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

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:

cd <workspace-root>

python3 skills/orchestrator/engine/orchestrator.py \
    --prompt-file /tmp/research-prompt.md \
    --condensed-prompt "<condensed version from Step 4>" \
    --mode [DEEP|REGULAR] \
    --task-name "<product-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

## 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.

1. 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)

1. Present files and ranked scores (if comparator ran) to the user
2. 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}

1. Update `prompt-template.md` if prompt quality issues were observed.
2. 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

<!-- Append new entries at the top of this section after each run -->

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)