agentleFS
n24q02mn24q02m/mnemo/skills/recall-context/SKILL.md

recall-context

Skill13 starsChanged 9 months agoApache-2.0View on GitHub

Use at session start, before significant decisions, or when a new task references a known project to recall mnemo memories matching the current working directory, recently edited files, or topic keywords. Helps maintain continuity across sessions and avoid redoing past research.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    After a long context gap: if the conversation referenced earlier decisions but the agent does not have them in working memory, recall them …

    line 23
  2. Distinctive:

    Default (no arg): combine cwd + last 3 file paths.

    line 34
  3. Very specific:

    Skip silently: if mnemo is offline (tool errors), returns 0 results, or only returns low-relevance matches (rerank_score < 0.3).

    line 54
  4. Distinctive:

    If 0 useful results: stay silent.

    line 75
  5. Very specific:

    Short summary > long dump: never paste raw memory contents - the agent should already have full memory access via the memory tool if it …

    line 85

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Recall Context

Proactive memory retrieval that pulls prior context relevant to the work about to happen. Reduces "starting from scratch" errors and prevents the agent from re-deriving conclusions already captured in mnemo.

When to Use

  • Session start: load any preferences, decisions, or open questions tied to the current cwd / project before the user types their first prompt.
  • Before a significant decision: surface prior decisions in the same area (e.g. database choice, lint rules, deployment target) so the agent does not contradict an earlier conclusion.
  • When the user names a topic: e.g. "let's work on the auth flow" - pull memories tagged or describing auth before proposing a plan.
  • After a long context gap: if the conversation referenced earlier decisions but the agent does not have them in working memory, recall them on demand.

Steps

  1. Resolve query terms from the trigger:
    • cwd: use the current working directory path + project name as the query (e.g. mnemo or /c/Users/.../wet).
    • recent: use the last 5-10 file paths the agent edited or read.
    • <topic>: use the topic verbatim (the user's words).
    • Default (no arg): combine cwd + last 3 file paths.
  2. Search mnemo with context_type filtering when applicable:
    memory(action="search",
           query="<resolved query>",
           context_type=null,
           limit=10,
           include_archived=false)
    • For decisions only: pass context_type="decision".
    • For preferences only: pass context_type="preference".
    • Without filter, results span all six context types.
  3. Synthesize results into a 2-3 sentence summary grouped by type:
    • decisions, preferences, facts, open tasks
    • Present to the user as: "From prior sessions: ..."
    • Include memory IDs for any item the user might want to update or delete later.
  4. Skip silently if mnemo is offline (tool errors), returns 0 results, or only returns low-relevance matches (rerank_score < 0.3). Do not inject noise into the conversation.

Output Template

## Recalled context (<N> memories)

**Decisions** ([id1], [id2])
- ...

**Preferences** ([id3])
- ...

**Open tasks** ([id4])
- ...

(Recalled via mnemo:recall-context. Use memory(action="get", id=...) for full text.)

If 0 useful results: stay silent.

Quality Rules

  • No injection on every prompt: this skill is invoked by the agent on judgment, not auto-fired by a hook on every user message.
  • Filter by context_type when intent is clear: searching all types is noisy; restrict to decision/preference when the user is about to make a choice.
  • Cite memory IDs: enables follow-up update/delete without re-search.
  • Short summary > long dump: never paste raw memory contents - the agent should already have full memory access via the memory tool if it needs detail.

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