Menu
Mint short-lived CLI access tokens via the daimon MCP server's get_cli_token tool.
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Based on SOC occupation classification
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/pymc-labs/daimon --skill cli-authThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Showing SKILL.md
| name | cli-auth |
| description | Mint short-lived CLI access tokens via the daimon MCP server's get_cli_token tool. |
Use the daimon MCP server's get_cli_token(service) tool to mint short-lived
access tokens for external CLIs. Export the result as the appropriate env var
before running CLI commands.
| Service | Tool call | Env var to export |
|---|---|---|
| GitHub | get_cli_token("github") | GH_TOKEN (or GITHUB_TOKEN) |
| Google Cloud / Workspace | get_cli_token("gcloud") | CLOUDSDK_AUTH_ACCESS_TOKEN |
Example shell flow:
export GH_TOKEN=$(get_cli_token github)
gh repo list
The tool requires:
github: an operator (or a Discord user via /agent-setup) must have
bound a GitHub PAT. Tokens are minted from the persisted PAT.gcloud: The agent must be bound to a Google identity
(agent_google_binding) with appropriate scopes, configured by an operator.
Tokens are short-lived (≈1 hour) impersonated access tokens.Tokens are minted fresh per call and never cached.
Mutate data without destroying the evidence — dedup, dtype coercion, missing-value decisions, outlier flagging, category and unit normalisation, joins, pivots and aggregations to a new grain, and columns the source does not contain — appending a row to run/changelog.jsonl for every operation, whether what you write is one parquet file, one part per table, parquet parts of a log too large to load, a saved warehouse query, or cleaned files plus their index frame. The source is never edited in place, and under pandas 3 copy-on-write a chained assignment like df[mask]["col"] = 0 silently changes nothing. Use when a validation check failed, when the grain has duplicates, wrong dtypes or unparseable dates, when a source is too large or raw to query repeatedly and must be materialised once as typed parquet parts, when a collection needs an index frame derived, when a metric or label no column holds must be defined, or when asked to clean, dedup, impute, join, reshape or fix a dataset.
Load whatever the request points at — one CSV or Excel export, twelve related tables, a folder of 500 emails, a 4 GB log you never load, a warehouse table you query in place — and record every source in run/manifest.json with the grain one row represents, the axis it is ordered by, its row count, columns and dtypes. Read as text and coerce on purpose — pd.read_csv inference turns order_id 00123 into the integer 123, and a paged pull that stops early looks identical to a complete one. Use when a user attaches a file or a folder, points you at a database, warehouse or API, or asks for analysis of data you have not loaded yet.
Decide whether data is fit to analyze before anyone analyzes it. A phase-1 pass runs in seconds — dtypes, the grain one row claims to represent, referential gaps between tables, plausibility ranges, coverage — and emits run/validation.json with a pass/warn/fail verdict; a fail is a stop, not a to-do item. Works the same on a flat export, twelve related tables, a keyless sensor stream, an index frame over 500 documents, or a warehouse table you check with SQL and never load. Phase 2 hunts the structural break — a definition, unit, currency or timezone that changed partway along whatever the data is ordered by. Use when data arrives from ingestion, before any chart, join or model, or when a number looks wrong and you cannot say why.