claude-code
4685c8db84
Generated sections (`<!-- generated by … -->` up to the next heading) are no longer checked by the writing rules, and the model and Codex are told to report nothing about them. See docs/prs/. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Reviewed-on: #122 Co-authored-by: claude-code <claude-code@javagil.de>
257 lines
11 KiB
TOML
257 lines
11 KiB
TOML
# werkrevisor operator config for the werkprobe trial repository: cheap and small.
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# A PR that changes this file is still reviewed with the deployed copy; Werkator passes it in WERKREVISOR_CONFIG_TOML.
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[model]
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# The trial chose Sonnet: gemini-3.8-flash missed an obvious resource leak that Sonnet found.
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id = "openrouter/anthropic/claude-sonnet-5.5"
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# Operator-maintained prices in USD per 1M tokens.
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input_price_usd_per_mtok = 2.00
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output_price_usd_per_mtok = 10.00
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# Longer prompts degrade the review even within the context window of a frontier model.
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# The budget derives a lower input limit: 2 attempts × (input × 2.00 + 4000 × 10.00) / 1M ≤ 0.48 USD gives 100000 tokens.
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# A review with one call costs at most about 0.24 USD.
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max_context_tokens = 150000
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# Includes reasoning tokens.
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max_output_tokens = 4000
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reasoning_effort = "low"
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timeout_seconds = 120
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max_retries = 1
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# Anthropic counts more tokens than o200k_base; calibrate with prompt_tokens / counted_raw_tokens of the usage records.
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# On werkrevisor PR#10 the factor 1.15 gave 49841 tokens, Anthropic reported 65792: 1.15 × 65792 / 49841 ≈ 1.52.
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token_count_factor = 1.55
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[budget]
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max_usd_per_review = 0.48
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min_key_remaining_usd = 0.50
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[size]
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# Changed lines and files outside the docs; above them a human looks at the PR (YELLOW).
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max_changed_lines = 800
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max_files = 30
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[categories]
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docs = ["**/*.md", "doc/**", "docs/**"]
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tests = ["src/test/**", "tests/**", "test/**"]
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main = ["src/main/**", "src/**"]
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api_spec = ["**/api-definition/**", "**/openapi*.yaml", "**/openapi*.yml"]
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auto_approvable = [
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"docs_only", "tests_only_added", "docs_and_tests", "refactoring", "feature_with_tests", "harmless_only",
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"mechanical_only", "mixed",
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]
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[hard_yellow]
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# Only secrets stay hard; Jev judges security, the build and the CI, see [path_judge].
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paths = ["**/*secret*", "**/*.pem", "**/*.key", ".env*"]
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judged_paths = [
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".gitea/**", ".github/**", ".gitTally*", ".werkator*", "Dockerfile*", "**/deploy/**", "**/infra/**", "**/ci/**",
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"build.gradle*", "settings.gradle*", "gradle/**", "package.json", "package-lock.json",
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"pyproject.toml", "requirements*.txt", "uv.lock",
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]
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secret_scan = true
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# Test data holds fake passwords; private keys and the real key formats stay flagged there too.
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secret_scan_exclude = ["src/test/**"]
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[pr_doc]
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# An open question in a changed PR document gives RED before any model call; TEMPLATE*.md and README.md never count.
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globs = ["doc/PR/*.md", "docs/prs/*.md"]
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open_question_headings = ["Open Questions", "Offene Fragen"]
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[review]
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extra_instructions = """
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Write all findings in English.
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The code-review procedure in AGENTS.md is for human reviewers and does not apply to this review:
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it edits no files, so write each finding as plain text without FIXME markers.
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Judge whether PR documentation (doc/PR/*.md, docs/prs/*.md) is clear and correct.
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werkrevisor itself checks line length, sentences per line and sentences per paragraph of the docs;
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never report these rules, not even as a style remark.
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A tool writes each section of a doc from a comment `<!-- generated by … -->` up to the next heading;
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report nothing about such a section.
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For documentation, report only bloated or mannered prose (filler, narration of the implementation),
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unclear statements, and contradictions between the PR document and the code.
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Report only concrete problems with a realistic impact.
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The user prompt contains, after the diff, the full new content of every changed file except the documentation;
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documentation appears only in the diff.
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Check each changed line against constraints, invariants and conventions stated elsewhere in these files,
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e.g. in header comments, and report a violation as a key issue.
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Review the diff; use the full files only as context.
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Start every issue_header with the severity of the issue: [high], [medium] or [low].
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[high]: wrong behavior, data loss, a security problem or a broken contract in a realistic scenario.
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[medium]: a defect with limited impact, or a missing test for changed behavior.
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[low]: cosmetics, wording, log noise or style.
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"""
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max_findings = 3
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# Without Jev, one high finding or this many medium findings give ORANGE, another medium finding YELLOW;
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# with Jev, finding_real_min and finding_severity_min of the host config decide.
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orange_min_medium = 2
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# The complete new version of each changed file follows the diff in the prompt.
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# 160000 chars are about 71000 of the 100000 input tokens, counted with token_count_factor = 1.55;
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# a larger prompt gets excerpts instead, then the diff only.
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full_files = true
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max_full_file_chars = 80000
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max_full_files_chars = 160000
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repo_context_files = []
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[writing]
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# Countable rules of the writing style in AGENTS.md, checked on the changed lines of Markdown docs and PR documents.
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# A violation is a hint in the comment and never blocks; the model still reviews the PR.
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enabled = true
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max_line_length = 128
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max_sentences_per_paragraph = 5
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# Empty means the docs category.
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globs = []
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[mechanical]
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# A changed file whose every changed line is an import, a whitespace change or a rename does not go to the model.
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enabled = true
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# Indentation or line breaks carry meaning in these files, so a whitespace change there is never mechanical.
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whitespace_significant = ["*.py", "*.yml", "*.yaml", "Makefile", "makefile", "GNUmakefile", "*.mk", "*.md",
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"*.haml", "*.pug", "*.coffee", "*.nim", "*.fs"]
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# Built-in import rules exist for java, kt, kts, ts, tsx, js, jsx, mjs, go and rs.
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# A [mechanical.imports] table adds or replaces the line regexes of a file extension; an empty list switches it off.
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[gate]
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# Branch protection requires one approval from {bot, human_reviewers}; see the README.
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yellow_gate = "approval"
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human_reviewers = ["mi"]
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assessment_context = "ai-review/assessment"
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# Only a PR whose title changed from "WIP: " to one without gets a review; the bot then schedules the merge,
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# so a GREEN PR merges at once and a YELLOW or ORANGE one with the human approval.
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require_wip_transition = true
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automerge = true
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[db_schema]
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# "never": every DB schema change needs a human; "jev" fits only a repository that deploys through stages.
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judge = "never"
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[engine]
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# "llm": the LLM review alone; "classifier": Jev alone, no LLM; "combined": Jev, then the LLM with Jev's hints.
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mode = "combined"
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# Jev decides with [path_judge] scope = "all"; the model only advises.
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model_role = "advises"
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[classifier]
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endpoint = "https://api.typesafe.ai/v1/systemone"
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model = "jev-latest"
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# Operator-maintained price in USD per 1M input tokens; Jev's output is free.
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input_price_usd_per_mtok = 0.042
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# Jev reads about 32000 tokens of state; a larger unit is not sent and makes the review YELLOW.
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max_state_chars = 60000
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max_units = 80
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timeout_seconds = 20
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pr_doc_globs = ["doc/PR/*.md", "docs/prs/*.md"]
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# Choice and score answers below this confidence do not count for the verdict; calibrate it with tools/jev-eval.
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min_confidence = 0.3
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# Every question asks for a problem: a value above red_above gives ORANGE, above yellow_above YELLOW.
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# A score's criteria run from "no problem" to "worst problem"; its value is the normalized expected level.
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[[classifier.scenario_questions]]
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name = "hard_to_read"
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type = "score"
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instructions = """
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`scenario` is a Gherkin scenario from a pull-request document; `feature` and `background` give its context.
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Domain experts who do not program approve these scenarios as the specification.
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How hard is the scenario to understand for such a domain expert?
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The text may be German or English.
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"""
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criteria = [
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"A domain expert understands the scenario at the first reading.",
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"A domain expert understands it, but must read some steps twice.",
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"A domain expert needs a developer to explain parts of it.",
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"A domain expert cannot tell what the scenario requires.",
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]
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red_above = 0.75
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yellow_above = 0.5
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[[classifier.scenario_questions]]
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name = "more_than_one_aspect"
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type = "noul"
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instructions = """
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Does the Gherkin scenario in `scenario` specify more than one independent business rule or aspect,
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so that it should be split into two or more scenarios?
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Variants of the same rule that differ only in their data count as one aspect.
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A second When with its own Then that checks a different case counts as a second aspect.
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"""
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red_above = 0.85
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yellow_above = 0.6
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[[classifier.scenario_questions]]
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name = "implementation_details"
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type = "noul"
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instructions = """
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Does the Gherkin scenario in `scenario` describe implementation details instead of business behaviour,
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for example class, method, table or column names, SQL, internal HTTP headers or JSON internals?
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Naming the public API endpoint, the HTTP method, the response status and the business fields
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counts as observable behaviour, not as an implementation detail.
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"""
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red_above = 0.85
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yellow_above = 0.6
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[[classifier.scenario_questions]]
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name = "given_when_then_misused"
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type = "noul"
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instructions = """
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Does the Gherkin scenario in `scenario` misuse Given, When or Then?
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Misuse is: an action or API call in Given, an expected result or check in When,
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an action in Then, or a Then without a preceding When.
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And-steps belong to the keyword above them.
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"""
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red_above = 0.85
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yellow_above = 0.6
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[[classifier.scenario_questions]]
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name = "vague_then"
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type = "noul"
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instructions = """
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Does a Then step of the Gherkin scenario in `scenario` lack a checkable outcome,
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for example "works", "is handled correctly" or "behaves as expected",
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without saying what an observer can check?
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"""
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red_above = 0.85
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yellow_above = 0.6
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[[classifier.section_questions]]
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name = "hard_to_understand"
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type = "score"
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instructions = """
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`section` is a section of a pull-request document that a human reviewer reads before the code.
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How hard is the section to understand for a reviewer who knows the project but not this change?
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Markdown lists, tables, links and code identifiers are normal here.
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The text may be German or English.
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"""
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criteria = [
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"The reviewer understands the section at the first reading.",
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"The reviewer understands it, but must read some sentences twice.",
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"The reviewer must guess what some sentences mean.",
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"The reviewer cannot tell what the section says.",
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]
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red_above = 0.75
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yellow_above = 0.5
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[[classifier.section_questions]]
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name = "slop"
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type = "score"
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instructions = """
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How much of `section` is slop: filler words, mannered or promotional prose,
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narration of the implementation instead of the reasons, or repetition of what other sentences already say?
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Plain lists, tables, links and code identifiers are not slop.
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The text may be German or English.
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"""
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criteria = [
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"No slop: every sentence carries information.",
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"A few filler words or one redundant sentence.",
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"Several sentences are filler, mannered prose, narration or repetition.",
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"The section is mostly filler, mannered prose, narration or repetition.",
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]
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red_above = 0.75
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yellow_above = 0.5
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[path_judge]
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# Jev judges every changed file: only a change that touches security or weakens the checks waits for a human.
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judge = "jev"
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scope = "all"
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[excerpt]
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# An economy model selects the lines of an excerpt; on 4 measured heads it never found less than the fixed window.
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select = "model"
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