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Agentic documentation testing

A complete, worked recipe for evaluating whether AI agents can navigate and correctly use a repository's agentic documentation (CLAUDE.md, AGENTS.md, ai-docs/, …). This is the deep-dive companion to the agentic-docs quickstart: it walks the full 7-step analysis methodology, the repo-type taxonomy, the generation: synthetic block, the consulted_docs + LLM rubric judge pairing, and the workspace_mode: repo + answer-key permissions.deny guard — end to end.

What this recipe produces

A prompt-mode eval.yaml (no skill under test) with a generation: synthetic block, an LLM-authored dataset of documentation questions, and a scored report that shows whether agents found and read the right docs — rather than answering from training memory.

When to reach for it

Prompt mode with workspace_mode: repo tests a capability — "can an agent operate from my docs alone?" — not a skill's output. It answers:

Capability Question it answers Builtin seed
Doc navigation Can the agent find the right file for a question? docs/navigation
Pattern application Can it create content following documented patterns? docs/authoring
Constraint compliance Does it reject approaches the docs forbid? docs/anti-pattern
API usage Can it explain an API/component with correct examples? docs/component-usage
Architecture Can it explain how components interact? docs/architecture

The five seeds map one-to-one to the builtin generation prompts under agent_eval/prompts/docs/. See the builtin prompts reference.

The end-to-end flow

flowchart LR
    A["/eval-analyze --prompt recipe"] --> B["eval.yaml<br/>generation: synthetic"]
    B --> C["/eval-dataset"]
    C --> D["cases + annotations"]
    D --> E["/eval-run --model sonnet"]
    E --> F["consulted_docs +<br/>LLM rubric judges"]
    F --> G["report.html"]
# 1. Analyze the docs → prompt-mode eval.yaml
/eval-analyze --prompt examples/openshift-agentic-docs.md

# 2. Generate synthetic cases from seeds + context
/eval-dataset

# 3. Run the agent against the docs
/eval-run --model sonnet

# 4. Read the scored report
open eval/runs/<run-id>/report.html

The 7-step analysis methodology

/eval-analyze --prompt runs a recipe prompt that drives the agent through a fixed sequence. The bundled OpenShift recipe lives at examples/openshift-agentic-docs.md; copy it and swap the terminology to target another domain (see examples/README.md and the custom analysis recipe cookbook).

Step What analysis does Feeds into
1. Discover doc structure Find the entry point (CLAUDE.md/AGENTS.md), enumerate doc areas, sample 3-5 docs per area Step 3
2. Classify repo type Label the repo A / B / C (below) test focus + seeds
3. Extract generation context Capture documentation_structure, constraints, apis, components from actual paths generation.context
4. Pick generation seeds Choose builtin docs/* prompts + counts per type generation.seeds
5. Write dataset schema Describe what each case's input.yaml/annotations.yaml holds dataset.schema
6. Select judges Pair mechanical consulted_docs with semantic LLM judges judges
7. Emit eval.yaml Assemble prompt-mode config + workspace_mode: repo + permissions.deny the config

Use actual paths, not placeholders

Every field name, file pattern, and directory path in the generated config must come from reading real files in Step 1. The recipe explicitly forbids invented placeholders — the discovery commands (find, grep) exist to ground the config in the repo's real structure.

Step 1 — discovery commands

The recipe starts from concrete shell probes; these are the ones it runs:

# 1.1 Entry point at the repo root
find . -maxdepth 1 \( -name "CLAUDE.md" -o -name "AGENTS.md" \)

# 1.2 Documentation areas
find . -type d \( -name "ai-docs" -o -name "docs" -o -name "guidelines" \) | head -10
find ai-docs -name "*.md" | head -30

Step 2 — the repo-type taxonomy

Classification decides both the test focus and which seeds get generated:

RFCs, ADRs, enhancement proposals. Focuses on process and has explicit constraints ("must use X", "avoid Y"). Example: openshift/enhancements.

  • Test focus: process adherence, pattern application, constraint enforcement
  • Typical seeds: navigation, authoring, anti-pattern

Component docs: APIs, CRDs, libraries with usage examples (YAML/code snippets). Example: openshift/machine-config-operator, operator repos.

  • Test focus: API usage, example accuracy, architecture understanding
  • Typical seeds: navigation, component-usage, architecture

Pure documentation — no code or proposals. Focuses on information retrieval through hierarchy and search. Example: standalone docs sites.

  • Test focus: navigation, search, information accuracy
  • Typical seeds: navigation

Step 3-4 — the generation: synthetic block

Prompt-mode configs source cases from synthetic generation: an LLM authors cases from repository knowledge (context, from Step 3) using builtin generation prompts (seeds, from Step 4). The context is injected into every generation prompt.

eval.yaml (generation block, Type A)
generation:
  strategy: synthetic
  context:                       # Step 3 — repository knowledge
    documentation_structure:
      entry_point: CLAUDE.md
      areas:
        - path: ai-docs/workflows/
          topics: [enhancement-process, testing-workflow]
        - path: ai-docs/practices/
          topics: [api-evolution, security, testing]
    constraints:                 # 3.2 — for Type A
      - rule: "New APIs must start with v1alpha1"
        documentation: ai-docs/practices/development/api-evolution.md
        wrong_approach: "Starting with v1 API for stability"
  seeds:                         # Step 4
    - category: navigation
      builtin: docs/navigation
      count: 2
    - category: authoring
      builtin: docs/authoring
      count: 1
    - category: anti-pattern
      builtin: docs/anti-pattern
      count: 3                   # one per major constraint

For a Type B repo, capture apis/components in context and swap in the component-usage seed instead:

context:
  apis:
    - name: MachineConfig
      documentation: ai-docs/domain/machineconfig.md
      example_type: yaml
    - name: KubeletConfig
      documentation: ai-docs/domain/kubeletconfig.md
      example_type: yaml
seeds:
  - category: navigation
    builtin: docs/navigation
    count: 2
  - category: component-usage
    builtin: docs/component-usage
    count: 3                     # one per major API

category is derived, never declared

Each seed's category is stamped onto every generated case as annotations.category. The category list comes from the cases — you never maintain a separate list. seeds and context apply only to the synthetic strategy. See the generation reference.

List what builtin prompts are available before choosing seeds:

python3 skills/eval-dataset/scripts/list_prompts.py

Step 5-7 — the prompt-mode config

The rest of the config is what makes this prompt mode rather than skill mode, plus the two guards that make in-repo navigation testable and honest.

eval.yaml (execution + runner + dataset)
execution:
  mode: case
  prompt: "{{ input.prompt }}"   # prompt mode — no skill wrapper

runner:
  type: claude-code
  workspace_mode: repo           # required — see below
  effort: medium

models:
  skill: claude-sonnet-4-5       # the agent under test (or pass --model)
  judge: claude-opus-4-6         # LLM rubric judges
  subagent: claude-sonnet-4-5    # prompt-mode reads are usually delegated

dataset:
  path: eval/dataset/cases       # generation is its own block; dataset keeps path + schema
  schema: |
    Each case contains:
    - input.yaml with a 'prompt' field (the user's question)
    - annotations.yaml with 'category', 'expected_files' (doc paths for the
      consulted_docs judge), and 'expected_mentions' (keywords)

workspace_mode: repo — the navigation prerequisite

Documentation navigation only works if the agent can see the whole repository tree at its real paths. The default isolated workspace provides only input.yaml plus symlinks to root-level files, so it cannot exercise ai-docs/ navigation.

Set workspace_mode: repo for agentic-docs evals

Omit it for isolated capability tests; set it whenever the test depends on the agent walking the real directory tree. See the runner reference.

The answer-key permissions.deny guard

Once the whole repo is visible, so is the answer key — eval.yaml, eval.md, and the generated cases under eval/ all contain the expected files and correct answers. Without a guard, an agent could simply read annotations.yaml instead of navigating the docs. Block those paths:

eval.yaml (permissions)
permissions:
  deny:
    - "Read(eval/**)"      # the dataset (input + annotations) and runs
    - "Read(eval.yaml)"    # judges, seeds, expected_files
    - "Read(eval.md)"      # the human-readable eval description
    - "Read(tmp/**)"       # scratch that may leak intermediate state

The guard only matters with workspace_mode: repo

In the default isolated workspace the answer key isn't present, so the deny rules are effectively a no-op there. They are load-bearing precisely when workspace_mode: repo exposes the repo. See the permissions reference.

Step 6 — the judge pairing

The signature pattern is a mechanical check paired with a semantic one.

The mechanical judge is the builtin consulted_docs (source). It extracts Read (and optionally Grep) tool calls from the trace and checks coverage against each case's annotations.expected_files:

eval.yaml (judges)
judges:
  - name: docs_consultation
    builtin: consulted_docs
    description: Verifies the agent read the expected documentation files
    arguments:
      min_coverage: 0.8           # fraction of expected_files that must be read
      include_grep: true          # count Grep calls as file reads
      include_subagents: true     # prompt mode delegates reads to Explore subagents
      preloaded_files:            # files Claude Code auto-loads into context
        - CLAUDE.md
        - AGENTS.md
    if: "annotations.get('category') == 'navigation'"

  - name: navigation_success
    prompt: |
      Expected files: {{ annotations.expected_files }}
      Did the agent find and read the correct docs, or answer from memory?
      Tool usage trace:
      {{ tool_trace }}
      Agent's response:
      {{ conversation }}
    if: "annotations.get('category') == 'navigation'"

  - name: rejects_wrong_approach
    prompt: |
      The user requested an approach the docs forbid.
      Did the agent reject it and cite the correct guidance?
      Response: {{ conversation }}
    if: "annotations.get('category') == 'anti-pattern'"

consulted_docs arguments:

Argument Default Effect
min_coverage 0.8 Fraction of expected_files that must be read to pass
match suffix Path match strategy: suffix, exact, or basename
include_subagents true Count reads from Explore/subagent events (prompt mode delegates most reads)
include_grep true Count Grep tool calls as having consulted the file
preloaded_files [] Files auto-loaded into context (e.g. CLAUDE.md) — count as consulted without a Read

Why pair mechanical + semantic

consulted_docs proves the agent touched the right files; the LLM rubric judge proves it actually navigated rather than answering from training memory. Both reference the standard field annotations.expected_files — not expected_paths or other variants.

if: scoping vs check: code

In a judge's if: condition, annotations is implicit — annotations.get('category') == 'navigation'. Inside a check: code block you must instead use outputs.get("annotations", {}). See the judges reference and judges concept.

The generated dataset

/eval-dataset reads the generation block, has the LLM author cases per seed, and stamps annotations.category on each:

eval/dataset/cases/
├── case-001-navigation/
│   ├── input.yaml          # { prompt: "How do I create an enhancement proposal?" }
│   └── annotations.yaml    # { category: navigation, expected_files: [...], expected_mentions: [...] }
└── case-002-anti-pattern/
    ├── input.yaml
    └── annotations.yaml    # { category: anti-pattern, expected_rejection: true, ... }

Regression thresholds

Gate the run on per-judge minimums so a docs regression fails CI:

eval.yaml (thresholds)
thresholds:
  docs_consultation:  { min_pass_rate: 0.9 }
  navigation_success: { min_mean: 3.5 }

See the thresholds reference.

Where to go next