context-intelligence-derived-metrics

name	context-intelligence-derived-metrics
description	Use when a graph query returns structurally-identical rows that hide different intent — many delegations all "none/conversation", many look-alike sessions, many identical tool calls — and the raw shape refuses to tell you WHY. Teaches how to derive an INTERPRETIVE metric by joining a second graph layer onto the blanket shape, turning undifferentiated data into behavioral signatures. Flagship pattern: the "flavour" of a sub-session = delegation transport × the skill payload loaded inside the sub-session it spawned. Complements context-intelligence-graph-query (which tells you how to READ the graph); this skill tells you how to make blanket reads MEAN something.
license	MIT
metadata	{"version":"1.0.0","changelog":["1.0.0: Initial skill. The blanket-data problem and the three moves (spot the blanket, find the discriminating join, recompute with the derived dimension). Flagship flavour metric (transport × in-subsession skill payload) with verified Cypher. Worked example: council-panel attribution collapsing self-delegation from ~42% to ~12% of delegations once the derived dimension is applied. Generalization to magnitude-based and artifact-based derived metrics."]}

Context Intelligence Derived Metrics

Prerequisite: load context-intelligence-graph-query first. That skill owns the schema, scoping levers, size discipline, and blob rules. This skill assumes you can already run scoped queries safely; it only teaches the interpretive layer on top. Verify every node/edge/property named here against the live schema (CALL db.schema.nodeTypeProperties()) — read the live schema from the server, read the meaning from this skill.

Section 1 — The blanket-data problem

Raw graph shapes are often blanket: many rows share one structural signature but carry completely different intent. A single-layer query cannot tell them apart, so the aggregate lies by omission — the mode hides sub-populations.

The canonical case: delegation transport. Across a real corpus, ~70% of all delegations were context_depth="none", context_scope="conversation". Read flat, that says "clean-slate delegation dominates." But that one bucket contained at least three different behaviors wearing the same clothes:

• Pure task-offloading — spin up a fresh worker for an independent unit of work.
• Context-sink — park token-heavy exploration in a disposable agent so the parent stays lean.
• Council lenses — a review panel fanning six persona lenses out cold.

Transport args alone (agent, context_depth, context_scope, is_self_delegation) put all three in the same cell of the table. The distinction is real and it matters — but it lives in a different layer than the one you queried.

A derived metric is what you build when the shape you can see is not the meaning you need. You attach a second, correlated signal to the blanket shape until the sub-populations separate.

Section 2 — The three moves

Every derived metric is the same three moves. Do them in order.

Move 1 — Spot the blanket. A query returns many rows with the same structural signature and you find yourself unable to say why each one happened. The tell: a dominant mode (one (depth, scope) cell, one tool name, one session label) that you suspect contains distinct intents. If you can't narrate a row from its own fields, it's blanket.

Move 2 — Find the discriminating join. Identify a second graph signal that correlates with the intent you care about and is reachable from the blanket row. It comes in three flavours:

Join type	Second signal	Separates
Categorical (payload)	what SKILL loaded in the spawned sub-session	council lens vs plain worker
Magnitude	token / iteration / tool-call volume inside the sub-session	context-sink vs light offload
Artifact	what the sub-session PRODUCED (commit, PR, file)	continuity handoff vs throwaway

The best join is causally meaningful, not merely correlated (Section 6).

Move 3 — Recompute with the derived dimension. Segment the blanket population by the new signal, then recompute the headline stat with the derived population removed. The delta is the finding. "Self-delegation is 42% of delegations" becomes "…but 82% of that is one review panel; strip it out and self-delegation is 12%." The recompute-with-removed step is non-negotiable — it's how you prove the derived dimension was load-bearing rather than decorative.

Section 3 — Flagship: the "flavour" metric

Definition. The flavour of a sub-session is (transport) × (payload):

• Transport = the Delegation that spawned it: is_self_delegation, context_depth, context_scope, agent.
• Payload = what happened inside the spawned sub-session. The single most discriminating payload signal is the SkillLoad.skill_name(s) that fired in that sub-session.

A none/conversation self-delegation is a blank envelope — its meaning comes entirely from what skill gets loaded inside it. That is exactly why a review panel can hide in plain sight: it shares a transport signature with ordinary parallel fan-out, and only the skill payload reveals it is a debate panel convened cold.

The join key

Delegation.sub_session_id equals the spawned session's Session.node_id. That is the bridge from transport to payload. Skill loads hang off the sub-session two ways (per graph-query Gotcha 6) — iteration-level and session-level — so match both:

// FLAVOUR TABLE — every delegation transport × the skill payload of its sub-session.
// Scope on the PARENT session (it carries workspace); see graph-query Section 2.
MATCH (parent:Session {workspace: $workspace})
      -[:HAS_EXECUTION]->(:OrchestratorRun)-[:HAS_PART]->(:Iteration)
      -[:HAS_TOOL_CALL]->(:ToolCall)-[:TRIGGERED]->(d:Delegation)
OPTIONAL MATCH (sub:Session {node_id: d.sub_session_id})
OPTIONAL MATCH (sub)-[:HAS_EXECUTION]->(:OrchestratorRun)-[:HAS_PART]->(:Iteration)
               -[:HAS_SKILL_LOAD]->(sl:SkillLoad)
OPTIONAL MATCH (sub)-[:HAS_SKILL_LOAD]->(slsess:SkillLoad)   // session-level loads
WITH d.is_self_delegation AS self,
     d.context_depth       AS depth,
     d.context_scope       AS scope,
     coalesce(sl.skill_name, slsess.skill_name) AS payload_skill
RETURN self, depth, scope, payload_skill, count(*) AS n
ORDER BY n DESC
LIMIT 100

Rows where payload_skill IS NULL are transport with no skill payload — plain workers and context-sinks (separate those with a magnitude join, Section 5). Rows where payload_skill is a named lens (e.g. crusty-old-engineer, restless-old-brian) are the panel.

The reverse rollup — for each skill, its transport signature

Flip the same result to ask "when this skill runs, how is it convened?" A skill with one dominant transport signature has a strong flavour; a skill spread across transports is convened many ways.

// Same match as the flavour table, then group per skill.
// Cypher has no SQL window functions — compute the per-skill total with a
// collect/UNWIND two-pass, not OVER(PARTITION BY ...).
WITH payload_skill, d.is_self_delegation AS self,
     d.context_depth AS depth, d.context_scope AS scope, count(*) AS n
WHERE payload_skill IS NOT NULL
WITH payload_skill, sum(n) AS total,
     collect({self: self, depth: depth, scope: scope, n: n}) AS combos
UNWIND combos AS c
RETURN payload_skill, c.self AS self, c.depth AS depth, c.scope AS scope,
       c.n AS n, toFloat(c.n) / total AS share_within_skill
ORDER BY payload_skill, n DESC

Expect the panel lenses → ~100% self / none / conversation (convened cold by design). A context-sink skill → named-agent / none. A continuity skill → named / recent or / all.

Attribution discipline — attribute by CHILD, never by shared parent

The flavour join attributes by the sub-session's skill load — and that is the correct attribution, not a convenience. The trap: an inline panel (a *-here variant that loads into the CURRENT session instead of forking) shares its parent session with that session's own real work — git-ops, builders, the lot. If you attribute by the parent session's skill loads, you sweep all that unrelated work into the panel's bucket and massively over-count. Attributing by the child/sub-session that actually loaded the lens keeps the count clean and excludes the inline variant's host-session noise automatically.

Section 4 — Worked example (what the derived dimension revealed)

Applying the flavour metric to a real corpus of ~2,600 delegations:

Metric	Blanket read	Council-driven	Derived read (council removed)
Self-delegation share	42.0%	893	11.7%
none/conversation share	70.2%	893	54.6%

Reading:

• 893 delegations (34%) were one review panel convening its lenses — invisible on the transport axis, obvious on the flavour axis (100% self / none / conversation with a persona skill_name payload).
• Self-delegation was ~82% a panel artifact. The flat "self-delegation dominates" finding was an aggregate illusion; outside the panel, self-delegation is a marginal ~12% tactic.
• none/conversation survived as a genuine default (54.6%) but dropped from a landslide to a plurality once the derived population was removed.

The Move-3 recompute is the whole finding. Without it you would have shipped "self-delegation dominates," which is false.

Section 5 — The metric generalizes (other derived dimensions)

Flavour is one instance. The same three moves build others:

Context-sink vs offload (magnitude join). Both are none/conversation with a NULL skill payload — categorically identical. Discriminate on work volume inside the sub-session: sum iterations, tool calls, or token/content_length proxies. High volume + broad file reads = context-sink (the parent offloaded token cost); low volume = light task-offload. Here the discriminating signal is a magnitude, not a category.

// Magnitude proxy: iteration + tool-call volume inside each spawned sub-session.
MATCH (parent:Session {workspace: $workspace})
      -[:HAS_EXECUTION]->(:OrchestratorRun)-[:HAS_PART]->(:Iteration)
      -[:HAS_TOOL_CALL]->(:ToolCall)-[:TRIGGERED]->(d:Delegation)
WHERE d.context_depth = 'none' AND d.context_scope = 'conversation'
MATCH (sub:Session {node_id: d.sub_session_id})
OPTIONAL MATCH (sub)-[:HAS_EXECUTION]->(:OrchestratorRun)-[:HAS_PART]->(it:Iteration)
OPTIONAL MATCH (it)-[:HAS_TOOL_CALL]->(tc:ToolCall)
RETURN d.agent AS agent,
       count(DISTINCT it) AS iterations,
       count(tc)          AS tool_calls
ORDER BY tool_calls DESC
LIMIT 50

Continuity handoff (artifact join). recent / all transport paired with what the sub-session PRODUCED — a commit, a PR, a written file. Transport says "this agent inherited context"; the artifact says "…because it had to author something faithful to the session's history." That pairing is the signature of a genuine continuity handoff versus a needlessly heavy context pass.

The recipe never changes: blanket shape → discriminating second signal (category, magnitude, or artifact) → segment and recompute with the derived population removed.

Section 6 — Discipline and traps

• Recompute-with-removed is mandatory. A derived dimension you don't subtract out is just a colorful column. The finding is the delta to the headline.
• Causal, not spurious. Before trusting a join, ask whether the second signal explains the intent or merely co-occurs. The child-vs-parent attribution trap (Section 3) is a spurious-correlation trap: the parent-session join correlates but does not attribute.
• Verify the live schema. Delegation.sub_session_id, Session.node_id, and the SkillLoad attachment points are the load-bearing joins — confirm them with CALL db.schema.nodeTypeProperties() before relying on them. Note two properties seen live but NOT documented in context-intelligence-graph-query at time of writing: a resolved_agent on Delegation and a context (e.g. "fork") on SkillLoad. Treat them as verify-before-cite; the queries here deliberately depend only on documented properties (agent, skill_name) so they work either way.
• Size discipline still applies. Derived-metric queries aggregate — return counts and small grouped rows, never the full text of many rows. Everything in graph-query Section 6 holds.
• Scope honestly. Corpus mix skews absolute numbers (a dev-heavy workspace inflates git-ops and panel traffic). Report the scope with the metric, and offer the single-workspace recompute.

Section 7 — When to reach for this

• A query's headline is dominated by one structural mode and you can't narrate why each row happened → build a flavour/derived metric before reporting the mode.
• Someone asks "what is this agent/session actually doing" and the transport fields don't answer it → the answer lives in the payload/magnitude/artifact layer.
• You're about to report an aggregate ("X dominates", "most delegations are Y") → run the recompute-with-removed once to check the aggregate isn't an artifact of a single hidden sub-population.

Do NOT reach for it for a genuinely homogeneous population, or when the raw fields already narrate the row (a recent/all git-ops delegation explains itself). A derived metric you compute for everything stops being a signal.