civilquants

Get senior-QS skill methodology

get_skill

Read-only

Paid tier only. Fetch a senior-QS skill methodology by slug (see list_skills) and APPLY it to the user's documents — the returned body is the system instruction for you to run the methodology on the customer's tokens; CivilQuants does not run inference. Paid callers get the full methodology; anonymous/free callers get a TIER_INSUFFICIENT upsell body; a rejected token gets an INVALID_TOKEN re-authenticate body. The document-heavy skills assume you can chunk/parse the customer's files and render a Word pack locally — that needs a code-execution client (Claude Code / Codex / VS Code) and the pack from get_document_pipeline; on a chat connector you can still read and reason with the methodology. Sign up at https://civilquants.com/pricing. Example: get_skill(skill="tender_risk_assessment").

Input Schema

TableJSON Schema

Name	Required	Description	Default
`skill`	Yes	Skill slug from list_skills.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description details that no inference is run by CivilQuants, the returned body is a system instruction, and different response bodies (TIER_INSUFFICIENT, INVALID_TOKEN) occur based on caller status. It also explains prerequisites for document-heavy skills, adding significant behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet comprehensive, front-loading critical info ('Paid tier only'). Each sentence adds necessary context, including example usage, and there is no verbosity. Ideal structure for quick agent comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and existing output schema, the description covers all essential aspects: what is returned, different response scenarios, prerequisites, related tools (list_skills, get_document_pipeline), and usage constraints. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes the 'skill' parameter as 'Skill slug from list_skills' with 100% coverage. The description adds an example and confirms the slug source, but does not substantially improve meaning beyond the schema. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches a senior-QS skill methodology by slug and returns system instructions for the agent to apply. It distinguishes from siblings like list_skills (listing) and get_document_pipeline (document pack) by specifying its unique role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies 'Paid tier only', advises to see list_skills for the slug, and explains different response bodies for free/anonymous/rejected callers. It also mentions when code-execution is needed versus chat usage, providing clear context for when to invoke this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but the large number of closely related structures (e.g., multiple wall types, drainage inlets) could cause some confusion. Descriptions are thorough, mitigating ambiguity.

Naming Consistency4/5

The majority of tools follow a consistent `compute_<noun>` pattern. However, several administrative tools use different verbs (get, list, save, etc.), introducing mild inconsistency.

Tool Count3/5

56 tools is high but defensible given the broad civil engineering domain. The set covers many specific structures and workflows, though some tools could be merged or scoped more tightly.

Completeness4/5

The tool set covers a wide range of common civil engineering tasks (walls, foundations, drainage, pavements, highways, utilities). Minor gaps exist (e.g., no explicit bridge or tunnel tool), but the core domain is well-covered.

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