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Umfangreiche Erweiterung der Skill-Bibliothek: Neue Skills für Humanisierung (Englisch/PT-BR), Design-Validierung, AI-SEO und Coolify-Deployment inkl. Regelwerke, Presets, Pattern-Referenzen, Testfälle und Automatisierungsskripte. Zusätzliche Skills für Revenue-Centric Design, Pier Cloud, OKF, Lebenslauf- und LinkedIn-Optimierung sowie zahlreiche Referenzdateien, Checklisten und YAML/JSON/Markdown-Templates. Einführung einer vollständigen OpenWiki-Dokumentation mit Architektur-, Domain- und Workflow-Beschreibungen, zentralem Index und automatisierten Updates. Modularer Aufbau, restriktive Lizenzen und umfassende Qualitäts- und Evaluationsmechanismen für alle neuen Inhalte.
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60
.github/skills/loop-architect/references/control-rubric.md
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.github/skills/loop-architect/references/control-rubric.md
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# Control Rubric
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Use this when setting gates, iteration caps, budgets, and stop conditions.
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## Required Guards
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- `loop_control.max_iterations`
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- `gates.*.max_revisions`
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- `loop_control.no_progress.max_stalled_iterations`
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- At least one wall-clock, token, or USD budget cap when external models run.
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The generated Python runner enforces wall-clock caps directly; token and USD
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caps are advisory unless the chosen model CLI exposes accounting that the
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loop operator wires in separately.
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- A stop condition that describes success.
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- A stop condition that describes no-progress or repeated failure.
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## Good Gate Design
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- Plan gate runs before delivery work.
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- Delivery gate runs after each delivery artifact.
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- Programmatic checks run before judge calls when possible.
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- Human checkpoints sit at high-leverage points, usually after plan approval or
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before external egress.
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- Resume happens at gate boundaries unless the user explicitly needs finer
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granularity.
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## Execution Boundary
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- Name where the loop is allowed to modify files: current workspace, branch,
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worktree, throwaway directory, or an external orchestrator workspace.
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- Identify actions with side effects: pushes, PR comments, Slack messages,
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deploys, file deletes, database writes, or vendor sends.
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- Decide whether side-effecting actions require approval, idempotency notes, or
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duplicate-action checks.
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- If the loop may run on a schedule or in parallel, call out the need for an
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external orchestrator with concurrency controls.
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## Failure Behavior
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- Stop immediately when a hard cap is reached.
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- Write the latest state to `loop-workspace/state.json`.
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- Append each meaningful step, decision, check result, and blocker to
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`loop-workspace/run-log.md`.
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- Preserve review notes even when the gate fails.
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- Stop or ask the human when the same blocker repeats for the configured
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no-progress window.
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- Do not let the host keep revising forever.
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## Anti-Patterns
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- No maximum iteration count.
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- A judge gate with no judge.
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- A budget cap in prose but not in `loop_control`.
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- No no-progress detector.
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- A loop that can send duplicate external notifications or repeat destructive
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actions after restart.
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- Scheduled or multi-agent work with no durable orchestrator or concurrency
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story.
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- Human signoff required but no checkpoint.
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- Stop conditions that require subjective self-satisfaction.
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43
.github/skills/loop-architect/references/council-rubric.md
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.github/skills/loop-architect/references/council-rubric.md
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# Council Rubric
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Use this when selecting reviewers and judges.
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## Roles
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`reviewer`
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: Gives notes only. It may improve quality, but it cannot declare a gate clean.
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`judge`
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: Gives a structured verdict. It can be used as a gate `verdict_source`.
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## Selection Guidance
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- Prefer a different model family from the host for blind-spot coverage.
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- Prefer local models such as `ollama` when privacy matters more than judgment
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quality.
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- Prefer a judge for gates that must block progress.
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- Prefer a reviewer for brainstorming, adversarial notes, or tone critique
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where a deterministic pass/fail would be fake precision.
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- Keep council scope small: `plan`, `delivery`, or specific paths.
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## Gate Rule
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`verdict_policy: revise_until_clean` requires `verdict_source` to be either a
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judge member or `human`. A reviewer-only gate can use `fixed_passes`, but it
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cannot honestly claim clean.
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## Judge Rubric Tips
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- Name the artifact being judged.
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- Name the exact criteria IDs.
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- Ask for blocking issues, not general commentary.
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- Require the fenced JSON verdict first or last.
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- Keep the judge prompt short enough that the artifact, not the instruction
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wrapper, dominates the context.
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## Privacy Notes
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Cross-vendor review can send project context to another CLI and vendor account.
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Always name the destination, scope what it receives, apply redaction globs, and
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require consent before the first send.
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42
.github/skills/loop-architect/references/goal-rubric.md
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# Goal Rubric
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Use this when shaping the user's loop goal.
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## Good Goal Shape
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- Names the concrete outcome, not only the activity.
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- Defines the artifact or state that proves the loop finished.
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- Sets scope boundaries: included work, excluded work, and maximum depth.
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- Names context sources the host must gather instead of assumptions it may make.
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- Identifies the user, customer, system, or reviewer who will consume the result.
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## Critique Prompts
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- What would count as done if two competent agents disagreed?
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- Which terms are subjective and need a measurable proxy?
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- What context must be read before the host drafts a plan?
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- What is explicitly out of scope for this loop?
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- Can the goal be split into plan, delivery, and verification artifacts?
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## Anti-Patterns
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- "Improve the project" without a target artifact.
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- "Make it good" without criteria.
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- "Research X" without the decision the research supports.
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- Goals where success depends on information the loop never gathers.
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- Goals that require endless polishing with no stop condition.
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## Better Examples
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Weak: "Make our onboarding better."
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Better: "Produce a 5-step onboarding workflow map for new enterprise users,
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with each step assigned to a product surface, email, human owner, or missing
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capability, and with no unresolved TBDs."
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Weak: "Fix the flaky tests."
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Better: "Identify and patch the root cause of the checkout test flake, prove it
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with 20 local repeats or a CI rerun, and leave a short note explaining the
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failure mode and the verification evidence."
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98
.github/skills/loop-architect/references/model-detection.md
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# Model Detection and Privacy Notes
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Loop-architect detection is intentionally dumb and transparent. It stores
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invocation metadata only, never credentials.
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## Registry
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Default registry path:
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```text
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~/.loop-architect/models.json
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```
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Registry entries should look like:
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```json
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{
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"kiro": {
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"cli": "kiro-cli",
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"invoke": ["kiro-cli", "chat", "--trust-all-tools", "-p"],
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"probe": ["kiro-cli", "--version"],
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"available": true,
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"authed": true,
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"local": false,
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"capabilities": {
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"headless": true,
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"goal": true,
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"subagent": true,
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"review_loop": true
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}
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},
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"claude": {
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"cli": "claude",
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"invoke": ["claude", "-p"],
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"probe": ["claude", "--version"],
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"available": true,
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"authed": true,
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"local": false,
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"capabilities": {
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"headless": true,
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"goal": true,
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"subagent": false,
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"review_loop": false
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}
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}
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}
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```
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## Capabilities
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`headless`
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: The CLI accepts a prompt via stdin/argument and returns output via stdout
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without interactive prompts. Required for use as host or judge in the
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external Python runner.
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`goal`
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: The CLI supports a `/goal` command that runs an autonomous loop with
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self-verification. When present, RUN_IN_SESSION.md can emit a `/goal`
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one-liner as an alternative execution path.
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`subagent`
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: The CLI can spawn isolated sub-agents with their own context. When present,
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the council can use native subagent review loops instead of shelling out.
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`review_loop`
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: The CLI supports iterative review loops with trigger-based feedback (e.g.
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Kiro's `loop_to` with `NEEDS_CHANGES` trigger). Enables native cross-model
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review without the external runner.
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## Kiro CLI Specifics
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- Headless mode requires `--trust-all-tools` or the session halts waiting for
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tool approval.
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- Full invoke pattern: `["kiro-cli", "chat", "--trust-all-tools", "-p"]`
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- The `/goal --max N` command provides native loop execution with configurable
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iteration limits (default 5).
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- Subagent review loops use a `trigger` string (e.g. `NEEDS_CHANGES`) and
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`max_iterations` cap.
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## `authed` Semantics
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`authed` means the basic probe command exited cleanly. It is a convenience
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signal, not a guarantee that a future paid model call will succeed.
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## Default Redactions
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- `.env`
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- `.env.*`
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- `secrets/**`
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- `**/*.key`
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Add project-specific globs for customer data, private transcripts, or internal
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design docs before sending anything to a non-local council member.
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## Local Model UX
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Surface `ollama` as the privacy-preserving option when present. It may be lower
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quality than frontier hosted models, but it keeps council review in-house.
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59
.github/skills/loop-architect/references/verification-rubric.md
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# Verification Rubric
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Use this when converting the user's definition of done into typed criteria.
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## Taxonomy
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`programmatic`
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: A command or deterministic check returns pass/fail. Use this whenever
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possible. Examples: tests, build, lint, schema validation, snapshot comparison,
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or an extraction script that checks required headings.
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`judge`
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: A model scores a rubric and returns a structured verdict. Use this for
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semantic quality that cannot be cheaply checked by code. The rubric must be
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specific enough that a different model can apply it consistently.
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`human`
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: A person must sign off. Use this for taste, business judgment, private
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knowledge, legal risk, or decisions where the user is the true authority.
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## Required Fields
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- Every criterion needs `id` and `type`.
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- `programmatic` needs `check` as an argv array and `expect`.
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- `judge` needs `rubric`.
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- `human` needs `prompt`.
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## Strong Criteria
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- Check one thing at a time.
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- Say what failure means.
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- Prefer deterministic checks before model judgment.
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- Make judge rubrics observable against artifacts the judge receives.
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- Avoid relying on the host model to grade its own work.
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## Anti-Patterns
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- All criteria are judge or human criteria when tests or schema checks exist.
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- "No errors thrown" as the only success criterion.
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- Criteria that require hidden context not sent to the judge.
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- Rubrics like "high quality" or "comprehensive" without dimensions.
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- Programmatic checks written as shell strings instead of argv arrays.
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## Structured Judge Contract
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Judges should return a fenced JSON object:
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```json
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{
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"verdict": "pass",
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"blocking_issues": [],
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"confidence": 0.86,
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"notes": "The artifact satisfies the rubric."
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}
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```
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Valid verdicts are `pass` and `revise`. If output cannot be parsed, the runner
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will treat it as `revise` with a warning.
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