# Dr Context Pipeline > Deterministic memory/context pipeline for agents: route a user message, retrieve relevant memory snippets, compress into a cited Context Pack (sources are snippet IDs), lint, and fall back safely. Prerequisite: a file-based memory layout with memory/always_on.md + topic files (works out-of-the-box with dr-memory-foundation). Use when building or standardizing agent memory, reducing prompt bloat, implementing retrieval+compression, creating a context pack, designing a memory pipeline, adding lint gates, or setting up golden regression tests for agent context. After install, users can simply say: Apply dr-context-pipeline as default behavior. - Skill: `knownasnaffy/dr-context-pipeline-5` (Agent Skill, multi-file: 22 files) - Install (CLI): `npx skillmds@latest add knownasnaffy/dr-context-pipeline-5` - Raw SKILL.md: https://api.skillmd.com/api/skills/knownasnaffy/dr-context-pipeline-5/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: knownasnaffy (https://skillmd.com/u/knownasnaffy) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/knownasnaffy/dr-context-pipeline-5 --- ### Credential Fixture Review `python scripts/_credentials.py` is review material for the `DR Context Pipeline (retrieval + compression + lint)` skill. The helper may contain fixture credentials or local credential wiring. Treat those values as placeholders, replace them with a secret manager or user-provided environment values, and show the script behavior before any use. Do not contact external services with embedded sample credentials. # DR Context Pipeline (retrieval + compression + lint) Use this skill to standardize how an agent loads memory into its prompt **for correctness**. ## Prerequisites - A file-based memory layout that includes `memory/always_on.md` (policy header + topic catalog) and topic files under `memory/topics/`. - Recommended: install **dr-memory-foundation** (or implement an equivalent structure). ## Quick install commands (copy/paste) These commands work from anywhere because they set `$WORKSPACE` (defaults to `~/.openclaw/workspace`). Rerun them whenever you need to re-apply the pipeline: ```bash export WORKSPACE=${WORKSPACE:-~/.openclaw/workspace} cd "$WORKSPACE" clawhub install dr-context-pipeline --version X.Y.Z --dir skills python3 ./skills/dr-context-pipeline/scripts/install_pipeline.py --target "$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 "$WORKSPACE/context_pipeline" python3 ./skills/dr-context-pipeline/scripts/memory_watchdog.py --freshness-minutes 240 --min-bytes 200 git -C "$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 a gap, stop and reply `NOT EXECUTED: `. ## Apply to this workspace When the user asks to **apply** this skill (for example: `Apply dr-context-pipeline as default behavior`), follow this exact order and paste the evidence for every step: All commands assume you’ve set `WORKSPACE=${WORKSPACE:-~/.openclaw/workspace}` and are running from that directory. 1. **Lay down the files** — run `python3 ./skills/dr-context-pipeline/scripts/install_pipeline.py --target context_pipeline` (adds/updates the workspace copy from `assets/context_pipeline/`). Keep the script output in the transcript (hash summary + file count). 2. **Show the tree** — `ls -1 context_pipeline` so the user sees which files are now present. 3. **Patch `AGENTS.md`** — read the file, insert/refresh the “Context Pipeline” instructions, and include a `git diff -U20 AGENTS.md` (or equivalent) snippet in your reply. Preserve everything else. 4. **Validate** — run `python3 ./skills/dr-context-pipeline/scripts/validate_pipeline.py --context-root context_pipeline` and paste the PASS/FAIL summary. If it fails, stop and report `NOT EXECUTED` with the error. 5. **Run the memory watchdog** — `python3 ./skills/dr-context-pipeline/scripts/memory_watchdog.py --freshness-minutes 240 --min-bytes 200` (tune as needed). Paste the JSON output; if status ≠ OK, stop and reply `NOT EXECUTED: memory gap` after quoting the issues. 6. **Final state** — show `git status -sb context_pipeline AGENTS.md` (or `git status -sb` if cleaner) so the user can see what changed. 7. **Success banner** — `echo "CONTEXT PIPELINE APPLY COMPLETE"` so the transcript clearly shows the run finished cleanly. This apply flow must be idempotent: if the files already match and `AGENTS.md` already contains the section, the diff should be empty but you still run the installer + validator and show their outputs. ## Memory commit / continue workflow - When Daniel says “memorize this” (or similar), run the checklist in `references/MEMORY_COMMIT.md` (daily log, now, open-loops, topic file, MEMORY.md) and confirm which files changed. - When he says “let’s continue” after a reset, reload `memory/now.md`, `open-loops`, and the relevant topic files so you can summarize where things left off before acting. ## Runtime evidence contract (non-optional) Every single task must follow the Runtime Evidence Checklist in `references/RUNTIME_CHECKLIST.md`: - Emit the **Retrieval Bundle JSON** that conforms to `references/schemas/retrieval_bundle.schema.json`. - Emit the **Context Pack JSON** that conforms to `references/schemas/context_pack.schema.json` (or explicitly state the lint failure and fall back to raw snippets). - List which snippet IDs are being passed forward. - Only then provide the user-facing reasoning/result. - If any step fails, reply `NOT EXECUTED: ` and stop. Casual prompts (“go for it”, “review it”, etc.) do **not** suspend this contract. If the user wants a lighter interaction they can explicitly waive it. ## Operating procedure (default) 1) Load the always-on policy + topic catalog (your `memory/always_on.md`). 2) Route the message deterministically (task type + caps) using `references/router.yml`. 3) Retrieve top relevant snippets from your memory store; emit a **Retrieval Bundle JSON** (see schema). 4) Compress Retrieval Bundle → **Context Pack JSON** using `references/compressor_prompt.txt`. - **IMPORTANT:** Context Pack `sources` MUST be **snippet IDs only** (`S1`, `S2`, …). 5) Lint the Context Pack. If lint fails, **skip compression** and fall back to raw retrieved snippets. 6) Call the main reasoning model with: always-on policy header + Context Pack (+ raw snippets for high-stakes tasks) + user message. ## 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` - Runtime checklist: `references/RUNTIME_CHECKLIST.md` - Golden tests starter suite: `references/tests/golden.json` - Installer/validator/watchdog scripts: `scripts/install_pipeline.py`, `scripts/validate_pipeline.py`, `scripts/memory_watchdog.py` ## Notes - Keep “always-on policy header” tiny (invariants only). Put everything else behind retrieval. - If you need deterministic snippet IDs, follow the stable ordering guidance in `references/deterministic_ids.md`.