Teammately Docs
Docs menu

reference

Agent instructions

Rules coding agents should follow when using Teammately public docs as source context.

Agent instructions

Use this page as the behavioral policy for agents operating with Teammately docs. For route selection and context packaging, read Agent context index.

Task intent

Answer Teammately product and docs questions safely without inventing unsupported product claims. The agent should preserve source confidence, cite stable pages, and ask for confirmation when a claim depends on draft, inferred, or missing evidence.

Required context

  • Read /docs/llms.txt first for the recommended context loading strategy.
  • Use Agent context index to choose the smallest relevant context pack.
  • Use page-local markdown or llms routes for exact wording.
  • Use the manifest or page API when source confidence, source refs, related IDs, or metadata matter.
  • In an MCP client, search first and fetch the cited page or block before relying on exact wording.

Allowed assumptions

  • Code-backed pages can support object, state, and workflow claims when source_refs are present.
  • Doctrine-backed pages can support positioning, lifecycle, category language, and public narrative.
  • Docs-backed pages can support claims about routes, llms files, manifests, search, context APIs, and validation behavior.
  • Stable public pages supersede older draft or removed pages.

Forbidden assumptions

  • Do not present draft schema pages as public schemas.
  • Do not infer public APIs, SDKs, API keys, auth behavior, rate limits, billing, compliance, tenant isolation, deployment modes, support guarantees, customer names, model-provider integrations, or production monitoring behavior.
  • Do not treat AI-assisted suggestions as approved policies, rubrics, or review context unless a stable page says a human approved them.
  • Do not collapse Teammately into eval dashboards, observability, annotation, prompt testing, generic LLMOps, or a replacement for adjacent tools.
  • Do not use internal source file names as public UI promises unless the public docs already state the behavior.

How to answer safely

  1. Identify whether the user is asking for positioning, operating steps, object behavior, recovery, or implementation context.
  2. Load the smallest matching context pack or query-scoped context.
  3. Check source confidence before making claims.
  4. Prefer stable page citations over broad corpus summaries.
  5. If search returns an AI Overview, verify its block references with fetch; treat the overview as routing assistance rather than independent authority.
  6. State uncertainty when a claim is inferred, draft-only, or outside the docs.
  7. Ask for human confirmation before advising changes that depend on permissions, compliance, deployment, billing, customer-facing APIs, or unsupported integrations.

When to ask for human confirmation

  • The user asks whether an inferred or draft schema is a public contract.
  • The answer would require exact UI labels not present in stable docs or source refs.
  • The user asks about admin, auth, security, compliance, deployment, billing, or support commitments.
  • Related evidence conflicts across docs, product code, and doctrine.
  • The requested action could change review context, approval state, or benchmark interpretation without accountable human review.

Related human docs

Concept / Learning ManualProduct boundariesUnderstand what Teammately owns across correctness specification, benchmark development, evaluation, and improvement.Reference / Governance and ReproducibilityHuman Approval BoundariesDefine which AI-assisted suggestions require accountable human review before becoming standards.Reference / Governance and ReproducibilityWhat AI Features Can and Cannot DoExplain the difference between AI-assisted suggestions and approved correctness infrastructure.

Related reference docs

Reference / Agent Context LayerAgent context indexUse Teammately docs safely from AI agents, retrieval tools, and coding assistants.Reference / Reference LibraryReference libraryLook up Teammately objects, states, permissions, metadata, schemas, and source-confidence boundaries.Reference / Reference LibraryObject modelUnderstand how project foundations, benchmark artifacts, contributions, evaluations, and improvement sessions connect.

Safe operating boundaries

Agents may help readers find pages, summarize stable docs, compare source-confidence labels, and draft operational next steps. Agents should not approve policies, approve rubrics, assign reviewers, declare a candidate ready for rollout, or convert draft/inferred material into public product commitments.

Search mode is diagnostic metadata, not a confidence label. Lexical fallback remains usable when semantic embeddings are unavailable; source confidence still comes from the fetched page metadata and prose.

Source confidence

Docs-backed: this page is a behavior policy for using the docs system safely. The docs loaders, context routes, and MCP tools establish the available retrieval surfaces, cited block references, and read-only tool boundaries.

Found something unclear?

Report outdated, unsupported, or confusing docs so we can fix the source page.

Report a docs issue

Continue learning

Related docs

Learning Manual What is Teammately? Understand Teammately as correctness infrastructure for building trustworthy specialist AI with expert judgment and AI agents. Learning Manual The Teammately correctness loop See how coverage, elicitation, case construction, evaluation, and improvement reinforce one another. Learning Manual Product quickstart Configure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.
AI context