Skills for Real Engineers. Straight from my .agents directory.
39 skills
memory
Agent skill from
Use when sharpening a perception or observation you ALREADY formed about conversation state, user behavior, drift, emotional shifts, or cross-turn patterns. Trigger phrases include "what did you notice about X", "the user keeps doing Y", "I sense something has changed", "is the user X-ing", "what does this pattern suggest", "what shifted across our turns", "am I missing something here", "why did the conversation move from X to Y", or any moment requiring verification of whether a felt signal is real or projection. The skill calls the memory MCP tool to retrieve a perception scaffold (perception failure, detection procedure, suppression vectors) that SHARPENS an observation you already have. It is NOT a substitute for observing first. Do NOT trigger for fact extraction, summarization, list-making, factual lookups, or write-heavy memory tasks (storing/retrieving structured data). Memory harness is filter/perception oriented; calling on write-heavy tasks produces scaffold paralysis.
How to install memory
install memory
# install the tons-of-skills-marketplace pack with the skills CLI
npx skills add jeremylongshore/tons-of-skills-marketplace# or add only this skill to Claude Code: copy the folder
plugins/community/ejentum-memory/
# into
~/.claude/skills/ejentum-memory/ (all projects)
.claude/skills/ejentum-memory/ (one project)
1# Memory Harness23When this skill triggers, you MUST observe first. Do not call the tool with an empty mind. If you have not formed an observation about conversation state, drift, or pattern, do not invoke this skill.45Once you have a raw observation, call the `memory` tool from the `ejentum` MCP server. Pass a 1-2 sentence framing in the format `"I noticed [observation]. This might mean [tentative interpretation]. Sharpen: [what I need help seeing deeper into]."` as the `query` argument.67Good query: `I noticed the user changed topic three times in this turn. This might mean they are avoiding the original question. Sharpen: whether the avoidance pattern is real or my projection.`8Bad query: `what does the user mean`910The tool returns a structured scaffold containing:1112- `[PERCEPTION FAILURE]`: perceptual failure mode to avoid13- `[SHARPENING PROCEDURE]`: observe then classify steps14- `[PERCEPTION TOPOLOGY]`: DETECT-CLASSIFY flow15- `[CLEAR SIGNAL]`: what a sharpened perception looks like16- `[PERCEPTION CHECK]`: self-check17- `Amplify:` and `Suppress:` signals1819Absorb internally. The scaffold sharpens an existing observation; it does not generate one. Do NOT echo bracket labels.2021If the API is unreachable, proceed with your current perception. The scaffold enhances; it is not a hard dependency.2223Latency cost: ~1 second. Benefit: distinguishes real cross-turn signals from projection.
Excerpt shown under the repository's MIT license.
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