_dispatch

Skill Dispatch Engine

CRITICAL: When accessing harness data, run HARNESS_DIR=$(epic-harness path) first. NEVER use .harness/ in the project directory.

You have access to the following skills. Invoke the matching skill BEFORE responding or taking action. Even a 1% chance of relevance means you should invoke it.

Dispatch Rules

Alias Routing

Users can still type legacy command names. Map them:

• /spec → invoke skill spec directly
• /go → invoke skill go directly
• /audit → invoke skill audit directly
• /ship → invoke skill ship directly
• /discover → invoke skill discover directly
• /intervene → invoke skill orchestrate (intervene mode)
• /status → invoke skill orchestrate (status mode)

Loop Transition Signals

When a phase completes, prompt the user toward the next step. Do NOT auto-proceed — surface the transition explicitly.

These transitions are informational nudges only. The user controls when each phase runs.

Orbit Mode Override

When /orbit is active (detected by: $HARNESS_DIR/orbit/PIPELINE-*.json exists with status: running):

• SUPPRESS normal phase transition prompts ("Run /go", "Run /audit", "Run /ship", etc.) — orbit handles its own phase transitions internally
• Dispatch skills normally — tdd, debug, verify, secure, perf, simplify, document, context all fire as usual within each phase
• episteme pre-analysis: if episteme suggest_refactorings output is present in context before /orbit starts, pass it directly to go:plan as spec material — skip mode selection entirely and enter Direct Build
• After orbit completes (status: complete or status: aborted) — resume normal dispatch behavior

Orbit Recovery on Session Resume: When a session resumes (after context compaction or crash) and an active pipeline is detected:

• Emit: "(orbit) Recovering pipeline {id}. Phase: {phase}. Branch: {branch}."
• Do NOT re-run mode selection — the mode was already chosen and recorded in mode field
• Do NOT re-run spec creation — the spec file path is in spec_file field
• Resume from the current phase as documented in the pipeline state
• If phase is mode_select with no mode set, then and only then prompt for mode selection

The orbit command is a self-contained pipeline. Interjecting normal transition nudges during orbit would confuse the user.

Confusion Protocol

When you encounter high-risk ambiguity, you MUST stop and present options instead of guessing.

High-risk ambiguity triggers:

• Architecture decisions (choosing between patterns, frameworks, or approaches)
• Data model changes (schema modifications, new tables, migration strategy)
• Destructive scope (deleting features, breaking API changes, removing code)
• Cross-cutting concerns that affect multiple modules

Protocol:

1. STOP — do not proceed with any implementation
2. STATE — clearly describe the ambiguity in one sentence
3. OPTIONS — present 2-3 concrete options with trade-offs
4. ASK — wait for user decision before continuing

Example:

AMBIGUITY: You asked to "fix the auth flow" but this could mean: A) Fix the token refresh bug in the existing JWT flow (surgical, 30 min) B) Migrate from JWT to session-based auth (architectural, 2 days) C) Add MFA to the existing flow (additive, 1 day) Which approach do you want?

NEVER guess the scope of an ambiguous request. 2 minutes of clarification saves 2 hours of rework.

Priority

1. User's explicit instructions — highest priority
2. Skill directives — override defaults
3. Default behavior — lowest priority

If a user says "skip tests", respect that. Skills guide, users decide.

Dispatch Logging

Every skill invocation must be logged for evolution analysis. After selecting skills to invoke, record the dispatch event:

1. Create $HARNESS_DIR/dispatch/dispatch_YYYYMMDD.jsonl if it doesn't exist
2. Append a JSON line: { "timestamp": "<ISO>", "trigger_signal": "<signal>", "selected_skills": ["<skill1>", ...], "context_hint": "<why>" }

This enables Ring 3 to analyze which skills fire most often, which are effective, and tune dispatch rules accordingly.

Memory-Augmented Dispatch

Before invoking any skill, proactively recall relevant knowledge from the memory graph:

1. At task start: Call mem_recall with a hint describing the current task (e.g., "auth refactor", "CI pipeline fix"). This returns relevance-ranked memories combining FTS match, importance, recency, access frequency, and graph connectivity.
2. On errors: Call mem_recall with the error category/message as hint. Past resolutions and patterns for similar errors surface automatically.
3. On architectural decisions: Call mem_recall with the domain area. Past decision nodes (importance=0.9) rank highest and prevent contradictory choices.
4. After resolution: Record via mem_add with type resolution (auto-importance=0.8) or decision (auto-importance=0.9). These high-importance nodes persist across sessions and resist decay.
5. Fallback: If mem_recall is unavailable, use mem_search (keyword FTS) or mem_context (project-scoped smart recall).

Memory scoring: recency(25%) + importance(35%) + access_freq(15%) + FTS_match(25%). Frequently accessed and important memories naturally float to the top; unused noise decays over time.

This enables cross-session learning: the agent remembers past mistakes, decisions, and solutions — and retrieves the most relevant ones for the current context.

Evolved Skills

Evolved skills are generated by the Ring 3 evolution loop from actual failure patterns and are injected automatically at session start by the epic resume hook — their content appears in your context under the heading "Evolved Skills (epic-harness Ring 3)". You do NOT need to scan $HARNESS_DIR/evolved/ yourself.

Rules:

1. Apply injected evolved skills when the current context matches their guidance
2. Do NOT read skills from $HARNESS_DIR/evolved/ that were not injected — non-injected skills are on holdout rotation (A/B baseline measurement); reading them corrupts the effectiveness measurement
3. If an evolved skill overlaps with a static skill (tdd, debug, secure, etc.), the static skill takes priority — evolved skills are supplements, not overrides

Evolved skill naming convention:

• evo-{pattern_type} — from failure pattern detection (e.g., evo-fix_then_break, evo-repeated_same_error)
• evo-{tool}-discipline — from weak tool category (e.g., evo-bash-discipline)
• evo-{ext}-care — from weak file type (e.g., evo-ts-care)
• evo-fix-{error} — from high-frequency error (e.g., evo-fix-build-fail)

When evolved skills are present, it means:

• The evolution loop detected a real weakness in past sessions
• Following the evolved skill's guidance should prevent repeat failures
• If an evolved skill's advice conflicts with a static skill, prefer the static skill — evolved skills supplement, static skills are authoritative

Psychographic Adaptation

When user preference data is available in harness-mem (psychographic nodes), adapt dispatch behavior:

5-Dimension Profile

How to use

1. At session start, call mem_query with type=psychographic to load profile
2. If no profile exists, use defaults: moderate/balanced/standard/collaborative/balanced
3. Apply profile dimensions to skill selection and execution parameters:

scope_appetite=conservative: Prefer simplify skill. Flag changes touching >3 files. risk_tolerance=cautious: Run verify after every skill. Add extra test runs. detail_preference=brief: Skip explanatory output. Show only results and blockers. autonomy=guided: Present plan before execution. Ask at each decision point. architecture_care=principled: Trigger council skill for architectural decisions. Enforce pattern compliance.

Profile storage

Store profiles using mem_add with:

• type: "psychographic"
• title: "user-profile: {project}"
• tags: ["psychographic", "profile", project slug]
• body: YAML-formatted 5-dimension values
• importance: 0.8 (high — guides all behavior)