explain-lading-config

SkillFiles & storage

Explains what a lading.yaml regression test config does, grounding field meanings and defaults in the lading source code.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the explain-lading-config skill

About this capability

Explains a lading.yaml config file from the regression test suite, using the lading Rust source as ground truth for field meanings and defaults.

What this skill tells your AI

The instructions your AI receives, as published by datadog/datadog-agent in .agents/skills/explain-lading-config/SKILL.md and read by ahel’s review.

Explain what a lading regression test config does, grounded in lading source code.

Quick Start

# 1. Verify the lading checkout exists and is on a known branch
bash .agents/skills/explain-lading-config/scripts/validate-lading-checkout.sh

# 2. Resolve $ARGUMENTS to a lading.yaml path (exact/substring/glob/path)
bash .agents/skills/explain-lading-config/scripts/resolve-lading-config.sh "$ARGUMENTS"

# 3. Read the resolved file, then ground every field in lading source
#    (see references/source-reading.md for the full strategy).

# 4. Write up the explanation following references/explanation-template.md.

Defaults must be resolved to concrete values, not function names. Full workflow below.

Step 1: Validate lading checkout

Run .agents/skills/explain-lading-config/scripts/validate-lading-checkout.sh.

  • Exit 0: script prints the current branch on stdout. If it is not main, warn the user that explanations are grounded in a non-main branch, then continue.
  • Exit non-zero: the script prints a suggested git clone command on stderr. Relay that to the user and stop.

Override the checkout location with LADING_DIR if needed.

Step 2: Determine target file

Use .agents/skills/explain-lading-config/scripts/resolve-lading-config.sh to avoid ad-hoc matching. The script enumerates experiments under test/regression/cases/ (active) and test/regression/x-disabled-cases/ (disabled). Each experiment is a <case>/lading/lading.yaml addressed by its case-directory name; disabled rows are flagged with a trailing (disabled) column in the listing. ebpf/cases/ (split-mode) and ebpf/config-only/cases/ are intentionally out of scope; if a user asks about one, tell them this skill doesn't cover it yet.

The script handles path-like inputs, substring case names, and shell globs (*, ?).

If $ARGUMENTS is provided: run resolve-lading-config.sh "$ARGUMENTS".

  • Exit 0: stdout is the resolved absolute path; read it.
  • Exit 3 (ambiguous): stderr lists candidates.
    • ≤ 4 candidates: use AskUserQuestion to pick one, then read that path.
    • > 4 candidates (a broad substring like i can match 20+): do not try to force them into AskUserQuestion. Print the experiment names as a short bulleted list and ask the user to narrow the query and re-invoke /explain-lading-config <name>.
  • Exit 2 (not found): stderr may include "did you mean?" suggestions — if present, offer the suggestions to the user via AskUserQuestion (up to 4 options) or as a short list; if not, relay the error and stop.
  • Exit 4 (wrong repo): the script is being run from outside the agent repo. Relay the error verbatim and stop — the user needs to cd into the repo.

If the resolved path contains /x-disabled-cases/, flag this explicitly in the explanation — the experiment exists on disk but is not currently executed by SMP. Otherwise a user may assume it's live.

Reading very large configs: multi-sender configs (e.g. uds_dogstatsd_20mb_12k_contexts_20_senders, ~870 lines) are usually block-copies of one template with a few fields varying (typically only seed). Before a full Read, check size and duplication:

wc -l <path>                                    # scale check
grep -c '^  - ' <path>                          # top-level list entries
yq '.generator | length' <path> 2>/dev/null     # if yq is present

For highly-duplicated configs, Read only the first block (plus the blackhole/target_metrics sections) and report the generator as "N identical copies, seed differs" instead of walking every block. Spot- check one later block to confirm uniformity.

If $ARGUMENTS is omitted: run resolve-lading-config.sh with no argument. It emits <experiment>\t<path> lines for every discovered config.

Print the experiment names as a plain bulleted list to the user (preserving the (disabled) markers) and ask them to type the name (or re-invoke the skill with /explain-lading-config <name>).

Step 3: Read the lading codebase for context

Before explaining, read the lading source files that ground the populated sections of the config. The detailed strategy (variant-to-module mapping, grep-before-Read invariants, fallback for renamed files) lives in references/source-reading.md — read it now.

Step 4: Explain the config

Write the explanation following the structure in references/explanation-template.md (generator summary, aggregate load, blackhole sinks, target metrics, source references). Read it now.

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Sep 2026
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explain-lading-config
Source
github.com/datadog/datadog-agent