Case Data Material Planner logo

Case Data Material Planner

Organization
openqa-cn
case-data-material-planner

Prepares executable case preconditions and case-executable.md from a requirement or written cases. Internal case-material engine; enter through codexqa-testdata-generator so SKILL_DIR and adapters resolve.

Overview

Publisheropenqa-cn
Repositorycodexqa
Skill namecase-data-material-planner
Stars
233
Forks
3
Bundled files
41
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 41 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openqa-cn on GitHub. Read the source before you install it.

Installation

Install the Case Data Material Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openqa-cn/codexqa/tree/main/skills/codexqa-testdata-generator/references/case-data-material-planner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openqa-cn/codexqa.git /tmp/codexqa
mkdir -p .claude/skills
cp -r /tmp/codexqa/skills/codexqa-testdata-generator/references/case-data-material-planner .claude/skills/case-data-material-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Case Data Material Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Case Data Material Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Case Data Material Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

case-data-material-planner

One-stop material engine between case authoring and case execution.

  • Build an optional business-context graph from requirements
  • Parse entities and actions from each case
  • Construct data and bind commands through adapters
  • Emit case-executable.md

How the work is divided

scripts/pipeline.ts owns orchestration. You supply judgment at three points only — parse-case, knowledge-build, and select-tool — and the script does construction, binding, linting, and writeback. This split exists because those three stages need reading comprehension while the rest must be reproducible: a case re-run tomorrow has to yield the same manifest, which is impossible if a model re-derives each step. So when the runner exits asking for one of the three, launch that Agent, merge its patch, and --resume; do not take over a stage the script already implements (invoke_entity.ts, bind_action.ts, slot_render.ts), and with 2+ cases go through the dispatcher so each case gets its own pipeline.ts process.

Hard constraints

These are the ones that silently corrupt output rather than failing loudly, which is why they are worth stating:

  • Do not invent business IDs, amounts, or config values. A plausible-looking ID makes a case that fails at execution time with no trace of where the value came from. When unsure, record failReason and let the gate surface it.
  • Parse before constructing. Data needs come from the case's preconditions and steps; skipping parse means constructing something the case never asked for.
  • Keep case-executable.md business-only. No node / python / bash invoke lines, no skillRoot, cwd, host, or mock-server ports — those live in manifest.json (invokeCmd / filledCmd). The case document is read by people and replayed in other environments, where local paths and ports are wrong or meaningless.
  • Persist every non-envelope business field, not just the primary ID. A downstream step usually needs the attributes that came back with it, and re-fetching them is not always possible.
  • Respect the gates. C3 stays blocking when lint-gate is abnormal, and the lint-gate loop runs at most 3 rounds before it must hand the decision to the user — an unbounded loop burns the run without converging.
  • During knowledge-build, describe the business, not a shopping list. The graph answers "what does this domain look like"; deciding what data to prepare is the parse stage's job and doing it early biases the parse.

Entry routes

After receiving the request, classify the input:

Path A: requirement / design doc (+ cases)

  1. Read planner.md and run scripts/pipeline.ts
  2. Exit 12 → knowledge-build Agent, merge, --resume
  3. Then parse (exit 11) or continue if cases were already parsed

Path B: cases only (no requirement doc)

  1. Read planner.md and run scripts/pipeline.ts --case-id … --source …
  2. Reuse ./testdata/case-materials/business-context.json via --context when it exists
  3. Otherwise knowledge-build is skipped by the runner
  4. Handle exit 11 (parse-case) then --resume. Construction, binding, and writeback stay inside pipeline.ts — do not take those stages over.

Path C: batch (2+ cases)

When the user supplies 2 or more cases:

  1. Read case-dispatcher.md and follow it
  2. One pipeline.ts process per case (parallel). Shared knowledge-build once
  3. The user talks to the dispatcher, not a per-case Planner Agent

Rule:

  • case count ≥ 2 → Path C
  • case count == 1 → Path A or B

Input sources (highest first)

PrioritySourceHowNotes
0Caller-supplied sources[]Passed in the Task / delegate promptEach item is {caseId, sourceType, sourcePath}; skip interactive collection
1Document URLdoc_source.get(<ref>)Requirement / design / knowledge-base page
2planIdworkspace_context.get_plan(<id>) + api_catalog.search_plan_changes(<id>)Plan detail and change APIs
3Local file pathRead the file
4Pasted textUse as-is

Priority 0 is an optional fast path. sourceType values: remote-case | local-file | planId | paste. They map to the four legal caseSource.type values. Callers that omit sources[] still use priorities 1–4 interactively.

Material gate (case source)

The prerequisite material is the case pack, not executor fields.

  • No case source → ask for a file, paste, planId, or document URL. Do not invent case text. Do not start parse or construct.
  • Path A without cases → finish knowledge-build, then wait for cases. Do not jump to invoke.
  • Path B → at least one case is mandatory. knowledge-build may be skipped; parse may not.
  • Batch (2+) → every listed case needs a resolvable source. Do not silently drop a case.
  • After the user supplies cases, --resume pipeline.ts. Do not skip parse, invoke, or writeback.
  • Executor / tool params inside pipelines follow the original fill rules (defaults for optional fields; do not invent core IDs). C3 / lint-gate stay blocking.

Constraints

  • Execution logic lives in scripts/pipeline.ts; planner.md is the host protocol
  • LLM Agents allowed: parse-case, knowledge-build, select-tool only
  • Do not skip C3; the happy path has zero human gates
  • Lint-gate loop is at most 3 rounds
  • Entities and actions are equal tracks; an empty track short-circuits
  • targetLocation is the only bind to the original case text
  • Action params: paramsFromEntities / paramsFromGenerators / paramsFromPriorActions
  • Tool verify uses tool_registry.query_input_list (see references/param-source-spec.md)
  • businessContext = null is a valid degraded mode
  • Entities follow constructionStrategy: tool-build → invoke_entity.ts, config → generate_config_commands.ts, static-value → filled at parse, runtime → deferred to execution
  • New scenes: drop a slot under pack slots/ or workspace.slot_roots. select_tool.ts binds from slot.yaml (or inferred executors). Do not edit the selector

Path roots

Two roots, and mixing them up is the most common failure here:

VariablePoints atOwns
SUBSKILL_DIRdirectory of this file ($SKILL_DIR/references/case-data-material-planner/)scripts/, agents/, templates/, references/ — every relative path in this sub-skill
SKILL_DIRthe parent skill root that contains the top-level SKILL.mdscripts/adapters/, scripts/search_data_build.ts, slots/

There is no adapters/ folder under SUBSKILL_DIR/scripts/. Adapter calls go to $SKILL_DIR/scripts/adapters/cli.ts.

File layout

SKILL.md                    entry routing (this file)
planner.md                  host protocol for pipeline.ts
case-dispatcher.md          multi-case: one pipeline.ts per case
agents/                     LLM specs (parse / knowledge-build / select-tool)
  knowledge-build.md
  parse-case.md
  select-tool.md
  writeback.md              slot_render render rules, applied by the script
scripts/                    pipeline.ts + construct/writeback scripts
templates/                  manifest / cache / context templates
references/                 thresholds and contracts

Outputs

  • ./testdata/case-materials/business-context.json
  • ./testdata/case-materials/tool-binding-cache.json
  • ./testdata/case-materials/{case-id}/manifest.json
  • ./testdata/case-materials/{case-id}/case-executable.md

Dependencies

No private CLIs. All I/O goes through the parent skill's adapters ($SKILL_DIR/scripts/adapters/cli.ts):

NeedAdapter
Discover / install domain skillsskill_marketplace
Query / execute / publish toolstool_registry
Discover HTTP APIsapi_catalog
Proven methodsexperience_store
Documentsdoc_source
Test-plan contextworkspace_context
Case writebackcase_writeback
Config and SQLconfig_store, data_store

Collaboration

This sub-skill is hosted by codexqa-testdata-generator. select_tool.ts reuses scripts/parallel_search.ts, which calls the same adapters. Enterprise tools are added through those adapters or a new slot under slots/ / workspace.slot_roots — invoke_entity.ts only dispatches on toolType.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Case Data Material Planner AI skill do?

Prepares executable case preconditions and case-executable.md from a requirement or written cases. Internal case-material engine; enter through codexqa-testdata-generator so SKILL_DIR and adapters resolve.

Why use Case Data Material Planner on TypingMind?

Because you install it once and use it with any model. Case Data Material Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Case Data Material Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openqa-cn/codexqa/tree/main/skills/codexqa-testdata-generator/references/case-data-material-planner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Case Data Material Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Case Data Material Planner?

As many as you like. As long as a model supports skills, you can use Case Data Material Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Case Data Material Planner AI skill free?

Yes. It is published on GitHub by openqa-cn under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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