Codex

memory-log-append

Append a structured event to a consumer's semantic memory log

104 stars

Best use case

memory-log-append is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

It is a strong fit for teams already working in Codex.

Append a structured event to a consumer's semantic memory log

Teams using memory-log-append should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/memory-log-append/SKILL.md --create-dirs "https://raw.githubusercontent.com/jmagly/aiwg/main/.agents/skills/memory-log-append/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/memory-log-append/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How memory-log-append Compares

Feature / Agentmemory-log-appendStandard Approach
Platform SupportCodexLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Append a structured event to a consumer's semantic memory log

Which AI agents support this skill?

This skill is designed for Codex.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# memory-log-append

Append a single structured JSON event to a consumer's `.log.jsonl` file. This is the write primitive for the semantic memory kernel's event stream.

## When to Use

Called internally by other kernel skills (`memory-ingest`, `memory-lint`, `memory-query-capture`) after completing an operation. Can also be called directly for custom event types.

## Parameters

### --consumer (optional)
Consumer ID to log against. Resolved via ADR-021 D4 precedence: explicit > wrapper > auto-detect.

### --op (required)
Operation type: `ingest`, `lint`, `query-capture`, `log-render`, `index-rebuild`, or a custom string.

### --data (required)
JSON object with operation-specific fields per the `memory-log-event` schema.

## Operation

1. **Resolve consumer** — determine which consumer's log to append to.
2. **Load topology** — read `memory.topology.log` path from consumer's `manifest.json`.
3. **Validate event** — ensure required fields (`ts`, `op`, `consumer`, `actor`) are present. Add `ts` and `actor` automatically if missing.
4. **Append** — write single JSON line to the `.log.jsonl` file (create file if first entry).
5. **Activity log** — also append a summary line to `.aiwg/activity.log` per the `activity-log` rule.

## Event Construction

The skill auto-populates:
- `ts` — current ISO 8601 timestamp (UTC)
- `actor` — current model identifier
- `consumer` — resolved consumer ID

The caller provides `op` and all operation-specific fields.

## Error Handling

- Log write failures are **non-blocking** — the calling skill reports success/failure of its primary operation, not the log write.
- If the log file path doesn't exist, create parent directories first.
- If the log file is corrupted (not valid JSONL), append anyway — `memory-lint` will flag the corruption.

## Schema Reference

@semantic-memory/schemas/memory-log-event.md

## Examples

```
# After an ingest operation
memory-log-append --consumer research-complete --op ingest --data '{"source":"paper.pdf","pages_touched":["summaries/paper.md"],"contradictions":0,"duration_ms":5200}'

# After a lint operation
memory-log-append --consumer sdlc-complete --op lint --data '{"findings":{"error":0,"warning":3,"suggestion":8},"duration_ms":2100}'
```

Related Skills

ralph-memory

104
from jmagly/aiwg

Manage Al semantic memory entries — list, query, and clear lessons learned across loop iterations

Codex

memory-query-capture

104
from jmagly/aiwg

Capture query synthesis as durable pages in semantic memory

Codex

memory-log-render

104
from jmagly/aiwg

Generate a human-readable Markdown view from a consumer's JSON Lines event log

Codex

memory-ingest

104
from jmagly/aiwg

Ingest a source into any consumer's semantic memory by reading the topology contract

Codex

memory-forensics

104
from jmagly/aiwg

Volatility 3 memory forensics workflows covering acquisition with LiME and WinPmem, and structured analysis using Volatility 3 plugin reference

Codex

debug-memory

104
from jmagly/aiwg

Query and manage the executable feedback debug memory

Codex

aiwg-orchestrate

104
from jmagly/aiwg

Route structured artifact work to AIWG workflows via MCP with zero parent context cost

venv-manager

104
from jmagly/aiwg

Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.

pytest-runner

104
from jmagly/aiwg

Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.

vitest-runner

104
from jmagly/aiwg

Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.

eslint-checker

104
from jmagly/aiwg

Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.

repo-analyzer

104
from jmagly/aiwg

Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.