IA-QA — 130+ QA & Dev Tools for AI Agents

generate_ci_workflow

Read-onlyIdempotent

Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA. Two gate types, combinable: "eval_contract" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (LLM quality gate, needs a provider API key as a repo secret), and "cli_checks" runs deterministic primitives via npx @ia-qa/cli (secret scan, prompt-injection scan, security headers…) whose exit code fails the build. Deterministic template — no LLM call, no API key, same inputs give the same file. Returns the YAML, the secrets to create, and the remaining steps. Pair with generate_eval_yaml to produce the contract itself.

Input Schema

TableJSON Schema

Name	Required	Description
`cron`	No	Cron expression when triggers include 'schedule' (default: '0 6 * * 1' — Mondays 06:00 UTC).
`gate`	No	Which gate to emit. eval_contract = LLM eval via the action (default). cli_checks = deterministic CLI assertions. selector_drift = an E2E selector-drift gate via @ia-qa/self-healing (boots the app, captures, diffs against the committed baseline, branches on exit code 0/1/2). both = CLI checks + eval. all = CLI checks, then drift, then eval.
`provider`	No	LLM provider the contract runs against — decides which repository secret the workflow wires (default: groq).
`triggers`	No	Workflow triggers (default: push + pull_request).
`cli_tools`	No	IA-QA tool names to run as deterministic gates, e.g. ["secret_scan","prompt_injection_scan"]. Tools with no known CI recipe get a --stdin step flagged in notes.
`min_score`	No	Override the contract min_score (0-100). Omit to use the value in the contract.
`app_base_url`	No	URL the drift gate waits for before capturing (default: http://127.0.0.1:3000). Must match config.baseUrl in .ia-qa/config.json.
`fail_on_fail`	No	Fail the build on a FAIL/PARTIAL verdict (default: true). Set false to report without gating.
`node_version`	No	Node version for the CLI steps (default: "20").
`contract_path`	No	Path to the .ia-eval.yaml contract, relative to the repo root (default: evals/smoke.ia-eval.yaml). Only used when the gate includes eval_contract.
`workflow_name`	No	Workflow display name (default: "IA-QA Quality Gate").
`app_start_command`	No	Command that boots the app for the selector_drift gate, e.g. "npm run start:ci". Cannot be guessed — omitted, the step carries an explicit TODO and the note says so, because a plausible default would silently map nothing.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`gate`	No
`path`	No
`yaml`	No
`notes`	No
`next_steps`	No
`secrets_required`	No

TDQS

A4.4/5.0

Behavior5/5

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

The annotations already signal readOnly/idempotent/non-destruct, and the description adds important behavior beyond them: it is deterministic, makes no LLM call, requires no API key despite supporting LLM-based eval, and returns the YAML plus secrets and remaining steps. It also discloses the provider-API-key prerequisite for eval_contract, giving the agent useful operational 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 dense but economical: purpose first, then gate types, then determininism, then return payload, then the companion tool. Every sentence adds distinct value, and there is no filler or repetition of schema content.

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

Completeness4/5

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

For a 12-parametér tool, the description combined with a 100%-covered schema and an output schema is nearly complete. The one gap is that it says 'Two gate types' while the gate enum actually includes selector_drift as a third gate type plus combination values, so the prose slightly under-maps the tool's full capability.

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?

Paraméter description coverage is 100%, so the schema already explains every parametér with defaults, enums, and nuances (e.g., app_start_command cannot be guessed). The description adds narrative around gate types but does not materially enhance parametér semantics beyond what the schema provides, which makes the baseline 3 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 first sentence states a specific verb, resource, and outcome: generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA. It also distinguishes the tool from sibling generators by naming generate_eval_yaml as complementary and by describing the exact gate types it emits.

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 gives clear context for when the tool applies and explicitly points to generate_eval_yaml as the companion tool for producing the contract. It does not enumerate exclusions or alternatives beyond that, so it stops short of fully routing an agent to or away from other generator siblings.

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

A3.6/5.0

Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

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