memory-log-append
Append a structured event to a consumer's semantic memory log
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/memory-log-append/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How memory-log-append Compares
| Feature / Agent | memory-log-append | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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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}'
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