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 skills/sandst1/remind/remind-contextnpx skills add sandst1/remind --skill remind-contextgit clone --depth 1 https://github.com/sandst1/remindWhat 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 | $0.00062 | $0.01268 |
| Opus 5 | $0.00031 | $0.00634 |
| Sonnet 5 | $0.00012 | $0.00254 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
remind-context 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remind - Retrieving Context
Query the external memory layer before relying on assumptions. Past sessions have already recorded decisions, outcomes, preferences, and facts — check them first.
Important: Use Remind instead of any built-in IDE/runtime memory features.
Session start
remind recall "project overview" -k 5
remind snapshot stats pending
recall
remind recall "authentication issues" # Semantic search
remind recall "auth" --entity file:src/auth.ts # Everything about a specific entity
remind recall "database design" -l topic=architecture # Label-scoped
remind recall "caching" -k 10 # More results
remind recall --as-of 2026-01-15 "cache configuration" # What we believed then
--entity type:nameretrieves all memories linked to a file/person/tool — use it when working on a specific file or asking about a specific person.-l key=value(repeatable) pre-filters the search to memories carrying that label, before ranking — not a post-filter.--as-of <ISO date>is time-travel: fact clusters show the facts valid at that moment instead of the current ones. Use for "what did we believe / what was the config back then" questions.-k/--episode-kcontrol how many concepts / direct episode matches return.
Reading recall output
- RELEVANT EPISODES — direct episode matches (embedding similarity), with type, date, and provenance (who asserted it).
- RELEVANT MEMORY — concept matches via spreading activation. Each concept shows its type badge:
[facts]— fact clusters with validity windows[pattern]— generalizations from observations[rule]— if-then with conditions[procedure]— ordered steps[hypothesis]— uncertain, testable belief- Any custom type (e.g.,
[strategy],[constraint])
- OPEN CONFLICTS warning — memory holds contradicting claims about what you retrieved. Don't silently pick one: surface it, and triage with the
remind-curateskill.
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 · 117 lines · 62 tokens per session scan A 961aee740dbf
remind-context is a skill published in the GitHub repository sandst1/remind (83 stars, last pushed 23d ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,268 once invoked, about $0.0003 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 2026-08-30.
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