Skip to main content
Glama
liza-studio

skillmem — long-term memory for Claude Code & Codex

mem_reinforce

Record a skill's outcome to adjust its strength: test_passed, diff_accepted, or user_confirmed raises it, failure lowers it, while self_report only refreshes recency. Apply this to reflect real results.

Instructions

Record how a recalled skill turned out, so strength reflects results. WRITES the skill's counters. evidence: test_passed / diff_accepted / user_confirmed raise strength; failure lowers it; the default self_report only refreshes recency — your own judgement that it helped is not evidence. Each call counts; calling twice for one outcome double-counts. Fails for an unknown slug or a record that is not a skill. Returns slug, strength, access_count and the evidence recorded. Use mem_update to correct a skill's text instead; use mem_pin for a rule that must never decay.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesSkill slug to reinforce.
evidenceNoWhat confirms the outcome. self_report (default): you judged it useful — recorded, not rewarded. test_passed / diff_accepted / user_confirmed: outside signal, raises strength. failure: the task went wrong after applying it, lowers strength.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.10.5

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels. It reveals that the tool WRITES counters (mutation), explains how each evidence value affects strength (raises, lowers, or only refreshes recency), warns that calling twice double-counts, and lists return values (slug, strength, access_count, evidence). It also states failure behavior. This is far beyond what structured fields would convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph with no filler. It front-loads the core purpose and mutation effect, then sequentially covers evidence semantics, counting behavior, failure cases, and alternatives. Every sentence adds value; nothing is redundant or vague. It is concise yet comprehensive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with a single enum, no output schema, and no annotations, the description covers all necessary context: what the tool does, side effects (counters, double-counting), expected evidence values and their effects, failure modes, and return values. It even includes routing to sibling tools. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds significant meaning beyond the schema: it explains the default behavior of self_report (only refreshes recency, not reward), clarifies that each call counts and double-counts, and elaborates on the consequences of each evidence enum value. The slug parameter is implicit in the description. This goes beyond the schema, so a 4 is justified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear verb-resource statement: 'Record how a recalled skill turned out, so strength reflects results.' It explicitly says it WRITES the skill's counters, which is a specific action on a specific resource. It also differentiates itself from siblings by naming mem_update (for text correction) and mem_pin (for never-decay rules), so an agent can immediately distinguish it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it tells the agent that self_report is only recency refresh, not evidence; it warns against double-counting by saying each call counts; it states failure conditions (unknown slug or non-skill record); and it names the alternatives (mem_update for text fixes, mem_pin for rules that must never decay). This fully covers when and when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.