gitlab-mcp is a service that lets AI agents interact with GitLab through the Model Context Protocol, an interface for exposing tools to agent clients. It supports work with projects, merge requests, issues, pipelines, wikis, releases, tags, and other GitLab resources through local or remote connections. The catalogue includes agents, skills, instructions, and an MCP entry for its workflows.
Borrowing it
Nothing to install: this file belongs to zereight/gitlab-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zereight/gitlab-mcp/main/.github/skills/remember/SKILL.mdgit clone --depth 1 https://github.com/zereight/gitlab-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zereight/gitlab-mcp/remember)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/remember"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/remember/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/remember"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00773 |
| Opus 5.5 | $0.00014 | $0.00309 |
| Sonnet 5.5 | $0.00007 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
remember scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remember
Promote durable, reusable knowledge into the right memory surface instead of leaving it buried in chat history.
When to Use
- User wants to preserve knowledge discovered during a session
- Organizing scattered findings into structured memory
- Cleaning up duplicate or conflicting stored information
When NOT to Use
- Ephemeral task notes → just keep in conversation
- Already documented in code or docs → reference directly
Memory Surfaces
| Surface | Use For | Durability |
|---|---|---|
| Project memory | Durable team/project knowledge | Permanent |
| Session context | Short-lived working notes | Session only |
| Docs / Instructions | Conventions, instructions | Permanent |
Workflow
- Gather the relevant session findings
- Classify each item:
- Durable project fact
- Temporary working note
- Operator preference or instruction
- Duplicate / stale / conflicting information
- Propose the best destination for each item
- Write or update only the appropriate memory surface
- Call out duplicates or conflicts that should be cleaned up
Rules
- Do not dump everything into one store
- Prefer project memory for durable team knowledge
- Keep entries concise and actionable
- If something is uncertain, mark it as uncertain rather than storing it as fact
Output
- What was stored
- Where it was stored
- Any duplicates/conflicts found
Quality Gate (Before Storing)
Before committing anything to memory, apply this 3-question filter. Skip storage if the answer to any question is "No":
| Question | Rationale |
|---|---|
| Is it actionable? "Does this tell someone what to DO in a future situation?" | Observations are not memory entries; decisions are. |
| Is it durable? "Will this still be true in 3 months, or is it tied to a temporary workaround?" | Ephemeral findings belong in session notes, not project memory. |
| Is it unique? "Does something close already exist in memory that covers this?" | Duplicate entries create confusion; prefer updating existing entries. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 89 lines · 36 tokens per session scan A 2d89dd3c51e7
remember is a skill published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 773 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
kiro-steering-custom
Create custom steering documents for specialized project contexts.
identity-snapshot
Generate a living profile of the user's working patterns, decision tendencies and quality preferences from their Dex data. Use when the user says 'what are my patterns', 'how do I work', or during week-review. Also use proactively when the model (older than 7 days) is stale. Not for planning a week; use week-plan.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
daily-journal
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning. Use when you run /daily-journal, say…
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
ijfw-handoff
Session handoff generation and loading. Trigger: session end, context full, /handoff.