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 vishaltorc/subconscious-mcp --skill subconscious-memorygit clone --depth 1 https://github.com/vishaltorc/subconscious-mcpWrote 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/vishaltorc/subconscious-mcp/subconscious-memory)<a href="https://agentmods.dev/skills/vishaltorc/subconscious-mcp/subconscious-memory"><img src="https://agentmods.dev/badge/skills/vishaltorc/subconscious-mcp/subconscious-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/vishaltorc/subconscious-mcp/subconscious-memory"><img src="https://agentmods.dev/badge/skills/vishaltorc/subconscious-mcp/subconscious-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 | $0.00070 | $0.00679 |
| Opus 5.5 | $0.00028 | $0.00272 |
| Sonnet 5.5 | $0.00014 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
subconscious-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 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subconscious memory discipline
Local semantic memory over MCP. The borderline cases below are where the value lives: a careless recall reuses a stale answer; a careless skip re-does work.
Recall before non-trivial work
Before research, multi-file edits, or repeating a workflow, call recall with
a short task description (not the full user message).
recall(task="deploy Next.js to Vercel production", threshold=0.85)
Then branch on the outcome:
- Hit at >= 0.88: reuse
answerafter a quick sanity check (deps, paths, dates still apply). - Hit below 0.88: treat as borderline. Verify the answer actually fits this task before acting on it; the phrasing matched but the intent may not.
- Miss, best similarity > 0.7: do NOT just lower the threshold. Call
echowith the same task first. If the nearest entry is the same task family, recall it explicitly (or lower the threshold deliberately); if it is a different family, work fresh. - Miss, best similarity <= 0.7: uncharted; do the work normally.
Remember after a reusable outcome
After producing something reusable (commands, config location, fix pattern):
remember(task="...", answer="...", tags=["project-name", "topic"])
Skip secrets, tokens, personal data, and answers that depend on today's file state.
Responding to a near_duplicate warning
If remember returns warning="near_duplicate", a close curated entry
already exists (similarity in [0.75, 0.92]). Do not blindly add a second
copy. Recall or inspect nearest_entry_id, compare it to what you were about
to store, then decide:
- Merge / update: if it is the same task,
forget(nearest_entry_id)and re-rememberthe better-phrased combined version. - Proceed: if it is genuinely a distinct task that merely reads similar,
store yours (the write already happened unless you passed
skip_if_duplicate=true).
Hygiene
- Run
drift_report()periodically (e.g. when reviewing cache health). For flagged entries,forgetand re-remember more specific variants, or raise the recall threshold for that family. stats()if the user asks whether memory is helping.- Never trust a hit above 0.85 on interpretive tasks ("extract X" vs "extract Y" read almost identically to the embedder); verify first.
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 · 70 lines · 70 tokens per session scan A ea008aac1c50
subconscious-memory is a skill published in the GitHub repository vishaltorc/subconscious-mcp (0 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 679 once invoked, about $0.0003 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.
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