docs-workflow-tech-review

name	docs-workflow-tech-review
description	Technical accuracy review with optional code-learner claim validation. Iteration logic owned by the orchestrator.
argument-hint	<ticket> --base-path <path> [--repo <path>]... [--iteration <N>]
allowed-tools	Read, Write, Glob, Grep, Edit, Bash, Skill, Agent, WebSearch, WebFetch

Technical Review Step

Step skill for the docs-orchestrator pipeline. Performs a single review pass; the iteration loop is driven by the orchestrator.

Execution

1. Prepare

python3 ${CLAUDE_SKILL_DIR}/scripts/prepare_review.py <args>

Pass args unquoted. The script emits JSON on stdout. Key fields used below: ticket, output_dir, output_file, claims_file, code_analysis_dir, repo_path, additional_repo_paths, additional_code_analysis_dirs, has_repo, has_code_analysis, source_files_block, prior_findings_file. Stop on non-zero exit.

2. Claim validation (conditional)

Skip this section entirely if has_code_analysis is false. Proceed to step 3.

2a. Extract claims

Agent:
  description: "Extract technical claims from docs for <ticket>"
  prompt: |
    Extract verifiable technical claims from documentation draft files.

    <source_files_block>

    Read all .adoc and .md files from the source location above.
    Skip files whose names match release-notes patterns: `new-features-and-enhancements*`,
    `release-notes*`, `known-issues*`, `fixed-issues*`, `deprecated-removed*`. These contain
    announcement-level statements not verifiable against source code.
    For each remaining file, extract factual claims verifiable against code: function names, signatures, parameters, behavior descriptions, config options, defaults, API endpoints, CRD kinds, class names, return types, CLI flags, subcommands.

    Write the claims list to: <output_dir>/claims-list.json

    Format: JSON array of objects with fields: id, text, file, line.

    After writing, print ONLY: Written <output_dir>/claims-list.json

After the agent completes, prepare claims for validation:

python3 ${CLAUDE_SKILL_DIR}/scripts/prepare_claims.py \
  --claims-list <output_dir>/claims-list.json \
  --output-dir <output_dir> \
  --prior-validation <claims_file>

Emits batch-summary JSON: {total_claims, batch_count, batches: [{sanitized, file, count, claims_file}]}. If batch_count is 0, skip 2b.

2b. Dispatch code-questioner agents

For each batch, dispatch a code-questioner agent. Launch ALL in a single message (parallel).

Agent:
  subagent_type: docs-skills:code-questioner
  description: "Verify <count> claims from <file>"
  prompt: |
    Verify documentation claims from <file> against the source code.

    Read the claims to verify from: <claims_file>
    It is a JSON array of objects with fields: id, text, file, line.

    Read the learn-code analysis data from: <code_analysis_dir>/
    Files available: detection.json, registry.json, ONBOARDING.md, summaries/, relationships/

    REPO_PATH: <repo_path>

    OUTPUT_FILE: <output_dir>/batch-verdict-<sanitized>.json

    Write a JSON array of verdicts — one entry for EVERY claim (keyed by its `id`):
    [{"claim_id": "<id>", "claim_text": "<text>", "verdict": "supported|partially_supported|unsupported|no_evidence_found", "evidence": "<1-2 sentences with file:line refs>"}]

    IMPORTANT: You must produce a verdict for ALL claims. Do not skip any.
    After writing, print ONLY: Written <OUTPUT_FILE>

2b-verify. Verify batch verdicts before merging

The batch_count from prepare_claims.py tells you how many verdict files to expect.

EXPECTED_BATCHES=<batch_count from prepare_claims.py output>
ACTUAL_BATCHES=$(ls ${OUTPUT_DIR}/batch-verdict-*.json 2>/dev/null | wc -l)
echo "Batch verdicts: ${ACTUAL_BATCHES}/${EXPECTED_BATCHES}"

HARD GATE — do NOT proceed to step 2c (merge verdicts) until ACTUAL_BATCHES equals EXPECTED_BATCHES. If agents are still running, wait. After all agents have returned, if verdict files are missing, log which batch sanitized names are missing — merge_verdicts.py assigns no_evidence_found fallback verdicts for claims in missing batches, so proceed to 2c after logging.

2c. Merge verdicts — set HAS_CLAIMS=true after this completes

python3 ${CLAUDE_SKILL_DIR}/scripts/merge_verdicts.py \
  --claims-list <output_dir>/claims-list.json --output-dir <output_dir> \
  --claims-file <claims_file> --summary-file <output_dir>/validation-summary.md \
  --code-analysis-dir <code_analysis_dir>

3. Dispatch reviewer

Use the Agent tool with subagent_type: docs-skills:technical-reviewer and run_in_background: false (the orchestrator must wait for the reviewer to finish before verifying output).

Before dispatching: If a prior review.md exists at <output_file>, delete it. This prevents the reviewer from reading stale findings from a prior iteration:

rm -f <output_file>

Prompt:

Perform a technical review of the documentation drafts for ticket <ticket>. <source_files_block> Review all .adoc and .md files. Follow your standard review methodology. Save your review report to: <output_file>

The report must include an Overall technical confidence: HIGH|MEDIUM|LOW line and a Severity counts: critical=N significant=N minor=N sme=N line.

After writing the report file, do NOT print the review contents. Print ONLY these three lines:

Written <output_file> Overall technical confidence: HIGH|MEDIUM|LOW Severity counts: critical=N significant=N minor=N sme=N

[if iteration >= 2] Prepend this paragraph to the prompt, before "Perform a technical review":

This is a re-review (iteration 2+). A prior review found issues and fixes have been applied to the source files. Base your assessment on the current file content. If a review.md already exists at the output path below, do NOT read it — it will be overwritten.

[if iteration >= 2 AND prior_findings_file is not null] Also prepend, immediately after the re-review paragraph:

Prior findings: Read <prior_findings_file> for a summary of what the previous iteration found, grouped by severity. For each prior finding, determine whether it is now FIXED or still PERSISTS, and state that status in your report. You may also report NEW findings, but prioritize verifying the prior findings over hunting for new minor issues.

[if has_prior_validation is true] Also prepend, after the re-review paragraph (or as the first prepended paragraph if iteration == 1):

A prior claim-validation pass produced verdicts in <claims_file>. Use those verdicts to calibrate your review — claims marked unsupported deserve extra scrutiny, while supported claims are lower risk.

[if has_repo] Append: Source code repository is available at <repo_path>.

[if additional_repo_paths non-empty] Append: Additional source code repositories: <list paths> and Additional code-learner analyses: <list additional_code_analysis_dirs>

[if HAS_CLAIMS] Append:

Claim Validation Evidence

Read the validation summary from: <output_dir>/validation-summary.md Full results: <claims_file> Verdicts: unsupported = likely inaccurate (critical/significant), no_evidence_found = may need SME, partially_supported = identify wrong part, supported = lower risk.

4. Verify and write sidecar

Verify <output_file> exists and contains an Overall technical confidence: line. If missing, treat as step failure.

python3 ${CLAUDE_SKILL_DIR}/scripts/write_step_result.py \
  --ticket "<ticket>" \
  --review-file "<output_file>" \
  --sidecar "<output_dir>/step-result.json" \
  --code-grounded <true if HAS_CLAIMS, else false> \
  --missing-batches "<comma-separated missing batch names from 2b-verify, or empty string if none>" \
  --iteration <iteration number from prepare_review.py output>