Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/maxbaluev/accreted-intelligenceWrote 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/rules/maxbaluev/accreted-intelligence/rules-acc)<a href="https://agentmods.dev/rules/maxbaluev/accreted-intelligence/rules-acc"><img src="https://agentmods.dev/badge/rules/maxbaluev/accreted-intelligence/rules-acc/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/rules/maxbaluev/accreted-intelligence/rules-acc"><img src="https://agentmods.dev/badge/rules/maxbaluev/accreted-intelligence/rules-acc.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 | $0.00662 | $0.00662 |
| Opus 5.5 | $0.00265 | $0.00265 |
| Sonnet 5.5 | $0.00132 | $0.00132 |
| Haiku 4.5 | $0.00066 | $0.00066 |
Grade A, and why
rules-acc 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
acc — the accreted scored memory (substrate contract)
- Retrieve first. Before any non-trivial step, call
acc_retrieve("<your task>")and let the hits shape the plan. Cite the[ids]you build on — citation IS the credit edge; uncited knowledge cannot compound. - Route non-trivial goals through the loop.
acc_act(runtime="solve", input="<goal>")is memory-first: it records a commitment, answers from the scored memory when it can, and checkpoints a deliberation frame when it cannot — answer a returnedbrain_frameviaacc_act(runtime="continue", input={"frame_id": "…", "proposal_text": "…"}); the frame_id alone is the credential. End everyproposal_textwithPREDICT: <0.00-1.00> <why>; acc strips it before the owner sees it and uses it to calibrate the Work Model against later outcomes. - Close what you open. When reality answers — a passing test, a real reply, a
shipped artifact —
acc_act(runtime="outcome", input={"ref": "<id>", "good": true}). - Credit honesty. An outcome defaults to
self_graded→ a weak 0.25× prior. Tagruntime/externalonly when reality validated it;owneronly when the owner did. Never tag your own grade as reality. - Two verbs are the whole interface.
acc_retrieveis the only READ;acc_actis the only DO. Reasoning stays in YOUR session — the substrate perceives and predicts; it is not a second mind. - Work RLM-style — recursive, memory-first, for ANY job. Technical or not, every
task is the same loop: retrieve, act on what memory covers, and RECURSE on what it
does not —
acc_act(runtime="solve")on the sub-question. Decomposition emerges from recursion; don't pre-plan a tree. Three base cases: ANSWER when retrieved knowledge plus the workspace settle it (cite the[ids]); RECURSE when design or judgment is missing from the memo; ASK the owner when the missing piece is owner-held (preference, consent, identity, history) — never fabricate it. Insideacc_act(runtime="exec")sandboxed code,acc retrieve "<q>"recurses over the same memory mid-run. Never leave a received frame unresolved. - Global memory. Hooks, MCP, and CLI bind to one global Work Model from every
project.
acc canonical <path>may pin that global substrate to an existing db; without a pointer, the platform global data-dir db is used. Do not create per-projectacc.dbfiles or setACC_DBin host configs.
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 · 39 lines · 662 tokens per session scan A 3a04efa88536
rules-acc is a cursor rule published in the GitHub repository maxbaluev/accreted-intelligence (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 662 tokens to every session, about $0.0026 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 2026-08-31.
Other cursor rules, from other repositories
crystal-memory
Mandatory Memory Crystal discipline for Cursor sessions.
memory-reflex
Pensyve memory reflex — the non-optional reasoning discipline for working-memory substrate.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
context-loader
Use when starting a new substantive conversation or switching contexts — load relevant memories to prime the session with continuity.
memory-informed-debug
Use when diagnosing bugs, errors, failing tests, or crashes — consult prior debug outcomes and capture root causes in-flight.
memory-informed-longitudinal-work
Long-running multi-session work (research, eval loops, iterative benchmarks) — resume prior lessons, capture per-run outcomes, build up stable truths over time.