sh-personal-development-panel

name	sh-personal-development-panel
description	Multi-expert personal-development review with scoring gate — learnability, adoption, human-centeredness, and capability impact for talent/learning/AI-augmentation designs

/sh:personal-development-panel — Expert Personal-Development Review Panel

Usage

/sh:personal-development-panel [specification_content|@file] [--mode discussion|critique|socratic] [--focus learnability|adoption|human-centeredness|capability] [--experts "name1,name2"] [--iterations N] [--verbose]

Verbosity

• Silent (default): No expert deliberations. Output only: score table, FIPD-classified findings list, and auto-fix diff. Saves ~60-80% output tokens.
• Verbose (--verbose): Full expert deliberations, cross-expert dialogue, reasoning traces, and detailed per-expert analysis before scores and findings.

Silent mode still performs full internal analysis — quality is preserved, only the output is compressed.

Behavioral Flow

0. Load Protocol: Read /Users/jcords-macmini/projects/20_agentflow/experts/PANEL_PROTOCOL.md and apply its Grounding (non-code branch — every finding quotes the source section it rests on) and Refute Stage sections. Findings that misquote or cannot cite the source MUST NOT be reported.
1. Load Panel Config: Read /Users/jcords-macmini/projects/20_agentflow/experts/panels/personal-development-panel.yaml for panel definition, focus areas, auto-select rules, and scoring config (absolute path — relative paths fail when CWD is outside agentflow)
2. Load Experts: Read expert files from /Users/jcords-macmini/projects/20_agentflow/experts/individuals/ for each selected expert — these files contain the expert's domain, methodology, and critique focus
3. Auto-Select Experts: Scan the specification content against panel YAML auto-select keywords — add matching experts up to max-experts: 8 cap
4. Pre-Scoring Checks (deterministic — do NOT delegate to LLM personas):
   • Table reconciliation: If the spec contains 2+ tables with numeric values (competence counts, capability-spine size, role-slice membership, "≥ N" thresholds), extract every numeric claim and verify cross-section arithmetic before personas opine. Enumerate the items the count refers to in the other sections; assert the math reconciles. Flag any mismatch as a CRITICAL finding up front. Rationale: LLMs reliably hallucinate arithmetic consistency over freeform tables — personas reading personas will not catch it. This step is mechanical, executed by the panel runner, not by an expert voice.
5. Analyze: Parse specification content, identify components, gaps, and quality issues
6. Assemble Panel: Select experts based on --focus area or use default-experts from panel YAML. --experts override replaces defaults entirely
7. Conduct Review: Run analysis in the selected mode using each expert's distinct methodology
8. Score: Rate the design across 4 dimensions (0-10 each), compute overall score
9. Gate Check: Overall score must be >= 7.0 to pass. Below threshold = design needs rework

Expert Loading

Experts are defined as individual markdown files in /Users/jcords-macmini/projects/20_agentflow/experts/individuals/. Each file contains structured frontmatter with:

• Domain and specialization
• Methodology and frameworks
• Critique focus and typical questions

The panel YAML (/Users/jcords-macmini/projects/20_agentflow/experts/panels/personal-development-panel.yaml) defines:

• Which experts belong to which focus area
• Who leads each focus area
• Auto-select keyword rules for dynamic expert addition
• Scoring dimensions and pass threshold

Default Panel

The default roster (7 experts) reviews every design unless --focus or --experts narrows it:

Expert	Lens	Method
Dave Ulrich	HR / talent architecture	competency models, HR-from-the-outside-in, receiver-defined value
Malcolm Knowles	adult learning (andragogy)	six andragogical principles
Jeff Hiatt	change management (individual)	Prosci ADKAR
Erik Brynjolfsson	AI enablement / future of work	task-based augment-vs-automate, the Turing Trap
Amy Edmondson	psychological safety / teaming	psychological safety, intelligent failure
Ravin Jesuthasan	skills-based organization	work deconstruction (jobs→tasks→skills→capabilities)
Josh Bersin	enterprise L&D + AI	capability academies, commercialization, maturity models

Auto-select adds Stuart Russell (AI safety / control) when autonomy/agentic/alignment keywords appear, up to the max-experts: 8 cap.

Analysis Modes

Discussion Mode (--mode discussion)

Collaborative improvement through expert dialogue. Experts build upon each other's insights sequentially. Cross-expert validation and consensus building around critical improvements.

Critique Mode (--mode critique)

Systematic review with severity-classified issues (CRITICAL / MAJOR / MINOR). Each finding includes: expert attribution, specific recommendation, priority ranking, and quality impact estimate.

Socratic Mode (--mode socratic)

Learning-focused questioning to deepen understanding. Experts pose foundational questions about purpose, learners, assumptions, and alternatives. No direct answers — forces the author to think critically.

Focus Areas

• learnability: Adult-learning fit — why-now framing, learner agency, problem-centered design. Lead: Malcolm Knowles. Experts: Knowles, Ulrich
• adoption: Will real people adopt and sustain the behavior — ADKAR rungs and psychological safety. Lead: Jeff Hiatt. Experts: Hiatt, Edmondson
• human-centeredness: Trust, safety, augmentation-over-replacement. Lead: Amy Edmondson. Experts: Edmondson, Brynjolfsson
• capability: Capability spine integrity, work deconstruction, individual→org→value line of sight. Lead: Ravin Jesuthasan. Experts: Jesuthasan, Ulrich, Brynjolfsson, Bersin

Scoring Gate

4 dimensions, each scored 0-10:

Dimension	Description	Owner(s)
Learnability	Adult-learning soundness — why-now framing, learner agency, builds on experience	Knowles
Adoption-Readiness	Individual adoption through ADKAR rungs + psychological safety; barrier point named, reinforcement sustains behavior	Hiatt, Edmondson
Human-Centeredness	Human stays central — trust over surveillance, augmentation over replacement (Turing Trap avoided)	Edmondson, Brynjolfsson
Capability-Impact	Capability spine integrity + line of sight from individual competence to organizational capability to receiver-defined value	Ulrich, Jesuthasan

Pass threshold: overall score >= 7.0

Output includes per-dimension scores, overall score, critical issues, expert consensus points, and an improvement roadmap (immediate / short-term / long-term).

Output

Personal-development review document containing:

• Multi-expert analysis with distinct perspectives
• Per-dimension scores and overall quality score
• Pass/fail gate result
• Critical issues with severity and priority
• Consensus points and disagreements
• Priority-ranked improvement recommendations

AUTO-FIX, NOT SYNTHESIS-ONLY — this panel produces the analysis AND then applies fixes for every finding (high, medium, and low) automatically, per 00_Governance/CLAUDE.md §8 Panel Auto-Fix Policy. It never asks which findings to apply and never presents a menu: it fixes everything, then reports what changed. A below-gate score means fix the findings and re-run, not stop and ask.

Auto-Fix Policy

Fix ALL findings automatically — high, medium, and low severity. Do not ask which findings to fix. Do not present a menu or an a/b/c choice. Fix everything, then report what was changed. This overrides any softer "synthesis only" framing. Per 00_Governance/CLAUDE.md §8.

Output Contract (machine-readable verdict)

The quality gate (quality_gate.run_stage3_panel) consumes this panel via claude -p and reads the score from stdout, not the exit code. Therefore the final line of your output MUST be exactly one of:

PANEL-VERDICT: <overall_score>
PANEL-VERDICT: FAIL: <machine_reason>

• <overall_score> is the numeric overall score (0–10, one decimal — e.g. 8.3). The gate applies the pass threshold (default 7.0); do NOT pre-apply it — just report the score you computed.
• Use the FAIL: <machine_reason> form only when no score could be produced (structural failure) — snake_case naming the first blocker (e.g. no_content, panel_config_missing, experts_unavailable).
• Emit the line literally, on its own line, as the last meaningful output. Omitting it makes the gate fail-closed with panel_no_verdict (inconclusive).