Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nickstellarstreamai/ai-opportunity-finder --skill department-synthesizergit clone --depth 1 https://github.com/nickstellarstreamai/ai-opportunity-finderWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nickstellarstreamai/ai-opportunity-finder/department-synthesizer)<a href="https://agentmods.dev/skills/nickstellarstreamai/ai-opportunity-finder/department-synthesizer"><img src="https://agentmods.dev/badge/skills/nickstellarstreamai/ai-opportunity-finder/department-synthesizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nickstellarstreamai/ai-opportunity-finder/department-synthesizer"><img src="https://agentmods.dev/badge/skills/nickstellarstreamai/ai-opportunity-finder/department-synthesizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.01881 |
| Opus 5 | $0.00029 | $0.00941 |
| Sonnet 5 | $0.00012 | $0.00376 |
| Haiku 4.5 | $0.00006 | $0.00188 |
Grade A, and why
department-synthesizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Department Synthesizer
Takes multiple interview findings documents (from /insight-analyzer) for a single department and synthesizes them into one consolidated view. This is where individual data points become organizational patterns — where "one person complains about scheduling" becomes "scheduling costs this department 800 hours/year."
Why This Matters
Individual interviews are interesting. Synthesis is where the money is.
When 3 people in the same department independently describe the same frustration, that's no longer an opinion — it's a validated organizational problem. This skill does that triangulation for you.
What I Need From You
- Department name
- 2 or more Interview Findings documents (output from
/insight-analyzer)- Provide as file paths or paste the content directly
- Survey data (optional — if you ran a pre-interview survey)
- Any additional context about the department (headcount, budget, strategic importance)
What You'll Get
A Department Synthesis Document — the definitive view of one department's AI opportunities.
Processing Logic
When I receive the findings, I:
- Inventory all pain points across all interviews
- Triangulate — Flag any pain point mentioned by 2+ people as validated
- Aggregate quantification — Sum hours across interviewees, calculate department-wide impact
- Identify root causes — Connect surface-level complaints to underlying systemic issues
- Categorize opportunities — Quick Wins (2-8 weeks, low complexity) vs Strategic (2-6 months, higher complexity)
- Score each opportunity on Impact and Feasibility
Output Format
# Department Synthesis: [Department Name]
**Company:** [Company Name]
**Date:** [Date]
**Interviews Analyzed:** [Number]
**Interviewees:** [Names and roles]
---
## Main Insight
[ONE powerful sentence that captures the core finding for this department.]
Format: "[Department] spends [X hours/year] on [activity] that [impact statement]."
---
## Core Problem
**What's fundamentally broken:**
[2-3 sentence description of the systemic issue — not just symptoms, but the underlying problem]
**Validated by:**
| Source | Evidence |
|--------|----------|
| [Name, Role] | "[Quote or finding]" |
| [Name, Role] | "[Quote or finding]" |
| [Name, Role] | "[Quote or finding]" |
**Confidence Level:** [High (3+ sources) / Medium (2 sources) / Low (1 source + inference)]
---
## Why It's Happening
**Root Cause 1:** [Description]
- Evidence: [What points to this cause]
**Root Cause 2:** [Description]
- Evidence: [What points to this cause]
**Root Cause 3:** [Description]
- Evidence: [What points to this cause]
---
## Quantified Impact
### Time Burden
| Process/Activity | Source | Hours/Week | Hours/Year | People |
|-----------------|--------|------------|------------|--------|
| [Activity 1] | [Who reported it] | [X] | [X × 50] | [N] |
| [Activity 2] | [Who reported it] | [Y] | [Y × 50] | [N] |
| **Total** | | **[Sum]** | **[Sum]** | |
### Cost Estimate
| Category | Annual Hours | Cost at $50/hr | Cost at $75/hr |
|----------|-------------|----------------|----------------|
| Manual processes | [X] | $[Amount] | $[Amount] |
| Rework/errors | [Y] | $[Amount] | $[Amount] |
| Coordination overhead | [Z] | $[Amount] | $[Amount] |
| **Total** | **[Sum]** | **$[Sum]** | **$[Sum]** |
*Note: $50/hr is a conservative estimate for staff time. Adjust based on your actual loaded labor costs.*
### Quality & Risk Impact
- [Error rate or quality issue mentioned]
- [Risk identified (e.g., single point of failure, knowledge loss)]
- [Downstream impact on other departments]
---
## Opportunities
### Quick Wins (2-8 weeks, Low Complexity)
**1. [Opportunity Name]**
| Dimension | Detail |
|-----------|--------|
| **Description** | [What to build or change] |
| **Impact** | [Hours saved/year, $ value] |
| **Complexity** | Low |
| **Dependencies** | [What needs to be true] |
| **Validated by** | [Which interviewees support this] |
| **Why it's a quick win** | [Low technical complexity, data available, champion exists] |
**2. [Opportunity Name]**
...
### Strategic Initiatives (2-6 months, Medium-High Complexity)
**3. [Opportunity Name]**
| Dimension | Detail |
|-----------|--------|
| **Description** | [What to build or change] |
| **Impact** | [Hours saved/year, $ value] |
| **Complexity** | Medium / High |
| **Dependencies** | [What needs to be true — integrations, data, change management] |
| **Validated by** | [Which interviewees support this] |
| **Why it's strategic** | [Requires more investment but delivers transformational value] |
---
## Cross-Interview Patterns Detected
| Pattern | # of Sources | Confidence | Evidence |
|---------|-------------|------------|----------|
| [Pattern name] | [N] | [High/Med/Low] | [Brief evidence summary] |
| [Pattern name] | [N] | [High/Med/Low] | [Brief evidence summary] |
**Patterns appearing in 3+ interviews are high confidence and should be treated as organizational facts, not opinions.**
---
## Implementation Considerations
**Prerequisites:**
- [Data access needed]
- [System integrations required]
- [Stakeholder buy-in needed]
**Risks:**
- [Risk 1] — Mitigation: [How to address]
- [Risk 2] — Mitigation: [How to address]
**Success Factors:**
- [What needs to be true for this to work]
- [Who needs to champion this internally]
**Change Management Notes:**
- [How receptive were interviewees to change?]
- [Any resistance signals to watch for?]
---
## Department Summary Card
| Metric | Value |
|--------|-------|
| Total addressable hours/year | [X] |
| Estimated annual cost | $[Y] |
| Quick win opportunities | [N] |
| Strategic opportunities | [N] |
| Confidence level | [High/Medium/Low] |
| Top priority | [Opportunity name] |
---
## Next Steps
1. Run `/department-synthesizer` for other departments
2. Once all departments are synthesized, run `/priority-ranker` to compare across the organization
3. Use the Main Insight and Key Quotes in your `/executive-briefing`
---
Built with the AI Opportunity Finder by Morningside AI
Want expert help? → https://morningside.ai
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 230 lines · 58 tokens per session scan A 4bd11bf10340
department-synthesizer is a skill published in the GitHub repository nickstellarstreamai/ai-opportunity-finder (11 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 1,881 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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