DR Context Pipeline

DR Context Pipeline

Use this skill to standardize how an agent loads memory into its prompt for correctness.

Prerequisites

• A file-based memory layout with memory/always_on.md and topic files under memory/topics/.
• Recommended: install dr-memory-foundation or an equivalent memory layout.
• For semantic retrieval assurance, OpenClaw memory search should be configured and visible through openclaw memory status --deep --json.

Quick Install

These commands work from anywhere because they set WORKSPACE:

export WORKSPACE=${WORKSPACE:-~/.openclaw/workspace}
cd "$WORKSPACE"
clawhub install dr-context-pipeline --version 2.1.1 --dir skills --force
python3 ./skills/dr-context-pipeline/scripts/install_pipeline.py --target "$WORKSPACE/context_pipeline"
ls -1 "$WORKSPACE/context_pipeline"
git -C "$WORKSPACE" diff -U20 AGENTS.md | cat
python3 ./skills/dr-context-pipeline/scripts/validate_pipeline.py --context-root "$WORKSPACE/context_pipeline"
python3 ./skills/dr-context-pipeline/scripts/memory_watchdog.py --freshness-minutes 240 --min-bytes 200 --require-semantic --probe-query "context pipeline embeddings semantic retrieval" --min-search-results 1
git -C "$WORKSPACE" status -sb context_pipeline AGENTS.md
echo "CONTEXT PIPELINE APPLY COMPLETE"

Paste every command's output. If any step fails or the watchdog reports GAP, stop and reply NOT EXECUTED: .

Apply To This Workspace

When the user asks to apply this skill, follow this order:

1. Lay down the files: python3 ./skills/dr-context-pipeline/scripts/install_pipeline.py --target context_pipeline
2. Show the tree: ls -1 context_pipeline
3. Patch AGENTS.md with the Context Pipeline instructions, preserving unrelated content.
4. Validate: python3 ./skills/dr-context-pipeline/scripts/validate_pipeline.py --context-root context_pipeline
5. Run the semantic memory watchdog: python3 ./skills/dr-context-pipeline/scripts/memory_watchdog.py --freshness-minutes 240 --min-bytes 200 --require-semantic --probe-query "context pipeline embeddings semantic retrieval" --min-search-results 1
6. Show final state: git status -sb context_pipeline AGENTS.md
7. Print the success banner: echo "CONTEXT PIPELINE APPLY COMPLETE"

This flow must be idempotent. If validation or the watchdog fails, stop with NOT EXECUTED and quote the issue.

Memory Commit / Continue Workflow

• When Daniel says "memorize this" or similar, run references/MEMORY_COMMIT.md.
• When he says "let's continue" after a reset, reload memory/now.md, memory/open-loops.md, and the relevant topic files before acting.

Runtime Modes

Every task must follow references/RUNTIME_CHECKLIST.md.

Modes:

• normal - compact day-to-day operation
• debug - full Retrieval Bundle and Context Pack artifacts
• audit - debug artifacts plus Receipt Ledger

Default to normal unless the user explicitly asks for debug, audit, tracing, receipts, or a full evidence dump.

Semantic Memory Contract

When persistent memory is relevant, semantic or hybrid memory search is the preferred retrieval source.

The agent must record retrieval mode internally as one of:

• semantic
• hybrid
• file_fallback
• not_needed

If semantic memory is unavailable, not attempted, or degraded to file/keyword retrieval:

• normal mode must say so compactly in the footer
• debug/audit mode must include the reason
• operational checks that depend on semantic recall must stop with NOT EXECUTED: memory/embedding gap

Normal footer shape:

Context: task type ; snippets ; retrieval ; compression .

Notification Safety

The watchdog can emit a dry-run notification payload on GAP, but this skill must not enable live delivery by itself.

Live memory/embedding alerts require a separate approval gate covering:

• schedule/frequency
• Discord DM target
• cooldown/suppression behavior
• exact message format
• retry behavior

Until approved, the watchdog prints JSON only.

Operating Procedure

1. Load memory/always_on.md.
2. Route the message deterministically using references/router.yml.
3. Retrieve relevant snippets from memory; prefer semantic/hybrid memory search when persistent memory is relevant.
4. Build a Retrieval Bundle object that matches the schema and records retrieval mode.
5. Compress to a Context Pack using references/compressor_prompt.txt.
6. Lint the Context Pack. If lint fails, fall back to raw retrieved snippets.
7. Call the main reasoning model with always-on policy + Context Pack + user message.
8. For debug and audit, persist artifacts described in references/RUNTIME_ARTIFACTS.md.
9. Emit evidence according to the active runtime mode.

What To Read / Use

• Router + caps: references/router.yml
• Compressor prompt: references/compressor_prompt.txt
• Retrieval Bundle schema: references/schemas/retrieval_bundle.schema.json
• Context Pack schema: references/schemas/context_pack.schema.json
• Receipt Ledger schema: references/schemas/receipt_ledger.schema.json
• Runtime artifact layout: references/RUNTIME_ARTIFACTS.md
• Runtime checklist: references/RUNTIME_CHECKLIST.md
• Semantic memory checklist: references/SEMANTIC_MEMORY_ASSURANCE.md
• Starter example file: references/tests/golden.json
• Positive-path harness fixtures: references/tests/harness_cases.json
• Anti-fabrication fixtures: references/tests/anti_fabrication_cases.json
• Installer/validator/watchdog scripts: scripts/install_pipeline.py, scripts/validate_pipeline.py, scripts/memory_watchdog.py
• Behavioral test runner: scripts/run_pipeline_tests.py

Notes

• Keep memory/always_on.md tiny; put detail behind retrieval.
• Use snippet IDs only (S1, S2, etc.) in Context Pack sources.
• Follow references/deterministic_ids.md when deterministic snippet IDs matter.
• Run scripts/run_pipeline_tests.py after changing routing, retrieval caps, snippet IDs, runtime artifacts, derived queries, daily-log triggers, or transcript evidence.
• Run scripts/memory_watchdog.py --require-semantic before claiming semantic memory is healthy.