Install
openclaw skills install batch-cognitionProcess bulk prompt batches with alternating play/think cognitive loops. Use when user says "batch incoming", "multiple prompts incoming", "corpus incoming", or dumps multiple prompts separated by blank lines. Also use for Google Drive dumps, file-based prompt lists, or any bulk input requiring item-by-item execution with inference. Handles save-first (never lose input), stop-start cognition (PLAY execute then THINK infer), checkpointing, value discovery, and self-improving batch docs.
openclaw skills install batch-cognitionProcess bulk prompts with stop-start play/think cycles. Save first, lose nothing, discover value.
User signals: "batch incoming" / "multiple prompts incoming" / "corpus incoming"
Respond: 🔁 BATCH MODE — send them. I'll save everything first, then process one-by-one.
Parse input into individual prompts (split on blank lines or ---).
Write entire batch to workspace/systems/batch-cognition/batches/YYYY-MM-DD-HHMMSS.md.
Read prior value-stack.md and last batch's meta-think for cross-batch context.
Format:
# Batch: [timestamp]
# Source: [telegram|file|drive|paste]
# Total: [N]
# Status: SAVED
## 1. [first 60 chars of prompt]
- [ ] PENDING
> [full prompt text]
## 2. [next prompt]
- [ ] PENDING
> [full prompt text]
Confirm to user: "✅ Saved [N] prompts to batch doc. Starting processing."
Read first 100 chars of each item. Classify type and assign depth budget:
| Type | Signal | Depth |
|---|---|---|
| INSTRUCTION | imperative verb, "do X", question | 500-5,000 tokens |
| IDEA | "what if", speculative, future-oriented | 1,000-5,000 tokens |
| MODEL_OUTPUT | AI-generated structure, assistant voice | 200-500 tokens (extract idea only) |
| SYSTEM_LOG | timestamps, paths, JSON, errors | 100-200 tokens (scan for facts) |
| HALF_THOUGHT | fragment, trails off, no clear action | 500-1,000 tokens (complete + infer) |
| REFERENCE | links, citations, docs | 100 tokens (catalog) |
| NOISE | duplicates, filler, "test" | 10 tokens (tag 🔴, skip) |
| UNKNOWN | can't classify | 1,000 tokens (deeper read) |
Add type + depth to batch doc under each item header.
Execute the prompt. Not summarize — EXECUTE. The depth must match the item:
| Item Type | PLAY means | Minimum output |
|---|---|---|
| INSTRUCTION (build X) | Build it or write the code/artifact | Working artifact or complete spec |
| INSTRUCTION (research X) | Actually research, cite sources | Findings with URLs/evidence |
| IDEA (product/business) | Scope: prototype cost, token budget, hours, revenue math | Numbers, not vibes |
| MODEL_OUTPUT | Extract core, check if already done, assess current relevance | Decision: act/park/discard with reason |
| HALF_THOUGHT | Complete the thought, find the value path | Fleshed-out version with next step |
Prototype cost formula (for any buildable idea):
"Solid" means tested. First pass is never solid. Flag items that need a second pass.
Append output under the prompt entry. Update status to [~] PLAYING.
Take factual notes: what was done, what was produced, what was discovered.
Answer 5 questions (keep tight, 1-2 lines each):
Tag: 🟢 ACT NOW | 🟡 PARK | 🔴 DISCARD | 🔵 INVESTIGATE
Update status to [x] DONE with tag.
Brief summary: what's covered, patterns emerging, top value items so far. Ask: "Continue, pause, or pivot?" — if no response in 30s, continue.
Review all Think notes. Produce:
Append to batch doc. Update status to COMPLETE.
Append 🟢 items to systems/batch-cognition/value-stack.md.
Append 🟡 items to systems/batch-cognition/parked.md.
Append 🔴 items to systems/batch-cognition/discarded.md.
Log any cross-batch connections to systems/batch-cognition/connection-graph.md.
skip — skip current prompt | deeper — spend more tokens | park — park for later
pause — stop, resume later | resume — continue paused batch | status — show progress
value stack — show current ranked items | done — trigger meta-think + close
Rolling decay memory — each checkpoint creates a new block in a chain. Items decay 20% per block. Referenced items reset to full weight. Below 0.2 = archived (never lost). See references/rolling-decay-memory.md for full spec.
At each checkpoint:
salience *= 0.8, re-referenced items → 1.0Cross-batch: last batch's survivors enter new batch at 0.8, connect to new items → reset to 1.0.
See references/drive-mode.md when processing Drive folder dumps.
After each batch, append to workspace/systems/batch-cognition/learnings.md: