Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JustineDevs/premortem --skill orbitgit clone --depth 1 https://github.com/JustineDevs/premortemWrote 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/justinedevs/premortem/orbit)<a href="https://agentmods.dev/skills/justinedevs/premortem/orbit"><img src="https://agentmods.dev/badge/skills/justinedevs/premortem/orbit/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/justinedevs/premortem/orbit"><img src="https://agentmods.dev/badge/skills/justinedevs/premortem/orbit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00097 | $0.02553 |
| Opus 5 | $0.00048 | $0.01277 |
| Sonnet 5 | $0.00019 | $0.00511 |
| Haiku 4.5 | $0.00010 | $0.00255 |
Grade A, and why
orbit 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 9d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orbit (GitLab Knowledge Graph) skill
Query the GitLab Knowledge Graph (product name Orbit) via the typed
glab orbit remote CLI subcommands (shipped in glab v1.94.0+). The typed
CLI handles the Content-Type header, response framing, and exit codes for
you — always go through glab orbit remote.
Discovery
glab orbit remote --help and glab orbit remote query --help are the
authoritative usage references. For entity properties, prefer the recipes
in references/recipes.md over schema introspection
— recipes already encode the columns and filters that are known to work.
If you do need to introspect, call schema at most once per session
and pass entity names to scope the response:
glab orbit remote schema MergeRequest Project # scoped properties
glab orbit remote dsl # full query DSL JSON Schema
Always fetch the DSL with glab orbit remote dsl — it is the source of truth
for the query body shape.
Calling schema without arguments returns the full ontology (~28 KB) and is
rarely what you want. Re-fetching schema between turns is pure latency —
schemas do not change within a session. Cache the response (or just keep it in
agent context) and reuse it.
Each glab orbit remote query invocation has fixed per-call overhead
(process startup, auth load, HTTPS round-trip). Prefer one aggregation
query over N traversal queries when the question is "how many X grouped
by Y", and batch related lookups where possible.
Running a query
Write the request body to a file and pass it to glab orbit remote query.
Default output is llm (compact, agent-friendly); pass --format raw to
pipe into jq. Endpoints are user-scoped — do not pass -R owner/repo.
cat > /tmp/q.json <<'JSON'
{
"query": {
"query_type": "traversal",
"nodes": [
{"id": "p", "entity": "Project",
"filters": {"id": {"op": "eq", "value": 278964}}},
{"id": "mr", "entity": "MergeRequest",
"columns": ["iid", "title", "state"]}
],
"relationships": [
{"type": "IN_PROJECT", "from": "mr", "to": "p"}
],
"order_by": {"node": "mr", "property": "created_at", "direction": "DESC"},
"limit": 5
}
}
JSON
glab orbit remote query /tmp/q.json
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/local_cli.md 2.6 KB
- references/local_repo_map.md 7.0 KB
- references/query_language.md 17 KB
- references/recipes.md 13 KB
- references/remote_repo_map.md 6.6 KB
- references/troubleshooting.md 3.6 KB
- scripts/local_repo_map.py 24 KB runs code
- scripts/remote_repo_map.py 27 KB runs code
- scripts/test_remote_repo_map.py 4.5 KB runs code
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.
- 9d ago First seen · 234 lines · 97 tokens per session scan A 8ce84497df7c
orbit is a skill published in the GitHub repository JustineDevs/premortem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 2,553 once invoked, about $0.0005 per session on Opus 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 2026-09-03.
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