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
Profil aset
Riset dan pekerjaan pengetahuan
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
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
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
MenjanjikanUseful candidate, but compare it with alternatives before adopting.
Kepercayaan
Hanya sandboxKandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.
Audit
Perlu ditinjauTinjauan 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.
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.
Tugas yang sesuai
- Alur kerja Agent riset
- Tim Claude Code
- builders willing to evaluate younger projects
- Sumber pencarian
Agent yang sesuai
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 consultantJangan 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
Skill alternatif
Last30days Skill
53.5K Star
npx skills add mvanhorn/last30days-skill -g
Skill alternatif
Academic Research Skills
38.4K Star
npx skills add Imbad0202/academic-research-skills
Skill alternatif
GPT Researcher
28.0K Star
npx skills add assafelovic/gpt-researcher
Skill alternatif
DeepResearch
19.8K Star
npx skills add Alibaba-NLP/DeepResearch
Keamanan Agent v2
54/100 · Hindari pemasangan otomatis
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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-consultantRencana 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 JSON
/api/agent/resolve?task=Use%20consultant%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Teks Resolve
/api/agent/resolve?task=Use%20consultant%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Serah-terima pemasangan
/api/skills/appautomaton-consultant/install
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.
Serah-terima pemasangan
/api/skills/appautomaton-consultant/install
Format teks LLM
/api/skills/appautomaton-consultant/install?format=text
Cari alternatif
/api/skills/search?q=consultant&limit=3
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 consultantMetadata Registry
Profil yang dapat dibaca Agent untuk pemilihan skill otomatis.
API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.
Manifest
/api/registry/manifest/appautomaton-consultant
Teks LLM
/api/registry/manifest/appautomaton-consultant?format=text
Alias pemasangan
/api/registry/install/appautomaton-consultant
Rekomendasikan
/api/registry/recommend?task=Use%20consultant%20in%20an%20agent%20workflow&limit=3
Kecocokan Agent
Agent riset
Tag use case
Platform
Claude Code
Laporan audit
Perlu ditinjau · 74/100
Tinjauan yang dapat dibaca mesin tentang kesiapan pemasangan, metadata keamanan, pemeliharaan, dan risiko adopsi.
Panel keputusan Agent
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
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
- 1Pasang di Agent sandbox dan jalankan satu tugas Agent riset dari awal hingga akhir.
- 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.
Profil kepercayaan
Hanya sandbox
Kandidat berguna dengan sinyal kepercayaan yang kurang atau bercampur. Gunakan di ruang kerja terisolasi hingga loop hasil membuktikan kecocokan tugas.
Adopsi GitHub
Periksa54 star GitHub
Aktivitas star/fork
Periksa54 star dan 4 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus2 hari sejak push
Kejelasan lisensi
PeriksaTidak 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.
Kecocokan alur kerja
Gunakan skill ini pada skenario berikut
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.
Kecocokan alur kerja
Tambahkan ke alur kerja lengkap
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.
Daftar alternatif
Bandingkan sebelum memasang
Similar skills that may fit this task.
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
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
recent repository activity
Audit
Tinjauan pemasangan
Tinjauan pemasangan dan adopsi
- Keamanan
- 75/100
- Pemeliharaan
- 100/100
- Pasang
- 92/100
Bukti tervalidasi Agent
Bukti tervalidasi Agent
Laporan hasil setelah resolve, tinjau, pasang, dan satu eksekusi terbatas.
- 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
Draf berbasis skenario untuk consultant, siap untuk posting manual di X.
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
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
Sumber listing
Diindeks Registry
Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.
- Kreator
- appautomaton
- Diindeks oleh
- Indeks komunitas OpenAgentSkill
Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.
Klaim skill iniKlaim 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.
[](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)Penulis
appautomaton
@appautomaton
Tag
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
- 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
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53.5K StarAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
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