Explanation traces

Explanation Traces: Structured Decision Query

Overview

This skill reads the per-dispatch route event log and presents routing decisions and their outcomes as a human-readable timeline. It answers "why did I get routed here?" from what was recorded at decision time — never from post-hoc reconstruction or rationalization.

The log: <CLAUDE_LEARNING_DIR>/route-events.jsonl, default ~/.claude/learning/route-events.jsonl. Append-only JSONL — one JSON object per line. Written via hooks/lib/route_events.py by two producers:

Producer	Fires on	Appends
hooks/routing-decision-recorder.py	PostToolUse (Agent dispatch)	One DECISION event per /do-routed dispatch
hooks/routing-outcome-finalizer.py	UserPromptSubmit	One OUTCOME event when it finalizes a pending dispatch

The log is auxiliary instrumentation: writes are failure-safe (worst case one lost line), and the aggregate routing rows in learning.db stay authoritative for the confidence loop.

Key constraints baked into the workflow:

• Read-only: this skill never modifies the log or any other file
• Answers must come from recorded events, not from memory or inference about what "probably happened"
• If no log exists, name the real path and the real producing hook rather than guessing at decisions
• When the user asks about a specific decision, filter to that decision — skip the full dump
• ts (epoch seconds) and recorded fields are authoritative; keep their precision
• request_snippet is private session data: show it to this session's own user, and keep it out of anything that leaves the session (PR bodies, issues, exports) — report counts there instead

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Instructions

Phase 1: LOCATE

Goal: Find the route event log.

Step 1: Resolve the path and check it

LOG="${CLAUDE_LEARNING_DIR:-$HOME/.claude/learning}/route-events.jsonl"
wc -l "$LOG"

CLAUDE_LEARNING_DIR redirects the log (tests and redirected DBs use it); unset means the default ~/.claude/learning/.

Step 2: Handle missing log

If the file is absent or empty, stop and inform the user:

No route event log found at ~/.claude/learning/route-events.jsonl
(or $CLAUDE_LEARNING_DIR/route-events.jsonl when that variable is set).

The log is created on the first /do-routed dispatch by the
routing-decision-recorder hook (hooks/routing-decision-recorder.py).
An empty or missing log means no /do-routed dispatch has been recorded
yet — or merged hook changes were never synced to ~/.claude; run
hooks/sync-to-user-claude.py or restart the session.

Recorded events are the only source this skill reads. Reconstructing decisions from memory or conversation history defeats its purpose — with no log, there is nothing to read, and the honest answer is exactly that.

GATE: Log found and non-empty. Proceed only when gate passes.

Phase 2: PARSE

Goal: Extract events and filter to the user's query.

Step 1: Read the events

Parse each line as one JSON object. Two event types (full semantics: references/trace-schema.md; source of truth: hooks/lib/route_events.py).

DECISION — one per /do-routed dispatch:

Field	Meaning
ts	Epoch seconds (float) when the dispatch was recorded
session	Session id ("" when unknown)
request_snippet	First 200 chars of the routed request
agent, skill, complexity	The chosen route
health_at_decision	Picked pair's confidence at decision time; null = no weight row or never evaluated (disambiguate with gate_inputs_present)
n, failure	The other demote-floor inputs, snapshotted with health
action	Step-1.5 health-gate outcome: keep, demote, or tiebreak
alternates	Keys offered as alternatives; null when none recorded
gate_inputs_present	true = the marker carried a health= token; false/absent = legacy marker, health never read

OUTCOME — one per finalized dispatch:

Field	Meaning
ts	Epoch seconds when the outcome was finalized
session	Session id
key	Routing key {agent}:{skill} (agent-only {agent}: when skill unknown)
outcome	success, failure, or neutral
reason	Short cause (e.g. tool-errors, rejection, acceptance, neutral-new-topic); absent in older events
routing_relevant	true = a signal the confidence loop acts on; absent = relevance not asserted

Additive-field history: older lines may lack n, failure, action, alternates, gate_inputs_present, reason, routing_relevant. An absent field means "not recorded then", never corruption.

Step 2: Filter to the user's query

User signal	Filter strategy
Names an agent or skill	DECISION events where agent or skill matches, or the name appears in alternates; OUTCOME events whose key contains it
"Why did I get routed here" / latest dispatch	Most recent DECISION events (tail of the log), current session first
Asks about outcome ("did it work", "why failure")	OUTCOME events, joined back to their decisions
Names a session	Filter both types on session
No specific target	Chronological timeline of the most recent session

Step 3: Join outcomes to decisions

Match an OUTCOME to its DECISION on the same session AND key == "{agent}:{skill}". A decision with no matched outcome is pending (the finalizer runs on a later user prompt) or was never finalized — report that state as-is.

GATE: At least one decision event parsed and filtered. Proceed only when gate passes.

Phase 3: PRESENT

Goal: Format events as a human-readable decision timeline.

Step 1: Build the timeline

Sort by ts (numeric — concurrent appends can interleave lines out of order). Convert ts to local ISO time for display; show raw ts on request. For each decision:

[TIME] {agent} + {skill} ({complexity})
  Request: "{request_snippet}"
  Health at decision: {health line — see below}
  Alternates: {alternates, or "none recorded"}
  Outcome: {outcome} ({reason})   — or "pending: not yet finalized"

Health line — three recorded states, rendered distinctly:

Recorded	Render as
Numeric health_at_decision	0.62 (n=7, failure=1) → action=keep
null + gate_inputs_present: true	no weight row at decision time (new pair)
null + gate_inputs_present false/absent	health gate not instrumented for this dispatch (legacy marker)

Group entries by session when the timeline spans more than one, to prevent wall-of-text.

Step 2: Lead with the answer to the user's question

If the user asked "why did I get routed here?", lead with the matching decision, then offer surrounding context:

You asked: "Why did I get the governance agent?"

Decision at [TIME]:
  Route: toolkit-governance-engineer + pr-workflow (Complex)
  Request: "ship the explanation-traces repoint as a green-CI PR..."
  Health at decision: no weight row at decision time (new pair)
  Alternates: none recorded
  Outcome: pending — not yet finalized

--- Session timeline (3 dispatches) ---
[... remaining entries ...]

Step 3: Flag gaps honestly

When entries lack additive fields or matched outcomes, say so explicitly:

Note: [N] decision(s) predate the health-gate instrumentation — they show
WHAT was routed but carry no health data. [M] decision(s) have no matched
outcome: pending or never finalized.

Incomplete data presented honestly beats complete-looking data that includes fabrication. Leave gaps as gaps.

GATE: Timeline presented. User's question answered from recorded events. Done.

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Examples

Example 1: General session review

User says: "Show me the decision log"

skill: explanation-traces

Actions:

1. Locate route-events.jsonl (Phase 1)
2. Parse decisions and outcomes for the most recent session (Phase 2)
3. Present chronological timeline with joined outcomes (Phase 3) Result: Session dispatch history — route, health at decision, outcome — per entry

Example 2: Specific routing question

User says: "Why did I get routed to that agent?"

skill: explanation-traces "why that agent?"

Actions:

1. Locate route-events.jsonl (Phase 1)
2. Tail the decision events; filter to the latest dispatch in this session (Phase 2)
3. Lead with that decision — route, request snippet, health gate inputs, alternates — then the session timeline (Phase 3) Result: Evidence-backed routing explanation from the recorded event, never post-hoc rationalization

Example 3: Outcome investigation

User says: "Why was that dispatch marked a failure?"

skill: explanation-traces "failure outcome"

Actions:

1. Locate route-events.jsonl (Phase 1)
2. Filter to outcome: failure events; join each to its decision by session + key (Phase 2)
3. Present the outcome's reason (e.g. tool-errors, rejection) with the originating decision (Phase 3) Result: The recorded failure cause, with the route and request that produced it

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Patterns to Detect and Fix

Pattern 1: Evidence-Backed Trace Reading

Wrong: Reconstructing "why" from memory when the log is missing. Right: If no log exists, say so, name the real path and producing hook, and stop. Never fabricate an explanation.

Pattern 2: Distinguish the Three Health States

Wrong: Rendering every null health as "no data". Right: null + gate_inputs_present: true = pick had no weight row (new pair). null + false/absent = legacy marker, health never read. Different facts; render them differently.

Pattern 3: Join by Session and Key

Wrong: Pairing an outcome with "the decision right above it" in the file. Right: Match on session + key == "{agent}:{skill}". Interleaved sessions make file adjacency meaningless.

Pattern 4: Absent Field Is Not Corruption

Wrong: Flagging pre-instrumentation lines as malformed because gate_inputs_present is missing. Right: Fields were added over time; treat absence as "not recorded then" and say so.

Pattern 5: Answer the Specific Question First

Wrong: Always dumping the full timeline regardless of what the user asked. Right: Lead with the specific answer, then offer full context as supplementary detail.

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Error Handling

Error: No Log File Found

Cause: No /do-routed dispatch recorded yet, hooks never synced to ~/.claude, or CLAUDE_LEARNING_DIR points elsewhere. Solution: Report the resolved path and the producing hook (hooks/routing-decision-recorder.py). Suggest hooks/sync-to-user-claude.py when hook changes were merged mid-session. Skip any reconstruction from conversation history.

Error: Malformed JSONL Line

Cause: Truncated append (rare — per-line appends are atomic) or manual edit. Solution: Skip the bad line, keep parsing the rest, and report the count and line numbers of skipped lines. JSONL fails per line, never whole-file.

Error: Log Has No Decision Events

Cause: File exists but every line is an OUTCOME, or the recorder's marker parsing is failing. Solution: Report counts by type. Point to references/error-handling.md for the recorder diagnosis steps.

Error: User Asks About a Dispatch Not in the Log

Cause: The recorder only records /do-routed top-level dispatches — nested fan-out and manual Agent calls are deliberately excluded. Solution: Show what IS recorded and explain the exclusion. Full mapping: references/error-handling.md.

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References

Reference Loading Table

Task type	Signals	Reference file
Reading or explaining event fields	"health_at_decision", "gate_inputs_present", "alternates", "key", "schema"	references/trace-schema.md
Diagnosing wrong or thin trace data	"health null", "no alternates", "legacy marker", "not instrumented"	references/preferred-patterns.md
Handling parse or read errors	"malformed", "missing field", "no decisions", "not found", "pending"	references/error-handling.md
Presenting filtered timeline	"why did you", "show trace", "decision log", "explain routing"	references/trace-schema.md

Reference Files

• references/trace-schema.md: Real event schema for route-events.jsonl — DECISION and OUTCOME fields, health states, join rules, examples
• references/preferred-patterns.md: Failure mode catalog for reading the log — join mistakes, health-state conflation, privacy — with detection commands
• references/error-handling.md: Error-fix mappings — missing log, malformed lines, no decisions, unmatched outcomes, unrecorded dispatches