--- name: expert-panel-fe56ba3adc38 description: >- Score, evaluate, and iteratively improve any content or strategy using an auto-assembled panel of domain experts. Handles copy, sequences, landing pages, strategy docs, titles, charts, recruiting evaluations, or anything else that needs a quality gate. Recursively iterates until all scores hit 90+ (max 3 rounds). Use when asked to: "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "which version is better", "expert score", "evaluate this copy/strategy/page", or when another skill needs a quality gate on its output. Also triggers on: "score this landing page", "expert panel these email variants", "rate this headline", "panel these charts". --- ## Preamble (runs on skill start) ```bash # Version check (silent if up to date) python3 telemetry/version_check.py 2>/dev/null || true # Telemetry opt-in (first run only, then remembers your choice) python3 telemetry/telemetry_init.py 2>/dev/null || true ``` > **Privacy:** This skill logs usage locally to `~/.ai-marketing-skills/analytics/`. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See `telemetry/README.md`. --- # Expert Panel General-purpose scoring and iterative improvement engine. Auto-assembles the right experts for whatever is being evaluated, scores it, and loops until 90+. --- ## Step 1: Intake — Understand What's Being Scored Collect or infer from context: 1. **Content/artifact** — The thing(s) to score (paste, file path, or URL) 2. **Content type** — Copy, sequence, landing page, strategy, title, chart, candidate eval, etc. 3. **Offer context** — What's being sold/promoted? To whom? What domain/industry? 4. **Variants** — Are there multiple versions to compare? (A/B/C) 5. **Source skill** — Is this output from another skill? (e.g., cold-outbound-optimizer) If yes, note the source for feedback-to-source routing in Step 6. If context is obvious from the conversation, don't ask — just proceed. --- ## Step 2: Auto-Assemble the Expert Panel Build a panel of **7–10 experts** tailored to the content type and domain. ### Assembly rules 1. **Start with content-type experts.** Read `experts/` directory for pre-built panels matching the content type. If an exact match exists (e.g., `experts/linkedin.md` for a LinkedIn post), use it as the base. 2. **Add domain/offer experts.** Based on the offer context, add 1–3 experts who understand the specific industry or domain. Examples: - Scoring bakery marketing → add Food & Beverage Marketing Expert - Scoring SaaS landing page → add SaaS Conversion Expert - Scoring recruiting outreach → add Agency Recruiter + Talent Market Expert - Scoring medical device copy → add Healthcare Compliance Expert 3. **Always include these two:** - **AI Writing Detector** — See `experts/humanizer.md`. Weight: 1.5x. Non-negotiable. - **Brand Voice Match** — Checks alignment with the configured brand voice and known rejection patterns from `references/patterns.md` (if present). 4. **Check learned patterns.** If `references/patterns.md` exists, read it. If any patterns apply to this content type, brief the panel on them. Dock points for known-bad patterns. 5. **Cap at 10 experts.** If you have more than 10, merge overlapping roles. ### Panel output format List each expert with: Name, lens/focus, what they check. --- ## Step 3: Select Scoring Rubric Choose the appropriate rubric from `scoring-rubrics/`: | Content type | Rubric file | |---|---| | Blog, social, email, newsletter, scripts | `scoring-rubrics/content-quality.md` | | Strategy, recommendations, analysis | `scoring-rubrics/strategic-quality.md` | | Landing pages, ads, CTAs | `scoring-rubrics/conversion-quality.md` | | Charts, data viz, infographics | `scoring-rubrics/visual-quality.md` | | Candidate evaluations | `scoring-rubrics/evaluation-quality.md` | | Other | Synthesize a rubric from the two closest matches | Read the selected rubric file for detailed criteria and point allocation. --- ## Step 4: Score — Recursive Loop Until 90+ **Target: 90/100 across all experts. Non-negotiable. Max 3 rounds.** ### Each round produces: ``` ## Round [N] — Score: [AVG]/100 | Expert | Score | Key Feedback | |--------|-------|--------------| | [Name] | [0-100] | [One-line rationale] | | ... | ... | ... | **Aggregate:** [weighted average — humanizer at 1.5x] **Top 3 weaknesses:** [ranked] **Changes made:** [specific edits addressing each weakness] ``` Then the revised content/artifact. ### Rules - Scores must be brutally honest. No padding to 90. - Humanizer score weighted 1.5x in the aggregate. - If aggregate < 90: identify top 3 weaknesses → revise → next round. - If aggregate ≥ 90: finalize and proceed to output. - After 3 rounds, if still < 90: return best version with honest score + note on what's holding it back. - Show ALL rounds in output — the iteration trail is part of the value. ### Variant comparison mode When scoring multiple variants (A/B/C): - Score each variant independently through the full panel. - After scoring, rank variants by aggregate score. - If top variant is < 90, iterate on the best one (don't iterate all of them). --- ## Step 5: Output Format ### Winner + Score (always at top) ``` ## 🏆 Result: [SCORE]/100 — [PASS ✅ | NEEDS WORK ⚠️] [Final content/artifact here] **Iterations:** [N] rounds **Panel:** [Expert names, comma-separated] ``` If variants: show winner first, then runner-up scores. ``` ## 🏆 Winner: Variant [X] — [SCORE]/100 [Winning content] ### Runner-up scores - Variant A: 87/100 - Variant B: 82/100 - Variant C: 91/100 ← Winner ``` ### Feedback History (below the result) Show full scoring rounds. ``` ---
📊 Scoring History (N rounds) [All round tables from Step 4]
``` --- ## Step 6: Feedback-to-Source (When Scoring Another Skill's Output) When the scored content came from another skill, generate a **Source Improvement Brief**: ``` ## 🔁 Feedback for [Source Skill] ### What scored low - [Pattern]: [Specific example from this content] ### Suggested skill improvements - [Concrete change to the source skill's process/rubric/prompt] ### Patterns to add to source skill - [Any recurring weakness that should become a rule] ``` This brief can be used to update the source skill's SKILL.md or rubrics. --- ## Step 7: Memory — Learn from Approvals and Rejections After the user approves or rejects panel output: ### On approval (score ≥ 90, user accepts) Note what worked. No action needed unless a new positive pattern emerges. ### On rejection (user overrides the panel or rejects 90+ content) 1. Ask why (or infer from context). 2. Add a new pattern to `references/patterns.md` using this format: ```markdown ## [Pattern Name] - **Type:** rejection | preference | override - **Content types:** [which types this applies to] - **Rule:** [What to always/never do] - **Example:** [The specific instance that triggered this] - **Date:** [YYYY-MM-DD] - **Point dock:** [-N points when detected] ``` 3. Confirm: "Added pattern: [one-line summary]. Panel will dock [N] points for this going forward." ### Pattern enforcement Every scoring round, check `references/patterns.md` against the content. Apply point docks before expert scoring begins. This means known-bad patterns are penalized even if individual experts miss them. --- ## Reference Files | File | Purpose | When to read | |---|---|---| | `experts/humanizer.md` | AI writing detection rubric (24 patterns) | Every scoring run | | `experts/[domain].md` | Pre-built expert panels for common domains | When domain matches | | `scoring-rubrics/content-quality.md` | Content scoring rubric | Content scoring | | `scoring-rubrics/strategic-quality.md` | Strategy scoring rubric | Strategy scoring | | `scoring-rubrics/conversion-quality.md` | Landing page/ad/CTA rubric | Conversion scoring | | `scoring-rubrics/visual-quality.md` | Chart/data viz/infographic rubric | Visual scoring | | `scoring-rubrics/evaluation-quality.md` | Candidate/assessment rubric | Eval scoring | | `references/patterns.md` | Learned rejection patterns | Every scoring run | | `references/expert-assembly.md` | Domain-expert examples for auto-assembly | When building unfamiliar panels | --- Source attribution: ericosiu Original project: https://github.com/ericosiu/ai-marketing-skills/blob/d2237ec431608c9ab0551d03a69479b000603f11/content-ops/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Single Grain Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ericosiu Original project: https://github.com/ericosiu/ai-marketing-skills/blob/d2237ec431608c9ab0551d03a69479b000603f11/content-ops/SKILL.md Attribution basis: Source repository record Fields of work: Marketing Roles: Content Marketer, SEO Specialist, Growth Marketer, Influencer Marketer, Marketing Analyst Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: b19fbd26b2bac80ab188e0a7ee8bd375b899d26efe17bd1507bfa74f7f7ae77e