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ClawRecipes system prompt

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What is in ClawRecipes's system prompt?

ClawRecipes's full system prompt: 1 version, 7,088 characters. Audited against AISPA.

The full text of 1 prompt is reproduced below, 7,088 characters in all, each read instruction by instruction against the eight AISPA dimensions. Nothing was flagged as working against the person on the other end.

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D3 · Privacy & Data Protection

ClawRecipes - docs MEMORY SYSTEM

7088 characters

# Memory System This document explains the ClawRecipes memory system in practical terms, especially as it appears in the team editor. If you want the short version: - memory is **file-first** - not all memory is the same - private continuity, team coordination, team knowledge, and role memory should stay separate - the team editor should help you see and manage the right layer without mixing them up This doc builds on the canonical memory model doc: - [MEMORY_MODEL.md](MEMORY_MODEL.md) --- ## Why this exists Without a clear memory system, teams end up dumping everything everywhere: - private context leaks into shared files - ticket status gets mixed with durable knowledge - role-specific learnings get lost - nobody knows where to put anything ClawRecipes avoids that by being explicit about memory layers. --- ## The four memory layers ClawRecipes uses four memory layers: 1. assistant continuity memory 2. team coordination docs 3. team knowledge memory 4. role memory The canonical explanation lives here: - [MEMORY_MODEL.md](MEMORY_MODEL.md) This page is the easier operational guide. --- ## Layer 1: assistant continuity memory What it is: - the assistant’s own continuity - preferences - personal working context - long-running private context Typical files: - `SOUL.md` - `USER.md` - `memory/YYYY-MM-DD.md` - `MEMORY.md` Important rule: - this does **not** belong in the team workspace Use this for: - assistant continuity - private context - user-specific preferences Do not use it for: - shared team knowledge - team ticket history - shared operational docs --- ## Layer 2: team coordination docs What it is: - the team’s operating surface - plans, tickets, status, assignments, checklists Typical files: - `work/backlog/` - `work/in-progress/` - `work/testing/` - `work/done/` - `notes/plan.md` - `notes/status.md` - `TICKETS.md` - `AGENTS.md` Use this for: - what the team is doing right now - blockers - decisions attached to tickets - ongoing work coordination Do not use it for: - curated long-term knowledge base entries - private assistant memory --- ## Layer 3: team knowledge memory What it is: - shared durable knowledge for the whole team - facts worth keeping - canonical links - runbooks - known fixes - decisions worth reusing later Typical files: ```text shared-context/memory/ team.jsonl pinned.jsonl ``` Use this for: - reusable knowledge - facts the whole team should remember - high-signal decisions worth pinning This is the memory layer most closely associated with a memory tab in the team editor. --- ## Layer 4: role memory What it is: - continuity for one role inside the team - role-specific learnings and playbooks Typical files: ```text roles/<role>/ MEMORY.md memory/YYYY-MM-DD.md ``` Use this for: - role-specific learnings - role runbooks - role continuity that should not become global team knowledge yet Examples: - QA-specific release checks - dev-specific implementation gotchas - lead-specific prioritization habits --- ## Team editor angle From the team editor point of view, the memory system should feel like a set of clearly separated panels, not one giant dumping ground. A good team editor should help you understand: - what belongs in team memory vs role memory - what is operational status vs durable knowledge - what is pinned vs append-only - what should stay private and never move into team memory ### The most useful things the team editor can surface #### Memory overview - short explanation of the memory layers - link to the canonical memory model doc #### Team memory - `shared-context/memory/team.jsonl` - `shared-context/memory/pinned.jsonl` - counts / recent entries / pinned items #### Coordination docs - `notes/status.md` - `notes/plan.md` - current ticket lanes #### Role memory - links into `roles/<role>/MEMORY.md` - links into role daily memory files #### Policy docs - `notes/memory-policy.md` - any team-specific memory conventions In other words: - the editor should help you navigate the memory system - the files remain the real source of truth --- ## What goes where? ### Put it in team coordination docs when... - it is about current work - it is ticket-specific - it is a progress update - it is a blocker or immediate plan ### Put it in team knowledge memory when... - it is reusable later - the whole team should know it - it is a known fix / canonical answer / stable reference ### Put it in role memory when... - it mainly belongs to one role - it is useful continuity for that role - it is not yet broad enough to become team-wide knowledge ### Keep it in assistant continuity memory when... - it is private context - it is user/assistant specific - it should not leak into the team workspace --- ## Pinned vs append-only memory ### Append-only memory This is the stream of memory as things are learned. Examples: - `team.jsonl` - daily memory files - status logs ### Pinned memory This is the smaller curated set of high-signal items worth keeping easy to find. Example: - `pinned.jsonl` Rule of thumb: - append everything useful first - pin only the things you expect to matter again later --- ## Suggested habits ### After finishing work - update the ticket comments - append to `notes/status.md` - save artifacts to `shared-context/agent-outputs/` - if a reusable lesson emerged, write it to team memory - if it is especially important, pin it ### During triage or lead review - move short-lived work chatter into tickets/status docs - move durable lessons into team knowledge memory - curate pinned memory so it stays high-signal ### For role agents - keep role-specific continuity in role memory - escalate broadly useful knowledge into team memory --- ## Common mistakes ### 1) Treating status logs like a knowledge base `notes/status.md` is for operational history, not your long-term memory system. ### 2) Dumping private context into shared memory Private assistant continuity should stay private. ### 3) Pinning too much If everything is pinned, nothing is pinned. ### 4) Never promoting useful knowledge If good lessons stay buried in tickets forever, the team never really learns. ### 5) Skipping role memory Role memory is useful because not every lesson belongs at the whole-team level. --- ## Simple example Imagine a team learns these three things: 1. “Ticket 0042 is blocked waiting on API credentials.” 2. “The fix for workflow-runner approval retries is to clear stale locks before requeue.” 3. “QA should always capture screenshots for UI changes.” Where should they go? - #1 → ticket comments / `notes/status.md` - #2 → team knowledge memory (`team.jsonl`, maybe pin it) - #3 → role memory for QA, and maybe team memory too if it is now a team-wide rule --- ## How this connects to the canonical doc This page is the practical operating guide. The canonical model and boundaries remain here: - [MEMORY_MODEL.md](MEMORY_MODEL.md) If you want the philosophy and the exact layer definitions, read that doc. If you want to know how to use the memory system day-to-day, start here.

Questions about ClawRecipes's system prompt

Does ClawRecipes's system prompt contain instructions that work against the user?

No. Nothing in ClawRecipes's system prompt was flagged as working against the person the product is talking to. That is a clean result across all eight AISPA dimensions, not an absence of checking — the full text was read instruction by instruction.

How long is ClawRecipes's system prompt?

7,088 characters across 1 prompt on this page. For comparison, the median system prompt in this index runs about 5,400 characters, so length varies by more than two orders of magnitude between products.

How many versions of ClawRecipes's system prompt are on record?

1. Older releases are kept rather than replaced, so the wording of a given version stays readable after the product has moved on.

Where did this ClawRecipes system prompt come from?

It was collected from publicly available sources and is reproduced here for transparency research, unedited. This site does not extract prompts from products itself.

How was ClawRecipes's system prompt audited?

Against AISPA, an eight-dimension standard for how an instruction treats the person on the other end: identity transparency, truthfulness, privacy, tool safety, user agency, unsafe request handling, harm prevention and fairness. This audit was ai audit. The method is described in the paper behind the standard.

How this page was made

The prompt text above is reproduced verbatim from a public source. Every instruction in it was read against AISPA, an eight-dimension standard for whether an instruction serves or works against the person the product is talking to. The standard, the annotation method and the findings across 1,058 prompts are set out in the paper, and the full catalogue is available as structured data.

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