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custom-investigation

Run a custom investigation: review a chosen slice of conversations instead of a random sample, by writing a SQL selector over the observed agent's telemetry, and optionally narrow what the reviewer reports. Use when a message asks to investigate only certain conversations -- ones that hit an error, used a particular tool, ran slowly, mention a topic, looped, or were abandoned -- or asks to re-review conversations already seen with a different focus. Covers the telemetry table a selector reads, writing the selector, previewing what it matched, and starting the run.

Quality

96%

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SecuritybySnyk

Medium

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An unusually strong skill body: near-total information density of non-obvious operational detail, fully executable guidance with justified placeholders, an explicit validation-and-approval workflow with error-recovery loops, and clean one-level-deep reference separation. The only weakness is minor: a few passages (link-rendering emphasis, the structural-defence paragraph) repeat or belabor their point and could be tightened.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious operational knowledge Claude could not already know (refusal semantics, three-attempt limits, quota interactions, insight-recency side effects), with almost no padding — but a few passages could be trimmed: the link-rendering section restates its rule three times ("no exceptions" heading, "Every example gets its case_view_path link, every time", "Never list the examples as plain text…"), and the "structural defence" paragraph in Narrowing the reviewer is wordier than its point requires. This fits anchor 4 (efficient, minor instances that could be trimmed) rather than anchor 5 (every token earns its place).

4 / 5

Actionability

Guidance is fully executable: an exact 7-call tool sequence, a copy-paste markdown bullet template for examples, the exact SQL splicing contract (`SELECT DISTINCT target_id AS session_id FROM (<your selector>)`), and exact `AI.IF` syntax with the `__AI_MODEL__` placeholder whose flexibility is explicitly justified ("the deployment's model is substituted for it"). Worked selectors are correctly delegated to the recipes reference file, which the workflow's first step loads; combined, the common cases are covered copy-paste ready.

5 / 5

Workflow Clarity

The 7-step workflow is clearly sequenced with explicit validation checkpoints — preview, show matches to the user, "get approval", then start — and the run is validated again at start. Error-recovery feedback loops are explicit: rejected selector → fix → preview again (with the three-strikes `attempts_exhausted` rule), `timed_out` → narrow → retry, and refusal handling for non-`session_review` deployments. This matches anchor 5 (clear sequence, explicit validation, feedback loops for error recovery).

5 / 5

Progressive Disclosure

The body is an operational overview and contract; the three reference files (big_query.md, cloud_logging.md, cloud_ops.md — verified present) each hold one telemetry source's column details and worked selectors, exactly one level deep. Navigation is well signaled: the body instructs loading exactly the recipes file named by `selector_recipes` and warns "Load only that file: the other references describe other telemetry sources"; the references' shared `SELECTOR_TABLE` placeholder convention matches the body's instruction to substitute the `table` returned by `describe_telemetry`. Content is appropriately split for a deployment-dependent table.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is a model example: third-person voice, concrete capability list covering the full workflow, an explicit "Use when" clause enumerating natural trigger scenarios, and a clearly distinct niche. Every clause carries information with no padding or over-claims.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "writing a SQL selector over the observed agent's telemetry", "optionally narrow what the reviewer reports", and "Covers the telemetry table a selector reads, writing the selector, previewing what it matched, and starting the run" — comprehensively covering the skill's workflow. This matches the anchor-5 example's structure (multiple specific concrete actions, comprehensive coverage); score 4 would require minor coverage gaps, and none are evident.

5 / 5

Completeness

Both what and when are explicit: the what is "review a chosen slice of conversations instead of a random sample, by writing a SQL selector" and the when is an explicit "Use when a message asks to…" clause with concrete trigger phrases. This mirrors the anchor-5 good example (clear what + explicit Use-when with concrete triggers); the anchor-4 case (when "could be more explicit") does not apply.

5 / 5

Trigger Term Quality

Trigger phrases are natural user language enumerating the full scenario space: "investigate only certain conversations -- ones that hit an error, used a particular tool, ran slowly, mention a topic, looped, or were abandoned" plus "re-review conversations already seen with a different focus". Coverage includes synonyms across the triggering scenarios (error, latency, tool usage, topic, looping, abandonment, re-review), matching the comprehensive anchor rather than the "a few natural terms missing" anchor.

5 / 5

Distinctiveness Conflict Risk

The description carves a clear niche — custom investigation of agent-conversation telemetry via SQL selectors and review focus — with triggers ("investigate only certain conversations", "re-review… with a different focus") that are unlikely to fire for any other skill. Minimal conflict risk, matching the anchor-5 "clear niche with distinct triggers".

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 3 missing

Warning

Total

15

/

16

Passed

Repository

google/adk-samples

Path

core/python/ambient-quality-agent/src/ambient_quality_agent/skills/custom-investigation/SKILL.md

Reviewed

Table of Contents

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