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kitaru-replay-ops

decode's Kitaru operator surface for headless replay and what-if — three-runs (observed / baseline-rerun / fork), CLI replay with --args/--overrides, checkpoint overrides, diffing execution records, cohort scaling, wait re-ask behavior, subagent-as-one-checkpoint. Use when replay

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Precio sin confirmar★ 358 Estrellas de GitHubRegistro actualizado · 5 sept 2026agent-skill

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Headless replay & what-if (Kitaru operator surface — documented, not wrapped)

decode replay wraps only the bypass model-swap common case, 1:1 over Kitaru's native flow-object replay (ADR-0010 §5). Full checkpoint → replay → diff → decide loop = Kitaru's own CLI/SDK — decode deliberately does not re-implement diff, cohort, or checkpoint-override machinery (ADR-0010 §6). Below: that operator surface, verified against installed kitaru 0.18 + docs.zenml.io ("Replay and Overrides", "Replay and improve"); patterns / roadmap items flagged as such.

Three runs, not two. Trustworthy what-if = three runs; the middle is the point:

Run	What it is	Role
Observed	original recorded run	what actually happened
Baseline Rerun	`kitaru executions replay <id> --from <cp>`, no change	control — proves replay reproduces faithfully
Fork	same `--from`, one input changed (e.g. `--args '{"model":…}'`)	your change

Diff Fork vs Baseline Rerun, not vs Observed — the control isolates your one variable. Baseline Rerun ≠ Observed (nondeterministic tool, external state, time)? Diff untrustworthy; pin the nondeterminism first.

CLI replay with overrides (surface decode replay --model wraps a slice of):

kitaru executions replay <exec_id> --from <cp>                                   # Baseline Rerun (control)
kitaru executions replay <exec_id> --from <cp> --args '{"model":"gemini-2.5-pro"}'   # Fork (flow-input swap)
kitaru executions replay <exec_id> --from <cp> --overrides '{"checkpoint.<name>":<value>}'  # checkpoint-output swap

--args = flow-input overrides (CLI mirror of flow.replay(..., model=…); decode replay --model surfaces this). The Model Override rides here.
--overrides checkpoint.<name> = Checkpoint Override: substitute a recorded checkpoint's single output at its direct consumers, re-executing from there forward. Keys must start with checkpoint. (else KitaruUsageError); overridden checkpoint must expose a single output.
--overrides checkpoint.X = the tool-output mock stand-in. Per-tool-call output= / raise_= mocks (fake value / forced failure) are Kitaru roadmap, not shipped — ZenML guide flags this. Today: override the tool's recorded checkpoint output.

Diff = compare the two execution records. No kitaru diff CLI, no .diff() SDK method in kitaru 0.18 (verified — do not assume one). Manual comparison; the ZenML guide's own pattern:

kitaru executions get <fork_exec_id>        # decision, per-checkpoint outputs, cost, latency
kitaru executions get <baseline_rerun_id>   # the control to compare against

SDK: KitaruClient().executions.get(fork.exec_id) vs .get(rerun.exec_id) — compare cost/latency/decision. Baseline reproduced → any difference attributable to your one change. decode replay prints the same stderr hint (kitaru executions get <new> vs <original>), pointing only at this confirmed surface.

Cohort: scale the winning change across recent runs — example pattern on SDK primitives, NOT a core Kitaru API. ZenML "Replay and improve" guide ships run_cohort (+ cost / latency / quality_judge metric callables) in the kitaru examples repo (examples/end_to_end/pydantic_replay_fork); "not in the kitaru package — copy or adapt" (import kitaru_recipes is not an installed module — verified):

from cohort import run_cohort                 # from the EXAMPLE dir, not `import kitaru`
from utils import cost, latency, quality_judge
# exec_ids: recent runs, e.g. KitaruClient().executions.list(flow="run_agent_task")
report = run_cohort(exec_ids, baseline_model="gemini-2.5-flash",
                    variant_model="gemini-2.5-pro", metrics=[cost, latency, quality_judge])
report.summary()      # per-metric baseline-vs-variant deltas + an is-it-better verdict
report.regressions()  # the metrics / decisions that got worse

Per run: reproduce baseline, replay variant, score the pair — decide on a cohort, not one lucky run.

Waits re-ask on replay. A replayed run re-asks every wait() — Kitaru "does not support overriding or pre-populating wait results." Hence decode replay is bypass-only + HITL answer-reuse deferred (see replay row above). Honesty note: on a decode run --hitl pause, Kitaru itself prints Waiting for input… to stdout (framework behavior) — the pipe-clean guarantee covers the completed bypass answer only.

A subagent run = one opaque checkpoint. A whole agent(...) spawn — the child's entire nested loop — is one opaque tool call → one checkpoint under "calls": nested child model calls are not replay anchors; a decode replay --model swap does not reach inside a child (child rides parent's model — AgentDef has no model field). Read-only child's cached summary is replay-safe → agent never joins the sandbox-bash cache-disable set; child token spend stays folded into that one tool call, invisible until Opik (M10) — ADR-0013 §9.

An agent can drive the whole loop. Kitaru exposes this replay surface over an MCP server (kitaru-mcp console script) — a coding agent (Claude Code, Codex, Cursor) can pull a recent run, propose a change, replay vs control, compare, widen to a cohort — future automation hook (no decode work now).

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Metadatos del archivo

name: kitaru-replay-ops
description: decode's Kitaru operator surface for headless replay and what-if — three-runs (observed / baseline-rerun / fork), CLI replay with --args/--overrides, checkpoint overrides, diffing execution records, cohort scaling, wait re-ask behavior, subagent-as-one-checkpoint. Use when replaying or forking a decode run, overriding checkpoints, or comparing executions with the kitaru CLI/SDK.

Ver texto original

---
name: kitaru-replay-ops
description: decode's Kitaru operator surface for headless replay and what-if — three-runs (observed / baseline-rerun / fork), CLI replay with --args/--overrides, checkpoint overrides, diffing execution records, cohort scaling, wait re-ask behavior, subagent-as-one-checkpoint. Use when replaying or forking a decode run, overriding checkpoints, or comparing executions with the kitaru CLI/SDK.
---

## Headless replay & what-if (Kitaru operator surface — documented, not wrapped)

`decode replay` wraps only the **bypass model-swap** common case, 1:1 over Kitaru's native flow-object replay (ADR-0010 §5). Full **checkpoint → replay → diff → decide** loop = Kitaru's own CLI/SDK — decode deliberately does **not** re-implement diff, cohort, or checkpoint-override machinery (ADR-0010 §6). Below: that operator surface, verified against installed **kitaru 0.18** + docs.zenml.io ("Replay and Overrides", "Replay and improve"); patterns / roadmap items flagged as such.

**Three runs, not two.** Trustworthy what-if = three runs; the middle is the point:

| Run | What it is | Role |
|---|---|---|
| **Observed** | original recorded run | what actually happened |
| **Baseline Rerun** | `kitaru executions replay <id> --from <cp>`, **no** change | *control* — proves replay reproduces faithfully |
| **Fork** | same `--from`, **one** input changed (e.g. `--args '{"model":…}'`) | your change |

Diff **Fork vs Baseline Rerun**, not vs Observed — the control isolates your one variable. Baseline Rerun ≠ Observed (nondeterministic tool, external state, time)? Diff untrustworthy; pin the nondeterminism first.

**CLI replay with overrides** (surface `decode replay --model` wraps a slice of):

```bash
kitaru executions replay <exec_id> --from <cp>                                   # Baseline Rerun (control)
kitaru executions replay <exec_id> --from <cp> --args '{"model":"gemini-2.5-pro"}'   # Fork (flow-input swap)
kitaru executions replay <exec_id> --from <cp> --overrides '{"checkpoint.<name>":<value>}'  # checkpoint-output swap
```

- `--args` = **flow-input** overrides (CLI mirror of `flow.replay(..., model=…)`; `decode replay --model` surfaces this). The **Model Override** rides here.
- `--overrides checkpoint.<name>` = **Checkpoint Override**: substitute a recorded checkpoint's single output at its **direct consumers**, re-executing from there forward. Keys **must** start with `checkpoint.` (else `KitaruUsageError`); overridden checkpoint must expose a single output.
- **`--overrides checkpoint.X` = the tool-output mock stand-in.** Per-tool-call `output=` / `raise_=` mocks (fake value / forced failure) are **Kitaru roadmap, not shipped** — ZenML guide flags this. Today: override the tool's recorded checkpoint output.

**Diff = compare the two execution records.** **No `kitaru diff` CLI, no `.diff()` SDK method in kitaru 0.18** (verified — do not assume one). Manual comparison; the ZenML guide's own pattern:

```bash
kitaru executions get <fork_exec_id>        # decision, per-checkpoint outputs, cost, latency
kitaru executions get <baseline_rerun_id>   # the control to compare against
```

SDK: `KitaruClient().executions.get(fork.exec_id)` vs `.get(rerun.exec_id)` — compare cost/latency/decision. Baseline reproduced → any difference attributable to your one change. `decode replay` prints the same stderr hint (`kitaru executions get <new> vs <original>`), pointing only at this confirmed surface.

**Cohort: scale the winning change across recent runs** — **example pattern on SDK primitives, NOT a core Kitaru API.** ZenML "Replay and improve" guide ships `run_cohort` (+ `cost` / `latency` / `quality_judge` metric callables) in the **kitaru examples repo** (`examples/end_to_end/pydantic_replay_fork`); *"not in the `kitaru` package — copy or adapt"* (`import kitaru_recipes` is **not** an installed module — verified):

```python
from cohort import run_cohort                 # from the EXAMPLE dir, not `import kitaru`
from utils import cost, latency, quality_judge
# exec_ids: recent runs, e.g. KitaruClient().executions.list(flow="run_agent_task")
report = run_cohort(exec_ids, baseline_model="gemini-2.5-flash",
                    variant_model="gemini-2.5-pro", metrics=[cost, latency, quality_judge])
report.summary()      # per-metric baseline-vs-variant deltas + an is-it-better verdict
report.regressions()  # the metrics / decisions that got worse
```

Per run: reproduce baseline, replay variant, score the pair — decide on a cohort, not one lucky run.

**Waits re-ask on replay.** A replayed run **re-asks** every `wait()` — Kitaru "does not support overriding or pre-populating wait results." Hence `decode replay` is bypass-only + HITL answer-reuse deferred (see replay row above). Honesty note: on a `decode run --hitl` **pause**, Kitaru itself prints `Waiting for input…` to **stdout** (framework behavior) — the pipe-clean guarantee covers the completed **bypass** answer only.

**A subagent run = one opaque checkpoint.** A whole `agent(...)` spawn — the child's entire nested loop — is one opaque tool call → **one** checkpoint under `"calls"`: nested child model calls are **not** replay anchors; a `decode replay --model` swap does **not** reach inside a child (child rides parent's model — `AgentDef` has no model field). Read-only child's cached summary is replay-safe → `agent` never joins the sandbox-bash cache-disable set; child token spend stays folded into that one tool call, invisible until Opik (M10) — ADR-0013 §9.

**An agent can drive the whole loop.** Kitaru exposes this replay surface over an **MCP server** (`kitaru-mcp` console script) — a coding agent (Claude Code, Codex, Cursor) can pull a recent run, propose a change, replay vs control, compare, widen to a cohort — future automation hook (no decode work now).

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Licencia: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
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Repositorio fuente: decodingai-magazine/building-a-coding-agent-from-scratch-course
Licencia: Apache-2.0
Versión: 1.0.0
Último push de GitHub: 3 sept 2026
Registro actualizado: 5 sept 2026
Ruta de instrucciones: .claude/skills/kitaru-replay-ops/SKILL.md @ 6ee643fbfeb1

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

69/100

Prometedor

Confianza

64/100

Solo sandbox

Auditoría

76/100

Riesgoso

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Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 69,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "1mo since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
  ],
  "agent_contract": {
    "task_input": "Use kitaru-replay-ops in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 76/100 Risky",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "decodingai-magazine-kitaru-replay-ops (kitaru-replay-ops)",
      "install_command": "npx skills add decodingai-magazine/building-a-coding-agent-from-scratch-course --skill kitaru-replay-ops",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "decodingai-magazine-kitaru-replay-ops",
      "task": "Use kitaru-replay-ops in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/decodingai-magazine-kitaru-replay-ops",
    "api": "https://www.openagentskill.com/api/agent/skills/decodingai-magazine-kitaru-replay-ops",
    "audit": "https://www.openagentskill.com/skills/decodingai-magazine-kitaru-replay-ops/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=decodingai-magazine-kitaru-replay-ops&task=Use%20kitaru-replay-ops%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20kitaru-replay-ops%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20kitaru-replay-ops%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/decodingai-magazine-kitaru-replay-ops/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/decodingai-magazine-kitaru-replay-ops"
  }
}

Para el creador

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Creador: decodingai-magazine
Fuente: decodingai-magazine/building-a-coding-agent-from-scratch-course
Indexado por: Índice comunitario de OpenAgentSkill

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