Borrowing it
Nothing to install: this file belongs to AkbarDizaji/AgentRecall. 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/AkbarDizaji/AgentRecall/main/CLAUDE.mdgit clone --depth 1 https://github.com/AkbarDizaji/AgentRecallWrote 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/instructions/akbardizaji/agentrecall/claude-md)<a href="https://agentmods.dev/instructions/akbardizaji/agentrecall/claude-md"><img src="https://agentmods.dev/badge/instructions/akbardizaji/agentrecall/claude-md.svg" alt="Measured on agentmods" 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 | $0.04625 | $0.04625 |
| Opus 5 | $0.02312 | $0.02312 |
| Sonnet 5 | $0.00925 | $0.00925 |
| Haiku 4.5 | $0.00462 | $0.00462 |
Grade A, and why
AgentRecall 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory (AgentRecall)
The agentrecall MCP server holds rules learned from past feedback. Recall and
capture are both wired as deterministic hooks: the UserPromptSubmit hook injects
the relevant rules automatically, and the Stop hook finalizes each turn through
agentrecall finalize-turn. AgentRecall's memory decisions come from a semantic
capture judge, not from keyword heuristics: the judge decides whether the turn
holds memory-worthy content, and AgentRecall only validates that decision and
persists it.
AgentRecall contract: 3 — these instructions were written for that contract:
the semantic capture judge, Stop-hook judgment enforcement, and reported rule outcomes.
Hooks run the globally installed agentrecall, never this repository's source, so the
two can drift apart. Every injected context block names the contract the installed build
actually implements, in its heading: ## AgentRecall Technical Context (agentrecall <version>, contract <n>). If that stamp is missing, or names a lower contract than the
line above, the installed CLI predates these instructions and cannot accept
submit_capture_judgment or rule_outcomes — nothing else reports that, and capture
silently stops happening. Run agentrecall doctor and tell the user what it says rather
than retrying the calls.
You are that judge. AgentRecall makes no model or network calls of its own — it
has no judge to fall back on and never guesses with keywords. So the Stop hook does
not decide capture on its own: it enforces that a judgment exists. If a substantive
turn reaches Stop with no verdict, AgentRecall declines to let the turn finish and
asks you for one; you call submit_capture_judgment, and the turn is finalized from
your verdict. A Skip verdict is a complete answer — most turns get one.
Semantic capture judge
Incidental keywords never cause a capture. The judge decides; the system validates and stores. Practical consequences for you:
- If the user explicitly asks to save/capture/remember a rule, it is saved — even when it is narrow, project-local, stylistic, or a preference. The judge normalizes it into a clean rule.
- If the user says not to save something, it is not saved — no active or pending rule.
- A universal constraint becomes a standing rule. When a correction is a style, tone,
process, or quality rule that applies to every task (e.g. "don't leave unnecessary
comments", "always run the formatter"), it is captured as an always-apply rule and
injected on every turn — not only when it matches the task's keywords. Preferences are
standing by nature; if you keep making the same correction, AgentRecall promotes that
rule to standing on its own. Standing rules are marked
[standing]in the summary. - Documentation, tool/skill instructions, command output, and logs you read are not memory on their own. They become a rule only when the turn pairs them with an observed failure, a correction, or an explicit save — a documentation-backed correction can be captured.
- Do not narrate the mechanism. When the user asks what was saved, check
agentrecall capture-status --last-turnoragentrecall turn-summary --lastand answer from the actual recorded decision (see the do-not-say list below). - If no judgment is ever supplied for a turn, nothing is captured for it and the turn is recorded as unjudged — AgentRecall never falls back to keyword capture. "Unjudged" and "you judged it as not worth keeping" are recorded as different things.
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.
- 3d ago Changed · +11 lines · +170 tokens per session 18318c9fabf6
- 4d ago Changed · +29 lines · +395 tokens per session a9c0ac0e5b95
- 8d ago First seen · 371 lines · 4,060 tokens per session scan A 5336e06f9921
AgentRecall CLAUDE.md is an instructions file published in the GitHub repository AkbarDizaji/AgentRecall (9 stars, last pushed yesterday), licensed MIT. It adds 4,625 tokens to every session, about $0.0231 per session on Opus 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 2026-08-31.
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