10-of-my-most-popular-text-to-image-series-prompts-78b0897e
generate a bunch of images, then you curate the results to handpick the best ones
Makes agent corrections persistent and reusable. When you override, reject, or correct an agent's output, this skill logs the correction and automatically injects it into future spawns of the same agent type. Solves "agent keeps making the same mistake across sessions." Installs correction-tracker lib + injection hook into agent-context-loader. Works standalone or alongside intent-engineering skill.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install donovanpankratz-del/correction-memory打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
When you correct an agent, that correction evaporates after the session. Next time you spawn the same agent type, it makes the same mistake. There's no memory of what you've already taught it.
lib/correction-tracker.js — logs corrections per agent type to memory/corrections/[AgentType].jsonl
agent-context-loader.js — correction preamble prepended to spawns automatically (if intent-engineering is also installed)cp references/correction-tracker-template.js $OPENCLAW_WORKSPACE/lib/correction-tracker.js
Verify it runs:
node $OPENCLAW_WORKSPACE/lib/correction-tracker.js
If lib/agent-context-loader.js is installed (from intent-engineering skill), correction injection is automatic — no wiring needed. The loader checks for correction-tracker.js at startup and loads it if present.
If you are NOT using intent-engineering, add this to your spawn logic manually:
const { buildCorrectionPreamble } = require('./lib/correction-tracker');
const agentType = 'CoderAgent'; // or whatever agent you're spawning
const corrections = buildCorrectionPreamble(agentType, workspaceRoot);
const fullTask = corrections ? corrections + '\n\n---\n\n' + originalTask : originalTask;
const { logCorrection } = require('./lib/correction-tracker');
logCorrection(
'CoderAgent', // agent type
'Used ESM import instead of require()', // what was wrong
'Always use require() for Node.js stdlib modules', // correct behavior
workspaceRoot,
{ session_channel: 'discord' } // optional metadata
);
Just tell the main agent:
"Note that [AgentType]: [what it did wron...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# Correction Memory
## The Problem
When you correct an agent, that correction evaporates after the session. Next time you spawn the same agent type, it makes the same mistake. There's no memory of what you've already taught it.
## What This Skill Installs
- **`lib/correction-tracker.js`** — logs corrections per agent type to `memory/corrections/[AgentType].jsonl`
- **Hook into `agent-context-loader.js`** — correction preamble prepended to spawns automatically (if intent-engineering is also installed)
## Installation
### Step 1 — Install correction-tracker
```bash
cp references/correction-tracker-template.js $OPENCLAW_WORKSPACE/lib/correction-tracker.js
```
Verify it runs:
```bash
node $OPENCLAW_WORKSPACE/lib/correction-tracker.js
```
### Step 2 — Wire agent-context-loader (if using intent-engineering)
If `lib/agent-context-loader.js` is installed (from intent-engineering skill), correction injection is **automatic** — no wiring needed. The loader checks for `correction-tracker.js` at startup and loads it if present.
If you are NOT using intent-engineering, add this to your spawn logic manually:
```javascript
const { buildCorrectionPreamble } = require('./lib/correction-tracker');
const agentType = 'CoderAgent'; // or whatever agent you're spawning
const corrections = buildCorrectionPreamble(agentType, workspaceRoot);
const fullTask = corrections ? corrections + '\n\n---\n\n' + originalTask : originalTask;
```
## Logging Corrections
### Programmatic
```javascript
const { logCorrection } = require('./lib/correction-tracker');
logCorrection(
'CoderAgent', // agent type
'Used ESM import instead of require()', // what was wrong
'Always use require() for Node.js stdlib modules', // correct behavior
workspaceRoot,
{ session_channel: 'discord' } // optional metadata
);
```
### Via main agent (natural language)
Just tell the main agent:
> "Note that [AgentType]: [what it did wron...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
Productivity system based on Maya calendar with 13 natural tones for project management and personal development
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.