A team can evaluate the same package three times and still have no shared answer about whether to use it. The decision lives in a conversation, the tests live in another folder, and the developer who remembers the limitation is busy elsewhere.
A component catalog connects those pieces. It describes a reusable capability, points to its evidence, identifies its owner, and states the conditions under which it should be reconsidered.
This chapter of The AI Software Factory, in the SalarsNet AI section, proposes a lightweight catalog for a small software team. It does not require a new service or an elaborate internal marketplace. The first useful catalog can be a few maintained records in the existing repository.
Its purpose is to preserve scoped knowledge so the next application can reuse a decision responsibly instead of repeating it or inheriting an undocumented dependency.
Catalog the capability, then its implementation
A package name tells a developer where code comes from. A capability name tells them what job it can perform.
For a hypothetical catalog, “supplier-file normalization” could identify the capability. Its current implementation might use a particular parser plus a local mapping and validation wrapper. The record should distinguish those layers.
Describe the input, output, permitted transformations, failure conditions, and review boundary. The capability contract belongs to the product system; the implementation can change while the required behavior remains stable.
Avoid vague entries such as “AI helper” or “data utility.” A developer needs to know whether the component drafts a response, validates a record, transforms a file, or performs an external action.
The capabilities chapter examines the architectural grouping. The catalog makes a chosen capability discoverable with enough evidence to decide whether it fits another task.
Start with the decisions the team already needs
A catalog should follow actual reuse, not an imagined future library of every useful tool. Begin with components already evaluated for a real app or candidate.
Gather their decision records, contracts, tests, rights information, and owners. Identify what is missing before calling them reusable. Internal origin does not establish documentation, secure behavior, or suitability for new inputs.
A small catalog can contain accepted, conditional, deferred, and rejected entries. Rejected candidates prevent repeated investigations when the reason remains relevant. Deferred entries preserve a question that may later be resolved.
The evaluation chapter supplies adoption evidence. The catalog should link to that evidence rather than duplicate it into an ever-growing summary that becomes stale.
Choose an existing storage convention. If the team uses repository documentation, a Markdown record and index may be enough. A structured file can support machine checks where those checks solve a real problem. Do not build a catalog platform before the records themselves are useful.
Use a compact record with meaningful fields
A proposed record should include identity, capability contract, implementation version, origin, rights record, evaluation evidence, supported conditions, known limitations, owner, consumers, update policy, and review triggers.
Each field should answer a practical question. Identity prevents confusion between similarly named components. Contract establishes fit. Version makes the evaluation reproducible. Rights points to the actual obligations. Evidence shows what was inspected or executed.
Supported conditions distinguish tested environments from hoped-for compatibility. Limitations identify cases that need review or rejection. Owner identifies who responds when the component changes. Consumers show which apps may be affected.
A record can remain concise by linking to detailed artifacts. The catalog is the map to evidence, not a duplicate archive of every test output.
A hypothetical entry might state that a normalization capability accepts specified CSV inputs, preserves source identifiers, flags missing required fields, and produces a reviewed output. It would name the tested version and say that live inventory import is outside scope. This is an illustrative record, not an actual Salars component approval.
Distinguish observed behavior from documented behavior
A documentation claim and an executed fit check should have different labels. The project may document a supported runtime, while the team has tested only one environment.
Record what was inspected and what was run. For executed checks, preserve input identity, environment, command, expected outcome, actual result, and version. For documentation-only conclusions, preserve the source and inspection date.
A successful test does not justify an unlimited supported label. “Passed identifier-preservation cases on these harmless sample files” is a scoped observation. “Handles all supplier data” is a much larger claim.
Use that distinction when another app requests reuse. If its input conditions differ materially, the existing evidence can guide evaluation without completing it. A new task may need additional cases or a narrower promise.
The reuse-before-build chapter treats search as a decision. The catalog preserves the scope of that decision so reuse remains evidence-based.
Connect the catalog to dependency inventory
A capability record should identify the actual software artifacts beneath it. Otherwise the team can know what a component does while losing track of the packages it brings into production.
GitHub’s dependency graph summarizes supported manifests, lock files, and submitted data, with version, license, known-vulnerability information, and supported transitive relationships. It can assist inventory, but it does not replace the capability contract or resolve every rights and security question. Dependency graph.
Link the capability to its relevant dependency record. If an update changes the resolved set, the owner can examine whether tests, rights, or operating limits need review.
Keep application consumers visible. One shared update may affect several apps. Conversely, several apps may use different versions despite sharing a capability name. The catalog should show that difference rather than imply a uniform state.
The inventory answers what is installed. The catalog answers why it is used and under which conditions. Both are needed for a manageable software factory.
Preserve rights obligations without inventing clearance
The catalog should point to the component’s license and reviewed use, including relevant notices or source obligations. A label such as permissive is too broad to carry the record alone.
GitHub distinguishes default copyright restrictions from public-platform viewing and forking rights. The actual material and terms must be examined before commercial reuse. Licensing a repository.
A catalog entry can say that an identified use was reviewed under recorded assumptions. It should not say that the component is legally safe for every future integration.
Changes to modification, packaging, delivery, dependencies, or terms can reopen the rights decision. Link to the licensing chapter for the broader orientation, while preserving the actual project’s review record separately.
The catalog’s role is to make obligations findable and current. It cannot grant rights or replace qualified review when the facts are consequential or uncertain.
Name an owner and a backup path
A reusable component creates shared reliance. Someone needs to notice relevant changes, review updates, and respond when consumers encounter failures.
Ownership should be explicit even in a small team. It can be a role rather than a permanent individual, but the current responsible person must be discoverable. The record should also explain what happens when they are unavailable.
The owner need not solve every application issue. They maintain the capability contract and shared mechanism; application owners remain responsible for their use and customer promise.
A component with no owner can still be documented as an unmaintained candidate. It should not quietly become a preferred shared dependency because its code is convenient.
The app-registry chapter tracks deployed applications and lifecycle. The component catalog tracks reusable mechanisms and their consumer relationships. Keeping those scopes distinct avoids turning one record into an overloaded inventory of everything.
Make updates a reviewable event
A new version should identify what changed, which consumers are affected, what checks ran, and whether the capability contract changed.
An implementation update can preserve the contract while improving an internal mechanism. A contract update can change what consumers may rely on. The second deserves more explicit coordination because applications may depend on the earlier behavior.
Use representative contract cases before promotion. Include important exceptions, not only the happy path. Preserve a known working state and understand whether data changes affect rollback.
A catalog entry should state the update policy: how relevant releases or advisories are noticed, who decides to adopt them, and which checks are required. The policy can be lightweight while remaining concrete.
Avoid automatically updating every consumer merely because the catalog marks a version current. The consumer’s environment and data conditions may require its own check. Shared evidence can reduce work without erasing local responsibilities.
Record limits where developers will see them
A limitation buried in a long review can be missed during reuse. Put the consequential limits beside the contract and quick-start reference.
For the hypothetical normalization capability, the record might say that ambiguous identifiers are flagged, not resolved; input beyond the supported size is rejected; and customer review remains required before live import.
The limit should describe behavior, consequence, and action. “Use cautiously” is too vague. “Do not call the publishing API from this read-only capability” is a concrete boundary.
If the limitation changes, revise the record and affected tests. Preserve the history needed to understand why consumers behave differently across versions.
These limits also help AI coding agents. An agent reading the catalog should receive the same contract and restrictions a developer needs. It should not infer new authority from the fact that the component is available.
Avoid the catalog that nobody trusts
A catalog can become stale when entries have no owners, every status says approved, and links point to old artifacts. Developers then search around it, and the catalog loses its purpose.
Keep records current through events rather than arbitrary ceremonial reviews. A release, advisory, contract change, new consumer, or observed failure can trigger the relevant update. A review date helps identify age but does not establish validity by itself.
Remove or mark superseded entries when the mechanism is retired. Preserve the old decision where history matters, while making the current recommendation clear.
Ask a new developer to use the catalog for a real task. Can they find the right capability, understand its limits, and identify the evidence they need? Their difficulty can reveal missing vocabulary or an unhelpful structure.
Keep a short record of those useful moments: the reused test, the avoided duplicate, or the limitation that changed scope. Such observations provide a better basis for improving the catalog than counting entries. The catalog should save a repeated decision or prevent a specific mistake. If it does neither, simplify it before adding more infrastructure.
Use automation for consistency
A small validator can check required fields, broken evidence links, missing owners, duplicate identities, and consumer references. It can flag a catalog entry whose implementation version differs from the installed version.
These checks are consistency tools. They do not establish that the component fits a task, that a rights conclusion is correct, or that a security review is complete.
Keep the validator aligned with actual needs. Requiring dozens of fields for a tiny utility can discourage maintenance. A component with broad authority or multiple consumers may deserve a richer record than a harmless formatting helper.
An automated suggestion to reuse a component should show the contract and scope. It should not silently add dependencies or promote a catalog entry into production without the application’s review process.
The proposed catalog workflow here has not been implemented or measured. An actual pilot would compare it with the team’s existing search and handoff process, using independently observed reuse decisions and time or error outcomes.
Walk through a reuse request
Suppose a hypothetical second app needs to normalize uploaded supplier data. The developer finds the catalog entry and compares the new task with the existing contract.
The current capability accepts specified columns and preserves original identifiers. The new app receives a different identifier scheme and wants to resolve missing matches automatically. The catalog has helped identify a partial fit, not granted approval for the larger promise.
The developer can reuse the parser and structural checks while keeping the new identity-resolution question separate. They might prepare representative harmless cases and ask the capability owner whether the contract should expand or the new app should retain an additional local step.
If the expansion is justified, the owner revises the contract, tests, limitations, and consumer impact. If it is not, the second app uses the component only within the existing scope. Either decision can be recorded without creating a duplicate general-purpose normalization module.
Now suppose the new app needs live inventory writes. That authority does not follow from the read-only transformation capability. The application requires its own permission and write-safety design. The catalog should make the boundary visible enough that convenience does not silently broaden access.
This example shows why a component name alone is insufficient. The contract and limits guide the reuse conversation and reveal the new work the application must own.
Evaluate whether the catalog earns its maintenance
A proposed catalog pilot should compare a small current record set with the team’s existing search and handoff method. The question is whether the catalog helps developers find and use the right scoped capability, not whether they like its design.
Define observable outcomes before the pilot: a developer identifies the suitable component, recognizes a consequential limitation, finds the relevant evidence, and names the owner without relying on the creator’s memory. Time can be recorded where observable, but correctness comes first.
Include a counterexample in which the most obvious component is unsuitable. A useful catalog should help the developer reject that reuse or narrow it. Testing only successful matches rewards a catalog that encourages indiscriminate adoption.
Keep later tasks outside revision while adjusting the record format. Once a task is used to improve the catalog, it becomes development evidence. Fresh tasks are needed before claiming that the format transfers.
Stop if the catalog duplicates an existing reliable inventory without improving the decisions, or if its maintenance burden exceeds the benefit observed within the authorized pilot. Simplify fields before adding a service or search system.
No such pilot has been executed here. Real reuse tasks, independent expected decisions, and recorded outcomes would be required to support a local effectiveness finding. The proposed framework remains an operating aid whose value must be checked in use.
What Would We Do at Salars?
A proposed Salars catalog would start with a few real capabilities already needed by software candidates. Supplier-file normalization, reviewed listing preparation, or task-specific validation could be candidates only if actual evidence and ownership exist.
Records would live in the existing repository convention and link to contracts, dependency identities, rights reviews, executed checks, limits, owners, and consumers. Documentation claims would remain distinct from tested behavior.
The team would add a small consistency check only where it prevents a concrete mistake, such as a missing owner or stale version reference. New app reuse would inspect the scope before relying on the entry.
Updates, new consumers, advisories, and failures would reopen affected decisions. No catalog effectiveness, accepted component, or saved development time is claimed in this article.
A useful catalog lets the next builder begin with knowledge the team can substantiate. That knowledge remains valuable because its limits and owner travel with it.
Sources
- GitHub: Dependency graph, official inventory sources and metadata.
- GitHub: Licensing a repository, official rights context.
Sources inspected October 7, 2026. Catalog fields, workflow, illustrative entry, and Salars application are proposals. No catalog pilot or component approval was executed for this article.
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