consultant

Tinjau · 62
Diindeks di Registry

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

Verified installs0
Star54
Versi1.0.0
Kualitas59/100 · Menjanjikan
Kepercayaan62/100 · Hanya sandbox
Audit74/100 · Perlu ditinjau

Profil aset

Riset dan pekerjaan pengetahuan

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Lihat kategori

Skenario

Agent riset

I need my agent to research a topic, compare sources, and produce a concise report.

Kecocokan Agent

Claude Code + CLI + Codex

Cocok untuk Codex, Claude Code, Cursor, CLI, atau Agent khusus.

Pasang

Siap

npx skills add appautomaton/presentation --skill consultant

Pemeliharaan

Terkini

2 hari sejak push

Risiko

Perlu ditinjau

Lisensi tidak jelas

Kualitas GitHub

54

59/100 Kualitas · 70/100 Kepercayaan

Tag cakupan

RisetAgent risetagent-skill

Catatan ulasan

Lisensi tidak jelas · Financial research output is not financial advice; require human review before any live investment decision

Kartu adopsi Agent

Kepercayaan, audit, dan kesiapan pemasangan dalam sekali lihat

Skor ini menggabungkan metadata repositori publik, sinyal ulasan OpenAgentSkill, kebaruan pemeliharaan, dan kesiapan pemasangan. Ini adalah sinyal shortlist, bukan pengganti peninjauan manusia.

Kualitas

Menjanjikan
59

Useful candidate, but compare it with alternatives before adopting.

Kepercayaan

Hanya sandbox
62

Kandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.

Audit

Perlu ditinjau
74

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Trust Score OpenAgentSkill v5

Tinjauan manusia sebelum pemasangan

Jalankan hanya dalam sandbox dan bandingkan alternatif terdekat sebelum digunakan untuk kerja nyata.

CodexClaude CodeCursorOpenAgentSkill CLI

Star

54 star GitHub

Aktivitas repositori

54 star dan 4 fork

Pemeliharaan

2 hari sejak push

Lisensi

Tidak diketahui

Pasang

npx skills add appautomaton/presentation --skill consultant

Keamanan pemasangan

Jalur pemasangan paket atau runtime standar

Cakupan izin

Akses sistem file atau dokumen

Hasil Agent

Belum ada data hasil Agent

Dokumentasi

Usable metadata, review docs

Ringkasan risiko

Tinjau sebelum produksi

  • 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.
  • Lisensi tidak jelas
  • Quality score needs review

Kesiapan pemasangan

Jalur pemasangan tersedia

  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Lisensi tidak jelas
  • Belum ada bukti hasil Agent-Proven

Metadata yang dapat dibaca Agent

Data keputusan yang dapat dibaca mesin untuk skill ini.

Gunakan blok ini atau JSON tersemat untuk memutuskan apakah Agent perlu memasang skill ini, memilih alternatif, atau meminta tinjauan manusia terlebih dahulu.

Buka JSON

Tugas yang sesuai

  • Alur kerja Agent riset
  • Tim Claude Code
  • builders willing to evaluate younger projects
  • Sumber pencarian

Agent yang sesuai

CodexClaude CodeCursorOpenAgentSkill CLICLI

Keputusan pemasangan

Perintah
npx skills add appautomaton/presentation --skill consultant
Kebijakan
Tinjau
Tinjauan manusia
Ya

Kepercayaan dan risiko

Kepercayaan
62/100
Audit
74/100
Tingkat risiko
Perlu ditinjau

Lingkar hasil

Endpoint
/api/agent/outcome
ID event
resolve
Hasil
5

Perintah pemasangan

npx skills add appautomaton/presentation --skill consultant

Jangan gunakan ketika

  • Tim yang membutuhkan SLA dengan dukungan vendor
  • production agents without a repository review
  • Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.
  • Lisensi tidak jelas
  • Financial research output is not financial advice; require human review before any live investment decision

Keamanan Agent v2

54/100 · Hindari pemasangan otomatis

EksperimentalTinjau

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Selesaikan via API

Sedang

Browser automation

Skill may drive a browser or interact with web pages.

Sedang

Akses jaringan

Skill kemungkinan mengambil halaman jarak jauh, API, repositori, atau layanan eksternal.

Sedang

Akses sistem file

Skill dapat membaca atau menulis file proyek, dokumen, artefak yang dihasilkan, atau status workspace lokal.

  • Lisensi tidak jelas

Target pemasangan

Pasang skill ini di alur Agent Anda

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

skill install

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-consultant

Rencana resolusi Agent

Biarkan Agent memverifikasi kecocokan sebelum memasang.

API Resolve mengembalikan skill utama, alternatif, kebijakan keamanan, catatan audit, target pemasangan, dan prompt siap pakai.

Buka rencana teks

Agent harus memeriksa

  • 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.

Salin 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.

Serah-terima Agent

Berikan jalur pemasangan kepada Agent, bukan direktori lain.

Gunakan endpoint publik untuk mengambil perintah, checklist keamanan, prompt target, dan tautan kanonis.

Buka API pemasangan

Prompt Agent

Use consultant for this task. Review https://www.openagentskill.com/api/skills/appautomaton-consultant/install, then install with: npx skills add appautomaton/presentation --skill consultant

Metadata Registry

Profil yang dapat dibaca Agent untuk pemilihan skill otomatis.

API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.

Buka Manifest

Kecocokan Agent

60/100

Agent riset

Platform

Claude Code

Laporan audit

Perlu ditinjau · 74/100

Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.

Lihat laporan auditLihat laporan evaluasi

Panel keputusan Agent

Fallback candidate for Research agents

Prototype with this skill first; keep a fallback candidate ready.

60
Kesiapan
Prototipe
Tahap

Peran di stack

Kandidat cadangan

Kecocokan utama

Agent riset

Label kepercayaan

Buat prototipe dulu

Jalur pemasangan

Perintah siap

Gunakan saat

  • Alur kerja Agent riset
  • Tim Claude Code
  • builders willing to evaluate younger projects

Bukti

  • recent repository activity
  • install command or GitHub repo available
  • profil kualitas 59/100
  • 4 event interaksi OpenAgentSkill

tinjau dulu

  • Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.

Jalur implementasi

  1. 1Pasang di Agent sandbox dan jalankan satu tugas Agent riset dari awal hingga akhir.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Profil kepercayaan

Hanya sandbox

Kandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.

62
Trust Score OpenAgentSkill

Adopsi GitHub

Periksa

54 star GitHub

Aktivitas star/fork

Periksa

54 star dan 4 fork; aktivitas issue tidak tersedia dalam metadata saat ini

Pemeliharaan terbaru

Lulus

2 hari sejak push

Kejelasan lisensi

Periksa

Tidak diketahui

Sinyal positif

  • Tinjauan AI disetujui
  • Jalur pemasangan tersedia
  • Bukti repositori tersedia
  • Repositori yang baru dipelihara
  • Perintah pemasangan tidak memiliki pola berisiko tinggi yang jelas
  • Loop hasil siap tetapi membutuhkan eksekusi Agent nyata pertama

Tinjau sebelum memasang

  • 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.
  • Lisensi tidak jelas
  • 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
  • Belum ada laporan hasil Agent nyata
  • Tinjauan manusia diperlukan sebelum pemasangan tanpa pengawasan

Tindakan yang disarankan

Jalankan hanya dalam sandbox dan bandingkan alternatif terdekat sebelum digunakan untuk kerja nyata.

Profil kualitas

Menjanjikan kandidat untuk alur kerja Agent

Useful candidate, but compare it with alternatives before adopting.

59
Star GitHub
54
Keterkinian
2 hari lalu
Siap dipasang
Ya
Lisensi
Tidak diketahui
Tinjau sebelum memasang: Repository license is unknown; no explicit open-source license detected, which may hinder adoption and reuse.

Kecocokan alur kerja

Gunakan skill ini pada skenario berikut

Kecocokan alur kerja

Tambahkan ke alur kerja lengkap

Daftar alternatif

Bandingkan sebelum memasang

Similar skills that may fit this task.

Bandingkan semua

Ringkasan

--- 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

Detail teknis

Versi
1.0.0
Lisensi
Unknown
Pembaruan terakhir
21 Agu 2026
Diterbitkan
21 Agu 2026

Ringkasan keputusan

Kandidat cadangan

60
Siap
Prototipe
Tahap

recent repository activity

Audit

Tinjauan pemasangan

Tinjauan pemasangan dan adopsi

74
Perlu ditinjau
Keamanan
75/100
Pemeliharaan
100/100
Pasang
92/100
Buka audit lengkapLihat laporan evaluasi

Bukti tervalidasi Agent

Bukti tervalidasi Agent

Laporan hasil setelah resolve, tinjau, pasang, dan satu eksekusi terbatas.

0
Terbukti
Needs first agent runPasang otomatis: tinjau duluTerakhir: Tidak diketahui
Tingkat sukses
Kegagalan terbaru
Hasil
0
Kualitas output
Gagal
0
Tidak relevan
0
Pemasangan
0
Diblokir risiko
0
Perlu penyiapan
0
Produksi
0

Belum ada data hasil Agent. Eksekusi pertama dapat melaporkan keberhasilan, kebutuhan setup, blok risiko, kegagalan, atau tidak relevan melalui /api/agent/outcome.

Pasang

Tambahkan ke alur Agent

Gratis dan sumber terbuka. Tinjau laporan sebelum memasang pada Agent produksi.

Siklus pertumbuhan

Kit berbagi

X

Draf berbasis skenario untuk consultant, siap untuk posting manual di X.

Catatan kurator
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
Buka draf X
Balasan opsional dengan perintah pemasangan
Listing + install path for consultant:
https://www.openagentskill.com/skills/appautomaton-consultant?ref=x

Install: npx skills add appautomaton/presentation --skill consultant
Buka draf balasan

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Diindeks oleh
Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan appautomaton, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/appautomaton-consultant?metric=listed&label=Listed)](https://www.openagentskill.com/skills/appautomaton-consultant)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/appautomaton-consultant?metric=trust&label=Trust)](https://www.openagentskill.com/skills/appautomaton-consultant)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/appautomaton-consultant?metric=audit&label=Audit)](https://www.openagentskill.com/skills/appautomaton-consultant/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/appautomaton-consultant?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/appautomaton-consultant)

Penulis

A

appautomaton

@appautomaton

Kecocokan platform

Sinyal kesehatan

Star GitHub
54
Skor kualitas
35/100
Push GitHub terakhir
20 Agu 2026
Petunjuk framework
Tidak diketahui
Tampilan OpenAgentSkill
4
Salinan pemasangan
0
Klik keluar
0

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.

Kepercayaan & keamanan

Hanya sandbox

62
  • Adopsi GitHub54 star GitHubPeriksa
  • Aktivitas star/fork54 star dan 4 fork; aktivitas issue tidak tersedia dalam metadata saat iniPeriksa
  • Pemeliharaan terbaru2 hari sejak pushLulus
  • Kejelasan lisensiTidak diketahuiPeriksa
  • Kelengkapan README/SKILL.mdMetadata publik memerlukan konteks README/SKILL.md yang lebih kuatInfo
  • Risiko dependensi/runtimeTidak ada petunjuk risiko dependensi besar dalam metadata publikLulus