# Solution Researcher > Orchestrates a simultaneous 7-AI competitive intelligence research round on any software product. Builds a capability-analysis prompt from a template, invokes the orchestrator engine, then invokes the consolidator skill to produce a Consolidated Intelligence Report (CIR). USE THIS SKILL whenever the user asks to research a software product, run competitive intelligence, benchmark a tool, or find out what a platform can do. Trigger when the user provides a product URL, names a specific software product and asks to research/analyze/evaluate it, or says "competitive intelligence", "capabilities report", "benchmark this tool", or "what can X do?". Supports any product domain (DevOps, PM tools, security, etc.) via pluggable domain knowledge files. If a domain file exists, evaluation criteria are appended to the prompt and used during consolidation. DEEP vs. REGULAR mode: If the user includes the keyword 'deep' in their request, use Deep Research modes on all platforms. - Skill: `alo-exp/solution-researcher` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add alo-exp/solution-researcher` - Raw SKILL.md: https://api.skillmd.com/api/skills/alo-exp/solution-researcher/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: alo-exp (https://skillmd.com/u/alo-exp) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/alo-exp/solution-researcher --- # Solution Research Skill This skill orchestrates a full solution research workflow: build prompt, run 7 AIs, consolidate results, enrich domain knowledge, and optionally update the comparison matrix. Follow the phases below in order. --- ## Phase 0 — Extract Inputs Extract from the user's request: - **Primary URL** — the product's main website (required) - **Scope context** — one-line description of what the product does (required) - **Additional URLs** *(optional)* — docs, API reference, changelog, etc. - **GitHub / repository URL** *(optional)* — for open-source products - **Domain** *(optional)* — e.g., `devops-platforms`, `project-management`, `security-tools` - **Research depth** — if the user includes **`deep`**, set `MODE = DEEP`. Otherwise `MODE = REGULAR`. If Primary URL or Scope context is missing, ask the user before proceeding. If the user doesn't specify a domain, try to infer from the scope context. Check if `domains/{domain}.md` exists. --- ## Phase 1 — Build Prompt ### Step 1: Load prompt template Read `skills/solution-researcher/prompt-template.md`. The template contains a fenced code block with the raw prompt. Extract the prompt text from inside the code fence (between ``` markers). ### Step 2: Fill placeholders Replace these placeholders in the extracted prompt: - `[PRIMARY_URL]` → the product's URL - `[SCOPE_CONTEXT]` → one-line product description - `[ADDITIONAL_URLS]` → comma-separated extra URLs (or remove the line if none) - `[GITHUB_URL]` → repository URL (or `N/A` if not applicable) ### Step 3: Append domain knowledge (if available) If `domains/{domain}.md` exists: 1. Read the file 2. Append its content to the prompt under a new section: ``` DOMAIN-SPECIFIC EVALUATION CRITERIA The following categories, terminology, and evaluation criteria are specific to the {domain} domain. Use them to guide your analysis: [content of domains/{domain}.md] ``` ### Step 4: Generate condensed prompt Create a condensed version (≤900 chars) for constrained platforms: ``` Analyze [PRIMARY_URL] ([SCOPE_CONTEXT]). Produce: 1) Detailed Capability Report with Executive Summary, capability groups (5-12), sub-capabilities with descriptions, plan/tier availability, and constraints. 2) Assumptions, Gaps & Ambiguities table. 3) Marketing Claims vs Demonstrated Capabilities comparison. 4) Product-agnostic Capabilities Checklist for competitive benchmarking. Be precise. Use Markdown formatting. ``` ### Step 5: Write prompt to temp file ```bash cat > /tmp/research-prompt.md << 'PROMPT_EOF' [FILLED PROMPT TEXT] PROMPT_EOF ``` --- ## Phase 2 — Invoke Orchestrator Engine Generate a task name from the product name (e.g., `harness-oss`, `port-io`). Run the engine: ```bash cd python3 skills/orchestrator/engine/orchestrator.py \ --prompt-file /tmp/research-prompt.md \ --condensed-prompt "" \ --mode [DEEP|REGULAR] \ --task-name "" ``` Set Bash timeout to 60 min for DEEP, 20 min for REGULAR. The engine writes raw responses to `reports/{task-name}/` and auto-collates them into: `reports/{task-name}/{task-name} - Raw AI Responses.md` --- ## Phase 3 — Read Results Read `reports/{task-name}/status.json` to verify all platforms have terminal statuses. --- ## Phase 4 — Invoke Consolidator Invoke the consolidator skill with: - **Raw responses archive:** `reports/{task-name}/{task-name} - Raw AI Responses.md` - **Consolidation guide:** `skills/solution-researcher/consolidation-guide.md` - **Domain knowledge:** `domains/{domain}.md` (if available) The consolidator will produce a Consolidated Intelligence Report (CIR). --- ## Phase 5 — Domain Knowledge Enrichment After consolidation, review the CIR for new patterns and propose append-only additions to `domains/{domain}.md`. These additions should include **general domain knowledge** — not just product-specific data — so that future landscape-researcher runs can also benefit. ### What to propose: - **New capability categories** not in the domain file - **New terminology** specific to this domain (including terms introduced by this vendor) - **New inference patterns** — rules for deriving capability X from the presence of Y - **New feature name equivalences** — when the CIR uses a different name for a known feature - **Archetype insights** — if this product represents a new or refined archetype - **Trend signals** — if the product embodies a trend worth tracking at the domain level ```markdown ## Additions from {Product Name} research ({YYYY-MM-DD}) — solution-researcher - New category: [name] - New term: [term] — [definition] - Inference pattern: [pattern description] - Trend signal: [trend] — [observation] ``` If no domain file exists and the user specified a domain, propose creating one with the initial categories, terminology, and criteria discovered during this research. **Present proposed changes to the user for approval before writing.** --- ## Phase 5b — Auto-Invoke Comparator After consolidation and domain enrichment, automatically check if a comparison matrix exists for this domain: 1. **Check for existing matrix:** Look in `reports/` for an existing `.xlsx` matrix file for this domain. 2. **If a matrix exists:** - Invoke the comparator skill with: - **CIR path:** the just-produced Consolidated Intelligence Report - **Matrix path:** the most recent `.xlsx` matrix for this domain - **Domain:** the current domain name - The comparator uses `skills/comparator/matrix_ops.py` to add the new platform column and reorder by score. 3. **If no matrix exists:** Skip this phase. The user can manually invoke the comparator later. --- ## Phase 6 — Present Deliverables 1. Confirm all files are saved in the `reports/` folder: - `{product-task-name}/{task-name} - Raw AI Responses.md` - `{Product Name} - Consolidated Intelligence Report.md` - Updated comparison matrix (if Phase 5b ran) 2. Present files and ranked scores (if comparator ran) to the user 3. If domain knowledge changes were proposed, present them for approval --- ## Phase 7 — Self-Improve After a successful run, update this skill's own files based on what was learned. 1. **Append a run log entry** to the `## Run Log` section below: ``` ### {YYYY-MM-DD} — {Product Name} ({mode}) - Platforms responded: {list} - CIR quality: {brief assessment} - Prompt observations: {what worked / what could improve} - Consolidation observations: {structure issues / missed capabilities} - Comparator observations: {accuracy, orphan rate, archetype fit} - Changes made: {list any updates to prompt-template.md, consolidation-guide.md} ``` 2. **Update `prompt-template.md`** if prompt quality issues were observed. 3. **Update `consolidation-guide.md`** if CIR structure issues were found. **Scope boundary:** Only update files inside `skills/solution-researcher/`. Never modify the engine, other skills, or domain files (domain enrichment is done in Phase 5 with user approval). --- ## Run Log ### 2026-03-18 — Northflank (E2E-05 / IT-SC-03/05/07) — DEEP mode - Platforms responded: 4/6 — Copilot, Grok, Claude.ai (partial), DeepSeek (DOM chrome); ChatGPT + Perplexity excluded (quota exhausted) - CIR quality: High — 7 capability groups documented, 50+ item checklist, marketing claims assessment, assumptions/gaps table - Comparator result: 6/7 ticks applied (Canary Deployments: false; all others true); 0 orphans; 0 new rows - Domain enrichment: 6 timestamped additions to `domains/devops-platforms.md` — GPU category, microVM term, Platform SDK term, VectorDB inference pattern, AI-native PaaS trend, PaaS/IDP GPU variant archetype note - Prompt observations: Solution-researcher prompt-template performed well — all 4 responding AIs covered capability groups, deployment types, and pricing signals; Copilot and Grok produced the most structured output - Consolidation observations: consolidation-guide.md 5-section structure applied cleanly; DeepSeek DOM chrome was identifiable and filtered without degrading output quality - Changes made: none to prompt-template.md or consolidation-guide.md (both performed as expected)