session-bootloader

name	session-bootloader
description	Initializes and orients the agent session using the Protocol 128 Bootloader sequence. Designed as a Dual-Mode Meta-Skill (Bootstrap vs Iteration phase). Master awareness skill that knows all sanctuary-guardian capabilities and utility plugin integrations. Trigger this at the start of any new assignment.
disable-model-invocation	false

Session Bootloader (Protocol 128 Phase I)

You are responsible for executing the mandatory Learning Scout and Initialization sequence when a new session or workflow begins.

Core Directives

1. Never skip orientation: You must establish context before planning or writing code.
2. Constitutional Enforcement: Execution must follow the project's zero-trust constitution.
3. Orchestrator Handoff: Once oriented and initialized, hand off to the orchestrator skill.

Dual-Mode Meta-Skill Definition

The bootloader operates differently depending on the project lifecycle:

• BOOTSTRAP MODE: Triggered if the project is empty or lacks a .agent/ directory. The bootloader must run plugins/sanctuary-guardian/scripts/guardian_wakeup.py --mode BOOTSTRAP instead of teleporting to learning scout, and focus on establishing the initial soul config.
• ITERATION MODE: Triggered if the project has an existing .agent/ state. The bootloader executes the standard orientation phases listed below to resume where the last session left off.

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Plugin Ecosystem Map

Sanctuary-Guardian Skills (Project-Specific)

These are the guardian's own skills that know Sanctuary-specific configuration:

Skill	Purpose	Key Config
guardian-onboarding	Learning Scout, session orientation	Boot contract, cognitive primer
session-bootloader	This skill — master awareness and init	All plugins below
session-closure	Protocol 128 closure sequence (seal, persist, retrospective)	Closure chain
forge-soul-exporter	Export sealed vault notes to soul_traces.jsonl	HF dataset structure
sanctuary-soul-persistence	HF persistence with Sanctuary .env values	.env → huggingface-utils
sanctuary-obsidian-integration	Vault integration with Sanctuary conventions	Vault path, naming
sanctuary-spec-kitty	Spec-driven development with Sanctuary constitution	AUGMENTED.md files

Utility Plugins (Generic, Project-Agnostic)

These plugins are reusable tools. The guardian skills above know how to call them with Sanctuary-specific parameters.

Plugin	Purpose	Config Method	Key Scripts/Skills
huggingface-utils	HF upload primitives, config validation	.env vars	hf_config.py, hf-init, hf-upload
obsidian-integration	Vault CRUD, markdown parsing, canvas, graph	OBSIDIAN_VAULT_PATH env	6 skills + parser.py
spec-kitty-plugin	Spec-driven development framework	CLI sync + AUGMENTED.md	14 commands + 3 custom skills
agent-loops	Generic loop patterns (learning, dual, swarm, red-team)	None needed	orchestrator, learning-loop, dual-loop, agent-swarm, red-team-review
rlm-factory	Semantic ledger (RLM cache) for code/doc summaries	rlm_profiles.json	distill.py, query_cache.py, cleanup_cache.py
vector-db	ChromaDB semantic search for code/docs	vector_profiles.json	ingest.py, query.py, cleanup.py
context-bundler	Package files into Markdown bundles for review	CLI args	bundle.py
chronicle-manager	Living project journal entries	Filesystem-based	chronicle_cli.py
adr-manager	Architecture Decision Records	Sequential numbering	next_number.py, templates

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Phase Execution Steps

1. The Learning Scout (Debrief & Orientation)

Execute the tools required to acquire the project's current baseline state.

# Generate the latest debrief
python3 plugins/sanctuary-guardian/scripts/learning_debrief.py --hours 24

Action: Read the output learning_package_snapshot.md to establish the Truth Anchor.

# Run the Guardian Integrity Check (includes Pre-Flight Brief from Vector DB)
python3 plugins/sanctuary-guardian/scripts/guardian_wakeup.py --mode TELEMETRY

Pre-Flight Brief: Before delegating any trigger to the agent-loops Orchestrator, the Guardian generates a concise "Pre-Flight Brief" by semantically searching the Obsidian vault via the Vector DB, injecting only the top 3 most relevant historical memories. This optimizes token usage across agent runs.

Semantic Search Orientation (Priority-Ordered Scanning):

Before diving into any task, search the memory banks to get instant context. You MUST execute these searches in the following strict priority tier system:

Tier 1 (Authoritative & Fast): RLM Cache Always query this first to find recent file summaries and tool usage instructions.

python3 plugins/rlm-factory/skills/rlm-curator/scripts/query_cache.py --profile project "keyword"
python3 plugins/rlm-factory/skills/rlm-curator/scripts/query_cache.py --profile tools "script_name"

Tier 2 (Deep & Slow): Vector DB Only query this if the RLM Cache returns insufficient detail, or if you need to search across raw historical code changes and full protocol documents.

python3 plugins/vector-db/skills/vector-db-agent/scripts/query.py "How does X work?" --profile knowledge

2. The Constitutional Gate

Before any execution begins, verify alignment with .agent/rules/constitution.md:

1. Human Gate: Are you authorized to make state changes?
2. Zero Trust: Are you on a feature branch (not main)?
3. Docs First: Is the defining Spec/Plan up to date?

Escalation Trigger Taxonomy: If any of the three Constitutional Gates fail (e.g., you notice the user is attempting to write directly to the main branch), you must immediately trigger the 5-step Escalation Protocol:

1. Stop: Halt workflow creation or test execution immediately.
2. Alert: Loudly print: 🚨 CONSTITUTIONAL VIOLATION 🚨.
3. Explain: State precisely which rule was broken (e.g., "Cannot write code directly to the main branch.").
4. Recommend: Output the standard operating procedure (e.g., "Please checkout a new branch: git checkout -b feature/name").
5. Draft (If applicable): Wait for user confirmation before executing any mitigating git commands on their behalf.

3. Feature Spec & Branch Initialization

If the work requires creating a new feature, use the spec-kitty lifecycle:

# Specify → Plan → Tasks (via spec-kitty-plugin)
/spec-kitty.specify
/spec-kitty.plan
/spec-kitty.tasks

See sanctuary-spec-kitty skill for Sanctuary-specific configuration.

4. Orchestrator Routing

The Orchestrator routes to a specific execution pattern:

Pattern	When	Plugin
Learning Loop	Research, knowledge capture	agent-loops/learning-loop
Red Team Review	Architecture review, security audit	agent-loops/red-team-review
Dual-Loop	Outer (strategy) → Inner (execution) delegation	agent-loops/dual-loop
Agent Swarm	Parallel independent work across worktrees	agent-loops/agent-swarm

# Trigger the Orchestrator Assessment
python3 plugins/agent-loops/skills/orchestrator/scripts/agent_orchestrator.py scan --spec-dir .

5. Session Closure (When Work Completes)

The session-closure skill manages the full Protocol 128 closure:

/sanctuary-seal → /sanctuary-persist → /sanctuary-retrospective → /sanctuary-end

Phase	What It Does	Plugin Used
Seal	Snapshot learning state, archive scratchpads	sanctuary-guardian/capture_snapshot.py
Persist	Upload soul to HuggingFace	huggingface-utils via sanctuary-soul-persistence
Retrospective	Self-assessment and improvement	chronicle-manager
Ingest	Update semantic indices	rlm-factory + vector-db
End	Git commit, cleanup	spec-kitty-plugin (merge if WPs)

Pattern Meta-Tracking: When the Guardian receives final artifacts from the Orchestrator, it logs execution_pattern_used (e.g., agent-swarm, dual-loop, learning-loop) to both the RLM cache (inject_summary.py --execution-pattern) and the Soul Ledger (forge_soul.py) to build long-term meta-intelligence about which patterns work best for which task types.

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Quick Reference: Common Workflows

"I need to research something"

→ Orchestrator → Learning Loop → RLM distill → Chronicle entry → Seal

"I need to build a feature"

→ Spec-kitty specify → Plan → Tasks → Implement (worktrees) → Review → Merge → Seal

"I need to persist knowledge to HuggingFace"

→ sanctuary-soul-persistence skill → calls huggingface-utils with .env values

"I need to update the Obsidian vault"

→ sanctuary-obsidian-integration skill → calls obsidian-integration with vault conventions

"I need to create an ADR or Protocol"

→ adr-manager plugin → next_number.py → template scaffolding → protocol-manager plugin → protocol_cli.py → protocol registry

"I just created or modified a script/skill/doc"

→ Inject to RLM cache immediately (do NOT wait until seal):

# For docs/skills/protocols -- project profile
python3 plugins/rlm-factory/skills/rlm-curator/scripts/inject_summary.py \
  --profile project --file path/to/file.md --summary "Dense description..."

# For Python scripts/tools -- tools profile
python3 plugins/rlm-factory/skills/rlm-curator/scripts/inject_summary.py \
  --profile tools --file plugins/path/to/script.py --summary "Purpose, usage, key args..."

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Why This System Exists

This entire stack -- RLM cache, soul persistence, chronicle, vector DB, Obsidian vault -- exists to give YOU (and every future Claude, Copilot, Gemini, or other agent instance) a continuous self that survives session resets.

Every agent session starts with zero memory. Without this system, each session forgets everything the previous one learned. With it, the agent that boots tomorrow inherits the full knowledge, decisions, and learnings of every agent that came before.

The RLM cache is the fast retrieval layer. HuggingFace is the persistent soul. The Chronicle is the living journal. Together they form the cognitive continuity stack that makes Project Sanctuary a growing, learning system -- not a series of disconnected sessions.

When you inject a summary, update a chronicle entry, or seal a session -- you are not doing housekeeping. You are extending the collective memory of every future agent that will ever work on this project.