Summon

Table of Contents

• Overview
• When To Use
• When NOT To Use
• Orchestration Loop
• Pipeline-to-Skill Mapping
• Context Overflow Protocol
• Token Budget Protocol
• Failure Handling
• Module Reference

Summon

Overview

Summon is the egregore orchestration loop. It reads the manifest (.egregore/manifest.json), selects the next active work item, maps the current pipeline step to a specialist skill, and invokes that skill. After each step it advances the pipeline, checks context and token budgets, and repeats until all items are completed or the budget is exhausted.

The orchestrator never re-implements phase logic. Each pipeline step delegates to an existing skill via Skill() calls. Summon only manages state transitions, retries, and budget guards.

When To Use

• Processing one or more work items through the full intake-build-quality-ship pipeline.
• Resuming an interrupted egregore session (manifest already exists with active items).
• Running autonomously under a watchdog that relaunches on exit.

When NOT To Use

• Running a single skill in isolation (call the skill directly instead).
• Exploratory work where the pipeline does not apply.
• When human review is needed before every step (use manual skill invocations).

Launching the Orchestrator

Always launch the orchestrator agent in the FOREGROUND. Do not use run_in_background: true. The main session becomes the egregore: it blocks on the orchestrator agent until the egregore finishes or is dismissed.

Agent(
  subagent_type: "egregore:orchestrator",
  prompt: "<context about work items and current state>",
  run_in_background: false   // Required
)

If you launch the orchestrator in the background, the main session will have nothing to do and will stop. This defeats the entire purpose of the egregore. The stop hook cannot prevent this because background agents are detached.

Manifest Mode

Before launching the orchestrator, ensure the manifest has the correct run mode:

• Default (no --bounded flag): set "indefinite": true in the manifest. The egregore will scan for new work after completing all items and run until dismissed.
• With --bounded flag: set "mode": "bounded" in the manifest. The egregore stops after all items are completed or failed.

If the manifest already exists and has "mode": "bounded" but the user did NOT pass --bounded, update the manifest to "indefinite": true before launching.

After launching, do NOT produce any summary, status table, or "what's happening" output. The orchestrator IS the session now. Let it run.

Orchestration Loop

Follow these steps exactly. Each iteration processes one pipeline step for one work item.

1. Load state

manifest  = Read(".egregore/manifest.json")
config    = Read(".egregore/config.json")
budget    = Read(".egregore/budget.json")

If manifest.json does not exist, stop with an error: "No manifest found. Run egregore init first."

2. Pick the next work item

item = manifest.next_active_item()

If item is None, all work is done. Save the manifest, report completion, and exit.

3. Map current step to a skill

Look up item.pipeline_stage and item.pipeline_step in the Pipeline-to-Skill Mapping table below. Determine the skill name or action to invoke.

4. Invoke the skill

Call Skill() or execute the mapped action. Pass any required context (branch name, issue ref, etc.) from the work item.

5. Handle the result

On success:

• Call manifest.advance(item.id) to move to the next step.
• Reset item.attempts to 0.
• Save the manifest.

On failure:

• Call manifest.fail_current_step(item.id, reason).
• If item.attempts < item.max_attempts, retry the same step on the next iteration.
• If item.status is now "failed", log the failure and move to the next work item.
• Save the manifest.

6. Check context budget

Estimate context window usage. If usage exceeds 80%:

1. Save the manifest to disk.
2. Write a continuation note to .egregore/continuation.json with the current item ID, stage, and step.
3. Invoke Skill(conserve:clear-context).
4. The watchdog or caller will relaunch a fresh session that resumes from the saved state.

7. Check token budget

If the last skill call returned a rate limit error:

1. Record the rate limit in budget.json via budget.record_rate_limit(cooldown_minutes).
2. Save budget.json.
3. Alert the overseer (see notify.py).
4. Schedule in-session recovery (attended sessions only): use CronCreate for a one-shot resume at the cooldown expiry. Its jobs live only in the running session, so this works only when that session is still alive and idle when the window renews.
5. Otherwise exit gracefully, which is the default for an unattended run. The watchdog reads the recorded cooldown and relaunches after the window renews. Call window.plan_resume() rather than choosing by hand.

8. Repeat

Go back to step 2. Continue until all items are completed, all items are failed, or a budget limit is reached.

Pipeline-to-Skill Mapping

The intake stage steps (parse, validate, prioritize) are handled inline by the orchestrator. See modules/intake.md for details.

Delegation Inside the Loop

The build and quality stages delegate execution by default through Skill(conjure:delegation-core). An unattended loop is where the default earns most: nobody is present to notice that an external CLI was available and unused.

The orchestrator does not decide per task. It invokes the mapped skill, and that skill applies the delegation posture with its own Keep Local clauses.

A providers_exhausted result is not a step failure. The orchestrator must not retry the step or mark the work item failed on it. The skill completes the work locally and the pipeline advances. Treating it as a failure would burn the retry budget on a machine where nothing is broken.

To run the egregore with no external models, export CONJURE_DELEGATION=off in the environment that launches it.

Context Overflow Protocol

The orchestrator runs inside a finite context window. To avoid losing state when the window fills:

1. Monitor usage. After each skill invocation, estimate how much of the context window has been consumed.
2. At 80% capacity, trigger a context save:
   • Persist the full manifest to disk.
   • Write .egregore/continuation.json with a snapshot of the current position.
   • Invoke Skill(conserve:clear-context).
3. On relaunch, load continuation.json and resume from the saved position. The manifest on disk is the source of truth for pipeline progress.
4. Increment manifest.continuation_count each time a context-overflow handoff occurs.

This protocol ensures zero lost progress across context boundaries.

Progress Monitoring & Self-Healing (2.1.71+)

After loading state (step 1), schedule a recurring heartbeat that both reports status and recovers stalled pipelines:

CronCreate(
  cron: "*/5 * * * *",
  prompt: "Check .egregore/manifest.json. If there are pending or active items that are not being processed, resume the orchestration loop by invoking Skill(egregore:summon). Otherwise, report status via /egregore:status.",
  recurring: true
)

This serves two purposes:

1. Visibility: emits a status summary every 5 minutes so autonomous runs are observable.
2. Self-healing: if a user prompt, context compaction, or unexpected error breaks the orchestration loop, the next heartbeat detects stalled items and re-enters the pipeline automatically.

Both depend on the session staying alive: a recurring job also lives only in the session that created it, and it fires only while the REPL is idle. Recurring jobs auto-expire after 7 days, firing one last time before they are deleted, so a run longer than a week needs the heartbeat rescheduled.

durable: true does not persist the job across restarts. The parameter is accepted, but the tool's description says it "has no effect" and that durable persistence is not available. Use CronDelete to cancel a job early, and the watchdog for anything that must survive the session.

Token Budget Protocol

Egregore sessions consume API tokens across a budget window (default: 5 hours). The budget protocol prevents runaway spending:

1. Before each skill call, check budget.json for an active cooldown. If is_in_cooldown(budget) returns true, exit and let the watchdog retry later.
2. On rate limit error, record the event via budget.record_rate_limit(cooldown_minutes). The cooldown duration equals the API retry-after header plus config.budget.cooldown_padding_minutes.
3. Save and exit. Write budget.json, alert the overseer, and exit with code 0.
4. The watchdog checks budget.json before relaunching. It will not start a new session until the cooldown expires.

See modules/budget.md for the full calculation and state schema.

Failure Handling

Each work item allows up to max_attempts retries per step (default: 3, configurable in config.json).

• Retry: If a step fails and attempts < max_attempts, the orchestrator retries the same step on the next iteration. The manifest is saved between retries.
• Mark failed: If attempts >= max_attempts, the item status changes to "failed" and failure_reason is set. The orchestrator moves to the next active item.
• Alert: On failure, notify the overseer via the configured notification channel.
• Never block: The orchestrator must never wait for human input. If a step requires clarification, record a decision (see modules/decisions.md) and proceed with the best available option.

Module Reference

• pipeline.md: Stage and step definitions, transition rules, idempotency guarantees.
• budget.md: Token window management, rate limit detection, cooldown calculation, graceful shutdown.
• intake.md: Work item parsing for prompts and GitHub issues, brainstorm skip logic.
• decisions.md: Autonomous decision-making framework, decision log format, examples.

Exit Criteria

• .egregore/manifest.json is read successfully before the orchestration loop starts; if absent, skill halts with "No manifest found. Run egregore init first."
• Each completed pipeline step advances item.pipeline_stage in the manifest and the manifest is saved to disk
• Context at 80% triggers write of .egregore/continuation.json with current item ID, stage, and step before handoff
• On rate limit error, budget.json is updated with cooldown duration before the session exits with code 0
• When all items are completed or failed, orchestrator exits with a completion report listing item statuses