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.
npx agentmods add rules/ag-dmitry/composer-enhanced/working-memorygit clone --depth 1 https://github.com/AG-Dmitry/composer-enhancedWhat 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 | $0.00026 | $0.00662 |
| Opus 5 | $0.00013 | $0.00331 |
| Sonnet 5 | $0.00005 | $0.00132 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
working-memory 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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working Memory
Why
A cheap model has a finite context window. When it fills up, early decisions (the "why" of a design choice) quietly evaporate. Later in the same task, the model re-examines those choices from scratch — and may reverse them inconsistently, creating the flip-flop / oscillation failure mode.
The working-memory file acts as durable, structured external state. It survives context compaction and model resets. Writing to it forces the model to distil its decisions explicitly, which itself improves quality (externalizing = understanding).
File
One file per task at .cursor/agent-memory/<task-slug>.md.
- Template:
templates/task-memory.md. - Not committed to git — add
.cursor/agent-memory/to.gitignore. - Lifecycle: create in phase 1, update throughout, discard or archive when done.
Sections
| Section | When to fill |
|---|---|
| Goal | Phase 1 |
| Success criteria | Phase 1 |
| Non-goals | Phase 1 |
| Complexity | Phase 1 |
| Open questions | Phase 1 + updated continuously |
| File ledger | Phase 2 (path → summary + relevant line ranges) |
| Decision log | Phase 3 (chosen approach + rejected alternatives) |
| Iteration log | Phase 4-7 (what changed + gate outcomes) |
| Red-team findings | Phase 5 |
| Self-report | Phase 6 on pass |
Rules
- Distil, do not dump. Write the key decision, not the full reasoning chain.
- Always update before a context boundary (compaction, end of chat, model reset).
- Before the iteration-3 re-plan checkpoint, re-read the entire file to restore full context.
- Use the git drift check (below) to verify your file-ledger is still accurate before trusting it in a retry.
- Context hygiene beats volume. A working-memory file that forces you to distil is more valuable than pasting raw output. Keep the live context window lean — prune irrelevant history and long raw outputs; summarize; send only high-signal tokens. Context rot (window stuffed with low-signal content) degrades a cheap model's attention faster than token-count alone suggests.
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 First seen · 71 lines · 26 tokens per session scan A 33db1a6ee310
working-memory is a cursor rule published in the GitHub repository AG-Dmitry/composer-enhanced (3 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 662 once invoked, about $0.0001 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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