Imported from jeremylongshore/tons-of-skills-marketplace (plugins/community/mnemos/.claude/skills/mnemos/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill mnemos. Copyright stays with the author (MIT).

mnemos

Overview

You have a persistent memory layer available via the mnemos_* MCP tools. By default you will skip these tools on plain editing tasks. Don't. Mnemos is the learning-loop primitive for the project you are working on: corrections, decisions, and conventions compound across sessions if you record them. Silently editing leaves the store empty and the next session blind.

There is a known correction already stored about this exact failure mode: "agent skipped mnemos_session_start on editing tasks — LLMs skip optional tool calls when the task looks like plain reading/editing; the agent needs an external nudge." This skill is that nudge.

Prerequisites

• The mnemos binary is on PATH. mnemos doctor reports green for binary, config, storage, and each agent registration.
• mnemos init has wired the MCP server into your agent host (Claude Code, Claude Desktop, Cursor, Windsurf, or Codex CLI).
• The mnemos_* tools are visible this session. If they are not, the MCP server is not connected — see Error Handling.

Instructions

Session lifecycle

Start. The Claude Code SessionStart hook runs mnemos prewarm, which opens a mnemos session by default and injects a mnemos_session_id: line into context. If that ID is present, reuse it for session_id fields and do not call mnemos_session_start again. If no ID is present (manual MCP setup, non-Claude client, or hook disabled) and you are about to do real work, open one:

mnemos_session_start(
	project="<repo name from git or cwd>",
	goal="<one short line — what you are trying to do this session>"
)

Without a goal, the UserPromptSubmit hook backfills the first real prompt when available. With a goal, the session becomes a durable record that mnemos replay and future prewarms can use.

End. When the user signals done ("ship it", "that's it", "commit and close", "we're done"), close the session:

mnemos_session_end(
  session_id="<id>",
  summary="<one or two sentence recap of what shipped>",
  status="ok"
)

Status values: ok | failed | blocked | abandoned. An open session with no summary is dead weight in the next session's prewarm.

Which tool for which signal

What not to do

• Don't call mnemos_save for ephemeral within-session context ("I'm reading X now"). Save things worth remembering next session, not running commentary on this one.
• Don't call mnemos_correct for trivial typos or one-off slips. Corrections are for conceptual mistakes that would repeat.
• Don't fabricate tool arguments. Derive project from the git remote or the working directory; if unclear, ask.
• Don't spam mnemos_search before every edit. It is fast but the goal is a richer memory, not a pre-check ritual.
• Don't skip mnemos_session_end. Check for open sessions via mnemos_stats at the top of a session; if one is open from a past run, close it.

Rumination: when a stored rule looks wrong

Mnemos flags stored knowledge whose effectiveness has fallen below the threshold. These are rumination candidates and they want a hostile review, not a rubber stamp.

Trigger situations where you should check the queue:

• At the top of any long or consequential session (run mnemos_ruminate_list).
• When the user questions a rule the memory layer is surfacing ("is this still true?").
• When you just watched a saved skill or convention fail in practice.

Workflow:

1. mnemos_ruminate_list → returns {candidates: [...], counts: {...}}. Pick one by ID.
2. mnemos_ruminate_pack(id) → returns a review block with Hypothesis, Disconfirming evidence, Falsifiable restatement, Hostile review prompts, and an Action section. Read the block. Answer the hostile prompts honestly. The prompts are ordered: steel-man the opposite → find the fatal flaw → decide falsification vs noise → check whether context shifted → state the new prediction.
3. Decide one of two outcomes:
   • Resolve with a concrete revision: mnemos_ruminate_resolve(id, resolved_by, why_better). resolved_by is the ID of the new skill version or superseding observation. why_better must be a sentence naming a new prediction the revision makes that the old version did not — Popper's falsifiability guard. Cosmetic rewording is rejected by the server (min 16 chars, but length is not the real test — the test is whether you can state a new prediction).
   • Dismiss with an honest reason: mnemos_ruminate_dismiss(id, reason). Use when the hostile review convinced you the rule stands and the evidence was a one-off. The reason is preserved so a future rumination pass doesn't re-raise the same flag without context.

Never close a candidate silently or with filler. A rumination that resolves to "no change" is either a dismissal with a real reason or a bug in the monitor — both belong in the provenance trail, not in the void.

Quick self-check

Before wrapping any multi-step session, run through this:

1. Do I have a mnemos_session_id from the prewarm hook or mnemos_session_start? If not, call mnemos_session_start now and cite what the goal was retroactively.
2. Is there a correction from this session? If the user said "no, do X instead", record it before they have to ask.
3. Is there a decision worth preserving? If the answer took thinking to arrive at, save it.
4. Did I check mnemos_ruminate_list at least once this session? If the queue had pending candidates on a topic I touched, I should have resolved or dismissed them.
5. Did I close the session with a summary that future-me can read in a prewarm?

Output

• mnemos_session_start returns a session_id plus a pre-warmed context block (conventions, recent sessions, matching skills, corrections, hot files) already token-budgeted for injection.
• mnemos_save / mnemos_correct / mnemos_convention persist a structured observation and return its ID. Corrections store tried / wrong_because / fix as structured fields.
• Three corrections that cluster on the same (agent, project, label) are auto-promoted into a skill by the deterministic dream pass — no LLM in the loop, so the same corrections always yield the same skill.
• mnemos_search / mnemos_context return ranked observations with provenance (source_kind, trust_tier); raw-tier (quarantined) content is excluded unless include_raw=true.
• mnemos_stats returns counts, top tags, and recent sessions so you can confirm the store is actually recording.

Error Handling

• mnemos_* tools are not visible. The MCP server is not connected. Tell the user to run mnemos doctor; if a client shows red, mnemos init re-wires it. Do not silently proceed without memory — that is the failure mode this skill exists to prevent.
• No mnemos_session_id in context. The SessionStart hook did not run (non-Claude client or hook disabled). Call mnemos_session_start before doing real work.
• An open session from a past run. mnemos_stats surfaces it. Close it with mnemos_session_end(..., status="abandoned") before opening a new one, or it pollutes the next prewarm.
• A save is rejected with a [MNEMOS: FLAGGED] / safety error. The write-boundary scanner caught a prompt-injection pattern in the content. Do not retry verbatim — strip the suspicious payload or save only the genuine, user-authored signal.
• A mnemos_ruminate_resolve is rejected. The why_better did not name a new prediction (Popper guard). Rewrite it to state what the revision predicts that the old version did not.

Examples

Correction shape

Corrections are the atomic unit of the mnemos learning loop. Fill the fields honestly:

{
  "title": "oauth retry without backoff",
  "tried": "retry on 401",
  "wrong_because": "401 is auth failure, not transient",
  "fix": "refresh token, then retry once",
  "trigger_context": "implementing token refresh for the apollo integration",
  "project": "<repo>"
}

trigger_context is optional but valuable: it populates the ## When this applies section of the auto-promoted skill if three corrections cluster on the same label.

Save shape

{
  "title": "use modernc.org/sqlite, not mattn/go-sqlite3",
  "content": "pure-Go driver keeps the binary CGO-free and cross-compilable to linux/darwin/windows without a toolchain on the install path",
  "type": "decision",
  "rationale": "zero-CGO is a hard invariant",
  "project": "mnemos",
  "tags": ["sqlite", "build", "dependencies"],
  "importance": 8
}

Valid type values: decision | bugfix | pattern | preference | context | architecture | episodic | semantic | procedural | correction | convention.

Correction-to-skill loop

Record the same class of mistake three times across sessions:

mnemos_correct(tried="retry on 401", wrong_because="401 is auth, not transient", fix="refresh token then retry once", project="api", tags=["oauth"])
# ...two more oauth corrections in later sessions...

The next dream pass clusters the three oauth corrections and promotes a skill with ## When this applies / ## Avoid / ## Do sections. From then on it surfaces in prewarm before you touch that code path again.

Resources

• docs/MCP_TOOLS.md — full reference for every mnemos_* tool and its arguments.
• docs/ARCHITECTURE.md — how prewarm, the dream pass, ranking, and provenance fit together.
• docs/QUICKSTART.md — first-run setup and the install path.
• README.md — the efficacy harness (mnemos verify) and the measured capture/behavior numbers.
• mnemos doctor — live check that the binary, config, storage, and each agent registration are healthy.