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Batch Cognition

作者 KairoKid · GitHub ↗ · v1.1.0 · MIT-0
cross-platform ⚠ suspicious
112
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当前安装
2
版本数
在 OpenClaw 中安装
/install batch-cognition
功能描述
Process bulk prompt batches with alternating play/think cognitive loops. Use when user says "batch incoming", "multiple prompts incoming", "corpus incoming",...
使用说明 (SKILL.md)

Batch Cognition

Process bulk prompts with stop-start play/think cycles. Save first, lose nothing, discover value.

Activation

User signals: "batch incoming" / "multiple prompts incoming" / "corpus incoming"

Respond: 🔁 BATCH MODE — send them. I'll save everything first, then process one-by-one.

Step 1: SAVE (mandatory, before any processing)

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."

Step 1.5: PRE-SCAN & CLASSIFY (per item, before 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.

Step 2: PLAY (per prompt)

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):

  • Website/app: hours × $0 (we build) + API costs + hosting. Estimate tokens for AI-assisted build.
  • Script/tool: lines of code estimate → token estimate (1 LOC ≈ 10-20 tokens to generate)
  • Research: number of searches + fetches × ~500 tokens each
  • Total = build tokens + test tokens + fix tokens (budget 30% extra for iteration)

"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.

Step 3: THINK (per prompt, immediately after PLAY)

Answer 5 questions (keep tight, 1-2 lines each):

  1. Learned: factual takeaway
  2. Pertinent: relevant to user's current projects/goals?
  3. Value: creative thinking → value creation → money path?
  4. Act?: yes/no — if yes, smallest next step
  5. Future: park / discard / investigate deeper

Tag: 🟢 ACT NOW | 🟡 PARK | 🔴 DISCARD | 🔵 INVESTIGATE

Update status to [x] DONE with tag.

Step 4: CHECKPOINT (every 5 prompts)

Brief summary: what's covered, patterns emerging, top value items so far. Ask: "Continue, pause, or pivot?" — if no response in 30s, continue.

Step 5: META-THINK (on "done" or batch exhausted)

Review all Think notes. Produce:

  1. Value Stack — ranked by expected value
  2. Patterns — themes, connections
  3. Action Items — concrete next steps
  4. Park List — not now but maybe later
  5. Discard List — safe to ignore (1-line reason each)

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.

Commands (user can say anytime)

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

Context Management

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:

  1. Score all items: salience *= 0.8, re-referenced items → 1.0
  2. Drop items below 0.2 threshold (write tombstone, archive to disk)
  3. Carry forward: rolling summary + surviving high-salience items + value stack
  4. New block header written to chain file

Cross-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.

Self-Improvement

After each batch, append to workspace/systems/batch-cognition/learnings.md:

  • What worked / what was wasted / grouping improvements / play-think split effectiveness
  • Update this skill if any rule changes warranted.

Key Rules

  • SAVE FIRST — never process before saving full batch to file
  • Never lose input — if Telegram truncates or splits, wait for "done" before processing
  • Play and Think are separate — don't blend execution with inference
  • Notes are mandatory — both Play notes and Think notes, every prompt
  • Prompts aren't always instructions — some are ideas, half-thoughts, value discovery. THINK phase handles these.
  • PLAY means EXECUTE, not summarize — if it says build, build. If it says research, research with sources. If it's an idea, scope it with real numbers.
  • First pass is never final — flag items that need deeper work. A 🟢 tag without execution is just a bookmark.
  • Prototype cost is mandatory for buildable ideas — hours, tokens, API costs, hosting. Not vibes.
安全使用建议
This skill appears to implement the batch processing it describes, but it will save every prompt to disk and may perform network research and access Google Drive without declaring credentials. Before installing or invoking: 1) Confirm how your agent/platform handles Drive access and OAuth — the skill assumes Drive navigation but does not declare required creds. 2) Expect persistent storage of all batch inputs under workspace/systems/batch-cognition; review and purge those files if prompts contain secrets. 3) Be cautious running untrusted batches because 'EXECUTE' plus autonomous continuation can cause broad activity (web fetches, artifact generation). 4) Ask the skill author to document what external APIs/capabilities are required and to add safeguards (explicit opt-in for network/Drive access, limits on token budgets, and a retention/erase policy). If you need guarantees about privacy or limited external access, do not run this skill until those clarifications are provided.
能力评估
Purpose & Capability
Name/description (bulk prompt processing with play/think cycles) lines up with the SKILL.md: it saves batches, processes items, checkpoints, and writes chain/summary files. However, the skill explicitly references Google Drive GUI navigation and web research/citation requirements while declaring no required credentials or environment variables — this reliance on unspecified external access is a mismatch worth noting.
Instruction Scope
The runtime instructions tell the agent to parse and save entire batches to workspace/systems/..., read prior batch artifacts, perform per-item 'EXECUTE' actions (including 'research' and generating artifacts), and fetch/cite sources. 'Execute the prompt' can mean performing arbitrary actions implied by user prompts; there are no explicit limits or safeguards. The skill also autonomously continues after 30s of silence. These behaviors expand the agent's scope to file I/O and network activity and could cause unintended data retention or external requests.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest install risk. Nothing is downloaded or installed by the skill itself.
Credentials
The skill requests no environment variables or credentials, which is generally proportional. But it expects Drive access and web research (network I/O) without declaring required credentials/APIs; that implicit dependency should be clarified. It writes persistent files under workspace/systems/batch-cognition, meaning local storage access is required.
Persistence & Privilege
The skill writes multiple persistent artifacts (batch docs, chain blocks, value-stack, parked/discarded lists) under workspace/systems/batch-cognition. always:false (not forced into every agent run) is appropriate, but the persistent on-disk storage of all user prompts — including potentially sensitive content — is a notable persistence behavior the user should be aware of.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install batch-cognition
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /batch-cognition 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.1.0
Improved descriptions, clearer activation triggers, better PLAY/THINK documentation. Not just AI text — battle-tested on real prompt dumps.
v1.0.0
Initial release of batch-cognition skill. - Enables structured processing of bulk prompt batches using alternating play/think cycles. - Automatically saves all input before processing to prevent data loss. - Classifies each prompt by type and assigns appropriate processing depth. - Separates execution (PLAY) and inference (THINK) phases for every item. - Provides value discovery, checkpointing, meta-analysis, and rolling decay memory for batch context management. - Supports batch commands (skip, park, pause, status, etc.) and self-improving documentation after each run.
元数据
Slug batch-cognition
版本 1.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Batch Cognition 是什么?

Process bulk prompt batches with alternating play/think cognitive loops. Use when user says "batch incoming", "multiple prompts incoming", "corpus incoming",... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 112 次。

如何安装 Batch Cognition?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install batch-cognition」即可一键安装,无需额外配置。

Batch Cognition 是免费的吗?

是的,Batch Cognition 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Batch Cognition 支持哪些平台?

Batch Cognition 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Batch Cognition?

由 KairoKid(@dodge1218)开发并维护,当前版本 v1.1.0。

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