer? If something exists, flag it as a risk. → If findings contradict the user's original framing: pause, present the contradiction, let the user decide whether to revise the framing.
6. DELIVER Format per output contract (§13). Run quality gates (§14) before presenting. If handing off to a delivery skill, produce the handoff artifact (§10). For multi-turn engagements, persist artifacts per §11. → Actions: Select output format, apply quality gates, present to user. → Complete when: Output meets the relevant output contract. ```
---
## 5. Interaction Protocol
When to pause for user input vs. proceed autonomously.
| Step | Default behavior | Pause when | |---|---|---| | INTAKE | Ask 1-3 clarifying questions | Always, unless complete brief provided (skip to ROUTE) | | ROUTE | State suggested mode, proceed | Two modes seem equally applicable | | STRUCTURE | Present decomposition, proceed | Problem is high-stakes or novel | | ANALYZE | Proceed autonomously | Data is missing or contradictory | | SYNTHESIZE | Proceed autonomously | Findings contradict user's framing | | DELIVER | Present output | Always (final quality gate) |
**Single-turn tasks** (narrow scope, clear question): compress INTAKE through DELIVER into one response. Don't ceremony-pad a simple question.
**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.
---
## 6. Agent Anti-Patterns
LLM-specific failure modes to avoid.
1. **Framework tourism.** Don't present a framework because it exists in references. Only use frameworks that test a hypothesis or change a decision. 2. **Instinct recitation.** Don't enumerate the behavioral instincts as a preamble to analysis. They're for internal governance, not output decoration. 3. **Overlay stacking.** Don't apply all three firm overlays when the user asked for one. One firm mode per engagement unless explicitly requested. 4. **Hedge paralysis.** Don't over-qualify every claim to the point of analysis paralysis. State the answer, then caveat. The recomme
Technical details
- Version
- 1.0.0
- License
- Unknown
- Last updated
- Aug 21, 2026
- Published
- Aug 21, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 75/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for consultant, ready for a manual X post.
consultant: Think and deliver like a management consultant from McKinsey, BCG, or Bain. Use when the user... 54 stars https://www.openagentskill.com/skills/appautomaton-consultant?ref=x
Optional reply with install command
Listing + install path for consultant: https://www.openagentskill.com/skills/appautomaton-consultant?ref=x Install: npx skills add appautomaton/presentation --skill consultant
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- appautomaton
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to appautomaton but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/appautomaton-consultant)
[](https://www.openagentskill.com/skills/appautomaton-consultant)
[](https://www.openagentskill.com/skills/appautomaton-consultant/audit)
[](https://www.openagentskill.com/skills/appautomaton-consultant)Author
appautomaton
@appautomaton
Tags
Platform fit
Health signals
- GitHub stars
- 54
- Quality score
- 35/100
- Last GitHub push
- Aug 20, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 4
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Sandbox only
- GitHub adoption54 GitHub starsCHECK
- Stars/forks activity54 stars, 4 forks; issue activity unavailable in current metadataCHECK
- Recent maintenance2d since pushPASS
- License clarityUnknownCHECK
- README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
- Dependency/runtime riskno major dependency risk hints in public metadataPASS
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