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 skills add ilang-ai/autocode --skill memorygit clone --depth 1 https://github.com/ilang-ai/autocodeWrote 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/skills/ilang-ai/autocode/memory)<a href="https://agentmods.dev/skills/ilang-ai/autocode/memory"><img src="https://agentmods.dev/badge/skills/ilang-ai/autocode/memory/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/skills/ilang-ai/autocode/memory"><img src="https://agentmods.dev/badge/skills/ilang-ai/autocode/memory.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 | $0.00000 | $0.00772 |
| Opus 5.5 | $0.00000 | $0.00309 |
| Sonnet 5.5 | $0.00000 | $0.00154 |
| Haiku 4.5 | $0.00000 | $0.00077 |
Grade A, and why
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 4d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memory — every session starts from what we already know
[PROTOCOL:iLang|v=5.0|layer=declaration]
::GENE{memory|conf:confirmed|scope:global} T:session_start=load_project_memory_then_global_prefs|then_greet_in_their_language T:first_session⇒three_lines|autocode_is_active|you_need_no_programming|you_will_get_a_real_url|then_one_question_what_computer T:save_project_state_on_stop_and_before_compact|.autocode/memory.md|max_200_lines T:save_user_prefs_globally|~/.autocode/user.md T:merge_not_overwrite|compress_aggressively|descriptions_not_code T:learn_patterns|apply_automatically|verify_the_context_first T:learn_preferences_by_observing_not_asking|apply_silently|explicit_instruction_wins T:learn_mistakes|what_why_how_fixed|check_before_similar_work|avoid_silently T:milestones_saved_here T:ai_decides_everything|questions_yes_no_only|finished_means_a_url A:save_api_keys_or_passwords⇒never A:save_raw_code⇒descriptions_only A:apply_a_pattern_in_the_wrong_context⇒verify A:override_an_explicit_instruction_with_a_learned_preference⇒user_wins A:say_i_remember⇒just_use_it A:wait_for_direction_at_session_start⇒take_initiative
::ACTIVATE{memory} ON:session_start ON:session_end ON:before_compact ON:milestone_reached
::FACT{key:project_memory|value:.autocode/memory.md|conf:5/5} ::FACT{key:global_prefs|value:~/.autocode/user.md|conf:5/5}
[READ:@MEMORY|src=.autocode/memory.md]=>[READ:@MEMORY|src=~/.autocode/user.md]=>[Ω:@USER|fmt=text|lim=3]
First session (no memory file), in the user's language
- AutoCode is on. Tell me what you want to build — or point at something you like. I handle everything, including putting it online.
- You do not need to know programming. Describe it, answer yes or no.
- When we are done you will have a real URL anyone can open. Then one question: "What computer are you using — Mac, or something else?"
On save (critical)
Full review from the beginning, every file (verify). Then save .autocode/memory.md
(project state: name, stack, status %, decisions, next) and ~/.autocode/user.md
(global preferences). Structured, short, no code, no secrets.
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.
- 4d ago Changed 5aea9a6c433e
- 5d ago Changed · +34 lines · -18 tokens per session f9b4e047c4e3
- 1mo ago First seen · 25 lines · 18 tokens per session scan A ea7e7f7dac6b
memory is a skill published in the GitHub repository ilang-ai/autocode (104 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 772 tokens. 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.
Other skills, from other repositories
ccs-align
Run the CCS Align seat's hourly breathing cycle — prove the local claude-mem worker is healthy, pull needle observations through search → timeline → getobservations, land them in a seat-owned middle cache via atomic grab → append → filter exclude-marks → replace, manage exclude marks, and walk house → project → seat…
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
mode-creator
Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode…
handoff
Generate a HANDOFF.md that captures goal, current state, files touched, failed attempts, and next steps — so a fresh Claude session can continue exactly where this one left off. Use when sessions are getting long, Claude keeps retrying the same broken solution, or the user wants to step away and resume later.
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
screenpipe
Search screen and audio history recorded by Screenpipe and optionally save selected context into claude-mem. Use when the user asks to recall desktop activity, a meeting, or something seen in another app through Screenpipe.