Insight Angles

/insight-angles

A lens-based, multi-pass discovery engine. Where /insight-explore asks "what are the ways to build this?" and /insight-adversarial attacks for flaws via personas, insight-angles asks "what frames, connections, assumptions, and second-order effects are not yet visible?" — and finds them by pointing the right lenses at the subject. It can run each lens as a cold sub-agent (fresh context, no anchoring) — independence is what makes discovery work, the same reason /cold-review outperforms inline /verify at finding things — and picks cold vs inline per subject, asking only when it isn't sure. Read-only and advisory — it surfaces, never decides or changes anything.

Structured execution spec: protocol.yon. Read it for the canonical rules and step sequence; this file is explanation. The two must stay in sync — if you edit one, update the other and refresh the @STAMP date.

The lens roster (seed, not ceiling)

17 families, grouped by what they perturb. Core = candidate for most subjects; Specialist = domain-triggered. Always also derive dynamic, subject-specific lenses (see below) — the roster is a seed.

8 core (Aesthetic/Elegance included — it's good practice across UX, DX, design, ergonomics) + 9 specialist. A run still fires only 4–6 — the roster's job is to make the picking rich.

Dynamic-lens derivation (the roster is a seed)

For every run, also derive subject-specific lenses from three named sources, then validate:

1. Domain-native — what would an expert in the subject's home discipline always ask here?
2. Foreign-transplant — what does a deliberately distant discipline ask that nobody here is?
3. Live-trend — from Classify's current-trends read, what recent shift / live debate reframes the space?
4. Validate — keep a candidate only if it generates a question the 17 families don't. Otherwise drop it as redundant.

Typed connections

Each carries type · the link · why it matters · what it enables: analogical · causal · structural · tension (productive paradox) · emergent (A+B→C) · dependency · resource · temporal.

Scoring — novelty × relevance

Every angle/connection is tagged: novelty (obvious / fresh / surprising, relative to current trends) · relevance (decision-relevant & actionable?) · so-what (what changes if true). Rank by novelty × relevance; the strongest unseen angle is the highest combined; flag high-novelty / low-relevance as "interesting but not actionable" rather than dropping it.

Steps

1. Frame — state the subject and the dominant framing to escape. Light context only.
2. Classify — domain · subject-type · maturity · current trends (calibrates novelty, drives selection).
3. Pick lenses — select roster families for the subject-type, plus derive dynamic lenses (above). Log picked + skipped (+ why).
4. Layered passes — run the picked lenses (see venue below):
   • Layer 1 — each lens → angles + typed connections, each scored.
   • Layer 2+ — add 2–4 lenses, informed by the prior layer (re-read prior angles through new lenses; link findings; revise scores). Deeper than default depth is HIL-gated.
5. Connect — consolidate typed connections across layers, especially cross-layer links.
6. Score & Synthesize — finalize scoring; emit the angle map + synthesis (strongest angle · key connection · most-fragile assumption). If nothing non-obvious surfaced, say so — never manufacture insight.

Venue & depth — context-adaptive

Cold fan-out is the discovery powerhouse (independence = the cold-review effect), but not every subject needs it. Select the venue case-by-case from context / corpus / task; proceed when certainty is near-max, ask the handler only when it isn't. Explicit overrides: "use agents" / "cold" forces fan-out; "inline" / "quick" forces inline.

• Cold (fan-out) when: high ambiguity, high stakes, novel subject, many lenses (anchoring risk), or independence clearly matters.
• Inline when: well-bounded, low stakes, few lenses, cost-constrained — or forced under a *-RESOLVED marker.

The three modes (selected adaptively, not by a fixed default):

• standard — cold fan-out: one fresh leaf sub-agent per picked lens. No shared context = no anchoring (the cold-review effect). Orchestrator collects + synthesizes. State the fan-out size up front; HIL-gate if it exceeds ~6 agents.
• quick — inline single pass (cheap). Forced when orchestrated under a *-RESOLVED marker (a lens-agent is a leaf — runs one lens, returns findings, spawns nothing — which keeps the depth guard intact).
• deep — cold fan-out across 2–3 layers; the orchestrator threads each completed layer's findings into the next layer's briefs (compounding at the orchestrator, independence in the agents). HIL-gated (cost).

Cold lens-agents are leaf workers: each gets a primed brief (subject + its one lens [+ prior-layer findings in deep mode]), runs cold, returns its angles/connections, and orchestrates nothing further.

Runtime: detect capabilities, not product names. When the active runtime exposes isolated workers (for example, Claude's Agent or Codex collaboration agents when available), cold fan-out may be used. Without isolated workers, all modes fall back to inline — the adaptive selector degrades gracefully since inline is always available.

The angle map (output)

Selected lenses (+ dynamic ones + skipped & why) → angles grouped by lens, each scored → typed connections (each with why-it-matters) → hidden assumptions (most-fragile flagged) → candidate-expansions → second-order effects → synthesis → layer trail.

Rules

• MUST surface ≥1 genuine connection or unseen angle, or explicitly state none found — never an empty map, never manufactured insight.
• MUST derive dynamic subject-specific lenses (3 sources + validate), not only pick from the roster.
• MUST keep each layer informed by the prior layer's findings (layers compound; not independent re-runs).
• MUST select venue (cold vs inline) per subject from context/corpus/task; proceed when certainty is near-max, ask the handler only when it isn't; honor explicit overrides ("use agents" → cold, "inline" → inline).
• Cold lens-agents MUST be leaves — run one lens, return findings, spawn nothing.
• MUST stay in perspective/connection space: a frame is not a flaw (/insight-adversarial) and not an option (/insight-explore).
• MUST make each connection assessable (type + why), and judge novelty relative to current trends.
• MUST log lens selection (picked + skipped + why).
• MUST NOT exceed default depth (extra layers, or fan-out > ~6 agents) without explicit handler consent — state the cost and offer.
• MUST NOT make changes, write code, or execute — ideation only.
• Under a *-RESOLVED marker: run quick (inline) — never spawn further sub-skills.
• SHOULD prefer the non-obvious; flag high-novelty / low-relevance rather than dropping it.

Self-improvement → roster discovery

This skill's SIP is specialized: usage grows the roster. When a dynamic lens this run produced a high novelty × relevance angle the roster lacked, SIP proposes a diff adding it to the lens roster + selection map (gated via /ask-gate, never auto-applied). When a roster lens consistently yields nothing for a subject-type, SIP proposes pruning/remapping it. The 17 families are the seed; real subjects reveal the rest.

Boundary

• /insight-explore — generates solution options. insight-angles surfaces frames & connections; its candidate-expansions are seeds, not finished options.
• /insight-adversarial — attacks from personas for flaws. insight-angles widens via lenses (same multi-pass shape; personas attack, lenses reveal).
• /insight-critique — reviews a concrete output. insight-angles explores a subject's representation.
• /insight-cross-examine — the deliberation engine that orchestrates insight-angles (in quick mode), then assesses and recommends. Use cross-examine for a decision; insight-angles to just see more.
• /cold-review — outside review of work artifacts against objectives. insight-angles borrows its cold-agent independence, but for angle discovery on a subject, not artifact review.

Human output. This skill's handler-facing output obeys the human-output contract (human-output/SKILL.md).

Next skills. On completion, run the Next Skills protocol (next-skills/SKILL.md): surface the next-skills recommendations from front-matter for the caller to pick. Offer only — never auto-invoke.

Self-improvement. On completion, run the Self-Improvement Protocol (self-improve/SKILL.md): if this run surfaced a concrete, blocking-or-recurring weakness in this skill — including a dynamic lens worth promoting into the roster — propose a specific fix for the handler to approve. Conservative — silent otherwise. Never auto-apply.