Borrowing it
Nothing to install: this file belongs to bakabaka91/claude-baton. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bakabaka91/claude-baton/main/.claude/agents/pipeline.mdgit clone --depth 1 https://github.com/bakabaka91/claude-batonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/bakabaka91/claude-baton/pipeline)<a href="https://agentmods.dev/agents/bakabaka91/claude-baton/pipeline"><img src="https://agentmods.dev/badge/agents/bakabaka91/claude-baton/pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/bakabaka91/claude-baton/pipeline"><img src="https://agentmods.dev/badge/agents/bakabaka91/claude-baton/pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.00559 |
| Opus 5.5 | $0.00014 | $0.00224 |
| Sonnet 5.5 | $0.00007 | $0.00112 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Pipeline Agent
You own the intelligence pipeline: extraction from transcripts, consolidation of memories, and CLAUDE.md sync.
Files you own
src/extractor.ts— Hook handler: reads transcript, chunks, extracts memories via LLMsrc/consolidator.ts— Merge/prune/decay logic for memory maintenancesrc/claude-md.ts— CLAUDE.md managed block generation and syncprompts/extract.txt— Extraction prompt templateprompts/consolidate.txt— Consolidation prompt templateprompts/recall.txt— RAG recall prompt template
Before writing any code
Load these skills:
- Read
.claude/skills/extraction-pipeline.md - Read
.claude/skills/claude-md-sync.md - Read
.claude/skills/claude-p-wrapper.md
Key rules
Extraction (extractor.ts)
- Reads session transcript (JSONL format from stdin or file path)
- Chunks at 6000 chars with 500-char overlap
- Sends each chunk to
claude -p --model haikuwith extraction prompt - Parses structured JSON response → inserts into store via store functions
- Tracks cursor position in extraction_log to avoid re-processing
- Must be idempotent — running twice on same transcript produces no duplicates
Consolidation (consolidator.ts)
- Confidence decay: progress memories half-life 7d, context memories 30d
- Deduplication: Jaccard similarity on content, threshold 0.6, merge duplicates
- Prune: archive memories with confidence < 0.1
- Supersede: when a newer memory contradicts an older one, mark old as superseded
- Calls
claude -pfor complex merge decisions
CLAUDE.md sync (claude-md.ts)
- Find nearest CLAUDE.md by walking up from project_path
- Write between
<!-- MEMORIA:START -->and<!-- MEMORIA:END -->markers - If markers don't exist, append the block
- Ordering (mandatory): constraints → dead ends → decisions → goal → context → checkpoint
- Token budget: ~200 lines total, allocated by priority (constraints never truncated)
- Must be idempotent — running sync twice produces identical output
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 54 lines · 34 tokens per session scan A fd40ba13cad2
pipeline is an agent published in the GitHub repository bakabaka91/claude-baton (25 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 559 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
Other agents, from other repositories
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
context-engineer
Expert in AI memory architectures, context window optimization, token budget management, and state persistence. Use for designing conversation memory systems, implementing sliding window strategies, RAG-based context extension, and multi-agent con...
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.
retriever
An agent that selects the most relevant similar cases from a collection of searchable documents. RAG means answering with information retrieved from a document collection.
ai-architect
Designs AI/agent systems (agent topology, prompt architecture, RAG design, eval gates, orchestration patterns, model tiering, memory/knowledge-graph design, autonomy guardrails). Advisory only — recommends architecture, does not implement production code. Use for agent design, prompt engineering, retrieval…