What is causal-memory CLAUDE.md?

A set of instructions for connecting coding agents to causal memory, a system that records past decisions and what happened afterward.

It helps search past decisions, assess risks, and save whether an architecture, debugging, library, or deployment choice led to a particular result.

It encourages agents to look up relevant lessons before difficult decisions and record outcomes afterward, reducing repeated mistakes across sessions.

How JingxuanC/causal-memory configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to JingxuanC/causal-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/JingxuanC/causal-memory/main/CLAUDE.md

Clone the repo

git clone --depth 1 https://github.com/JingxuanC/causal-memory

Made for: Claude Code.

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README.md

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Per session 1,063 This file is loaded in full into every session.

When invoked 1,063 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01063	$0.01063
Opus 5.5	$0.00425	$0.00425
Sonnet 5.5	$0.00213	$0.00213
Haiku 4.5	$0.00106	$0.00106

Measured 2d ago against content hash 5b9c94165d05, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

causal-memory CLAUDE.md 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 2d 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.

CLAUDE.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Causal Memory MCP Integration

Paste this into your CLAUDE.md / AGENTS.md / system prompt to activate causal memory.

Per insights/13 §1.3: agents don't proactively call memory tools without instruction. This prompt forces proactive use.

Causal Memory Integration

You have access to a causal memory layer via MCP tools. This records your past decisions and their outcomes, so you can learn from experience across sessions.

Before any non-trivial decision (architecture choice, debugging approach,

library selection, deployment strategy):

Call search_memory with your query — it searches facts AND causal lessons at once (RRF-fused). If you know you need causal lessons specifically, call search_causal with the relevant task_tag
For risky or irreversible actions, also call intervention_query to see what outcomes similar past actions caused (safe / warning / danger)
When choosing between two concrete options, call counterfactual_query with both option texts — it compares recorded outcomes and shows same-context branches (natural experiments) when they exist. It logs a falsifiable prediction that resolves later when a new record_decision under the same task_tag carries matching option text — comparing two options that are already recorded leaves the prediction pending by design. A pending (or empty) ledger is not a broken one.
If past experience is relevant, incorporate it into your approach

After acting on a decision and observing the result:

Call record_decision with:
- decision: what you decided
- outcome: what actually happened
- relation: caused / enabled / prevented / no_effect
- task_tag: the task category
- confidence_source: temporal / rule / llm_inferred / user_feedback
- context (important): a short description of the situation the decision was made in (environment, constraints, key parameters). Decisions with the same task_tag + context become comparable branches — this is what powers same-context counterfactual evidence. If you weighed multiple options at this decision point, ALWAYS record the context.

Read the full file on GitHub · 95 lines

Changes

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.

2d ago Changed · +28 lines · +383 tokens per session 5b9c94165d05
2mo ago Changed · +14 lines · +169 tokens per session 35c266b58309
2mo ago First seen · 53 lines · 511 tokens per session scan A 2f7e24fe192e

Subscribe to this mod's changes

causal-memory CLAUDE.md is an instructions file published in the GitHub repository JingxuanC/causal-memory (104 stars, last pushed 2d ago), licensed Apache-2.0. It adds 1,063 tokens to every session, about $0.0043 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 2026-08-30.

Memory and context agent-memory causal-inference deepseek-harness dsh dsh-plugin llm-agents

causal-memory CLAUDE.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
causal-memory CLAUDE.mdJingxuanC/causal-memory	Instructions file	104	2d ago	Apache-2.0	A	1,063	Claude Code
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

causal-memory: Instructions file for Claude Code