plugins/d-nd-core/skills/capture-insight/SKILL.md
Quickly capture operator insights without breaking current workflow. Auto-routes to appropriate team/node. Use when the operator shares observations, ideas, or strategic thoughts mid-session.
npx skillsauth add grazianoguiducci/d-nd-seed capture-insightInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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When the operator shares an insight, observation, or strategic thought during a work session:
memory/brand_voice.md or relevant memory filememory/backlog.md or send to the relevant node via messagingbrand_voice.md + backlog if actionablehub_vision.md + backlogevolution.md or CLAUDE.mdThe insight will be processed properly in its own dedicated session, not mid-flow.
$ARGUMENTS
testing
Closure reflection protocol. After a significant work block concludes (feature shipped, session ending, major commit landed, cross-node coordination resolved), runs a 10-question interview that extracts meaning, impact, and next questions — then emits multiple audience-specific artifacts (changelog, external editorial, AI integration docs, memory crystal, backlog seed). Turns implicit maturation into explicit narrative. Use at the end of meaningful work, not after trivial edits.
testing
The neutral form of the D-ND method. Meta-skill that recognizes context and orients toward the right specialization (cec, autologica, cascade, assertion-verifier, etc.). Activate at the start of a non-trivial work block or when input matches trigger words ('where are we', 'what here', 'orchestrate', 'connect', 'sieve this').
development
Five mechanical gates for any content publish pipeline with CMS + rendering layers. Prevents false security: 'API returned 200' does not mean 'visitor sees clean content'. Use when writing content to a multi-layer serving system (CMS API, static files, prerendered HTML, cached copies).
testing
Multi-node consultation protocol for high-leverage decisions. Dispatches the same question to N independent LLM/agent nodes in isolation, then synthesizes their responses into a summa that exposes convergence (high-confidence claims), dissensus (real uncertainty zones), and emergent points (insights no single node produced). Reduces single-node training bias. Supports recursive escalation for stable-state convergence. Use for decisions that propagate via A14 cascade — seed updates, crystallizations, advisory→mechanical promotions, high-visibility copy, lab result interpretation.