# Skill Replay Eval Product Discovery Report

Date: 2026-06-09

Product stage: existing product

Primary feature: Skill Replay Eval for Beaver skill learning and publishing

Source context:

- Existing product and deployment: `README.md`, `部署指南.md`
- Feature design: `docs/superpowers/specs/2026-06-08-skill-replay-eval-design.md`
- Delivery plan: `docs/superpowers/plans/2026-06-08-skill-replay-eval.md`
- Current implementation signals: `beaver/skills/learning/{case_selection,preservation,replay,surrogate,eval}.py`, Skills page replay report UI, publish gate checks
- Customer positioning: `docs/presentations/beaver-project/index.html`

## Executive Summary

Beaver is positioned as an enterprise Agent execution and governance platform. Its core value is not only running tasks, but also making AI work traceable, acceptable, reusable, and governable. Skill Replay Eval is the quality gate that makes the "reusable skill" promise credible: before a skill draft is published, Beaver should test whether it improves or preserves real task behavior.

The current design correctly identifies the product risk: heuristic-only skill scoring is not enough for enterprise trust. A draft skill can look complete in text while causing tool misuse, dropping safety instructions, or regressing accepted workflows. Replay evaluation closes this gap by comparing baseline and candidate behavior on accepted historical tasks, classifying tool calls into executed, surrogate, or blocked modes, and adding a preservation check for revised skills.

The product direction should be: ship replay eval as a staged trust feature, not as a perfect benchmark system. The first release should make evaluation coverage and uncertainty visible, block obvious regressions, and give reviewers enough evidence to approve or reject drafts. The next releases should improve case quality, sandbox isolation, surrogate judgment quality, and operational dashboards.

## Product Summary

### Product Description

Skill Replay Eval is a review and publishing gate for Beaver skills. It evaluates skill drafts against prior accepted task runs and shows whether the draft improves, preserves, or harms real task outcomes. It separates safe tool execution from surrogate evaluation for unsafe or unavailable tools, and it checks whether revised skill drafts preserve important original instructions.

### Target Users

| Segment | Job To Be Done | Success Looks Like |
| --- | --- | --- |
| Enterprise AI platform owner | Govern reusable Agent capabilities before they spread across teams | No risky skill is published without evidence, review, and audit trail |
| Skill reviewer / admin | Decide whether a skill draft is good enough to approve | Replay report explains score, coverage, regressions, and preservation risks |
| Internal workflow owner | Convert accepted tasks into repeatable team methods | Similar future tasks become faster and more reliable |
| Engineer / implementer | Build and debug the eval pipeline | Replay failures are reproducible, scoped, and observable |
| Security / IT reviewer | Understand side effects and tool risk | Production writes are not executed during automatic replay |

### Current Features

Existing Beaver product capabilities relevant to this feature:

- Task lifecycle: route, plan, execute, track, accept, modify, or abandon.
- Evidence and timeline: tool calls, artifacts, task status, and validation signals.
- Skill learning: candidates, drafts, safety report, eval report, review, publish.
- Multi-instance deployment: isolated `app-instance` per user/team via Docker.
- Tool and connector framework: local tools, MCP tools, external connectors, files, web/search, scheduled tasks.

Current Skill Replay Eval implementation signals:

- `SkillDraftEvalReport` has replay fields: mode, eval version, execution coverage, surrogate coverage, blocked coverage, confidence, case reports, tool mode summary, and preservation report.
- `select_replay_cases()` selects up to 10 accepted historical runs by candidate type.
- `ReplayToolExecutor` classifies tool calls as executed, surrogate, or blocked.
- `ReplayRunner` runs baseline and candidate arms through AgentLoop with a replay tool executor.
- `SurrogateToolEvaluator` scores non-executed calls through deterministic intended-call heuristics.
- Publish gates block low-confidence replay reports, fully blocked replay reports, and failed preservation reports.
- Skills UI exposes execution coverage, surrogate coverage, confidence, replay cases, raw case reports, and preservation reports.

### Current Architecture

```text
Accepted task runs
  -> SkillLearningCandidate
  -> SkillDraft
  -> case selection
  -> baseline arm and candidate arm
  -> replay tool executor
       -> executed tools for safe toolsets
       -> surrogate traces for external writes or unsafe integrations
       -> blocked traces for destructive calls
  -> surrogate scoring and coverage aggregation
  -> preservation checker for revise/merge
  -> SkillDraftEvalReport
  -> Skills review UI
  -> publish gate
```

Product boundary:

- Replay Eval should evaluate skill behavior, not replace human review.
- Replay Eval should never write to production workspace, user files, external accounts, third-party systems, credentials, permissions, or payments by default.
- Low confidence should increase review burden instead of creating false certainty.

### Current Value Proposition

For enterprise users, Beaver can say: "Accepted work can become reusable skills, and those skills are checked against real task behavior before they are published." This directly supports Beaver's larger promise of controlled, traceable, reusable Agent execution.

### Current Challenges

| Challenge | Product Impact | Current Risk |
| --- | --- | --- |
| Historical accepted runs may be sparse or low quality | Replay evidence can be weak | Medium |
| Surrogate scoring is currently simple | Unsafe tool calls may be judged with low fidelity | High |
| Replay environment isolation must be enforceable | Enterprise trust depends on no accidental production side effects | High |
| Reviewers need clear explanations | Raw case reports can overwhelm non-engineers | Medium |
| Publish gates may be too strict or too loose | Either slows adoption or lets regressions through | Medium |
| Skill preservation is section-based | Important instruction changes inside a section may be missed | Medium |

## Missing Information And Ambiguities

- No real customer interview data is provided for skill reviewers, enterprise admins, or workflow owners.
- No baseline metrics exist for current heuristic eval false positives or false negatives.
- No defined quality threshold exists for minimum acceptable replay coverage per skill category.
- No clear operational owner is assigned for replay failures, low confidence reports, or blocked tool classifications.
- No explicit policy matrix exists per toolset, customer deployment mode, or tenant risk tier.
- No customer-facing language has been finalized for explaining surrogate evaluation limitations.

## User Segments

### Segment 1: Skill Governance Admin

This user owns skill approval. They need a reliable way to decide whether a skill should be published. Their main pain is that a skill draft can appear well-written but still fail on real tasks.

### Segment 2: Enterprise AI Platform Buyer

This user evaluates Beaver as an internal AI platform. They care about risk, adoption, cost, governance, and operational control. They need to see that reusable Agent capabilities are not published blindly.

### Segment 3: Workflow Owner

This user has repeatable work such as weekly reports, project delivery, technical support, or file processing. They want accepted workflows to become faster and more consistent over time.

### Segment 4: Beaver Engineer / Operator

This user debugs replay failures, expands safe tool coverage, adjusts publish gates, and keeps the eval pipeline reliable.

## JTBD

| User | Job Story | Current Alternative | Desired Outcome |
| --- | --- | --- | --- |
| Skill reviewer | When a skill draft is ready, I want to see whether it works on prior accepted tasks, so I can approve it with evidence | Read the draft manually | Approve, reject, or revise with confidence |
| Admin | When a skill touches tools, I want to know what would execute, what is simulated, and what is blocked, so I can manage risk | Trust reviewer judgment | Clear coverage and side-effect evidence |
| Workflow owner | When my accepted task becomes a reusable skill, I want it to preserve what made the original task successful | Rewrite prompts manually | Similar future work gets better |
| Operator | When replay fails, I want to know whether the issue is provider, tool policy, case data, or candidate behavior | Read logs manually | Fast diagnosis and recovery |

## Alternative Product Positioning

| Positioning | Strength | Weakness | Recommendation |
| --- | --- | --- | --- |
| "Skill unit tests for Agents" | Easy for engineers to understand | Too narrow; suggests deterministic tests only | Use in engineering docs |
| "Replay-based skill quality gate" | Accurate and product-relevant | Needs explanation for non-technical buyers | Primary internal positioning |
| "Enterprise Agent governance evidence" | Strong for buyers | Less precise for builders | Use in sales and customer docs |
| "A/B testing for skill drafts" | Captures baseline vs candidate | May imply live user traffic experiments | Use carefully |

Recommended positioning:

> Skill Replay Eval is Beaver's evidence-based quality gate for reusable Agent skills. It replays accepted historical tasks, compares baseline and candidate behavior, and exposes execution coverage, surrogate coverage, regressions, and preservation risk before publication.

## Opportunity Areas

| Opportunity | Importance | Current Satisfaction | Opportunity Score | Notes |
| --- | ---: | ---: | ---: | --- |
| I need proof that a skill draft improves real task behavior | 0.95 | 0.25 | 0.71 | Core opportunity |
| I need automatic replay to avoid unsafe side effects | 0.95 | 0.35 | 0.62 | Required for enterprise trust |
| I need reports that are understandable to reviewers | 0.85 | 0.35 | 0.55 | Key adoption driver |
| I need preservation of existing skill instructions | 0.80 | 0.45 | 0.44 | Important for revisions |
| I need replay failures to be diagnosable | 0.75 | 0.40 | 0.45 | Operational maturity |
| I need configurable policy per deployment | 0.70 | 0.30 | 0.49 | Later enterprise hardening |

Top opportunities:

1. Evidence that a draft improves or preserves accepted task behavior.
2. Safe replay with explicit executed/surrogate/blocked coverage.
3. Reviewer-facing explanation that turns raw traces into decisions.

## Product Expansion Ideas

Generated from PM, Designer, and Engineer perspectives.

### Product Manager Ideas

1. Replay Readiness Score: show whether a draft has enough historical evidence before eval starts.
2. Skill Release Gate Levels: allow advisory, strict, and regulated gates per workspace.
3. Regression Triage Queue: collect failed cases and route them to skill authors.
4. Customer-facing Audit Export: export replay report as PDF/Markdown for security review.
5. Skill Quality Trend: show whether a skill improves or degrades across versions.

### Product Designer Ideas

1. Reviewer Decision View: summarize "approve / revise / reject" with reasons before raw JSON.
2. Coverage Timeline: visualize executed, surrogate, and blocked calls per case.
3. Preservation Diff: show dropped or changed sections in a readable comparison.
4. Replay Case Drilldown: task text, expected behavior, baseline output, candidate output, and validator notes.
5. Confidence Language: translate low/medium/high confidence into concrete reviewer actions.

### Engineer Ideas

1. Pluggable Tool Policy Registry: classify tools by toolset, transport, metadata, and deployment risk.
2. Deterministic Replay Fixtures: save replay inputs and traces for reproducible debugging.
3. Sandbox User File Namespace: isolate user-file writes per replay arm.
4. LLM Surrogate Provider: replace deterministic heuristics with structured model judgment when available.
5. Replay Telemetry: metrics for replay latency, failure mode, blocked coverage, and provider availability.

Top 5 selected ideas:

| Rank | Idea | Why Selected | Assumptions To Validate |
| ---: | --- | --- | --- |
| 1 | Reviewer Decision View | Converts technical eval into action | Reviewers trust summarized recommendations |
| 2 | Sandbox User File Namespace | Directly addresses production side-effect risk | Existing file tooling can be redirected cleanly |
| 3 | LLM Surrogate Provider | Improves unsafe tool judgment quality | LLM judgment is consistent enough for review support |
| 4 | Replay Readiness Score | Prevents weak reports from appearing authoritative | Enough metadata exists to estimate readiness |
| 5 | Preservation Diff | Makes revision risk visible and actionable | Section and body-level diffs catch meaningful drops |

## Key Assumptions

| Assumption | Category | Impact | Uncertainty |
| --- | --- | ---: | ---: |
| Accepted historical runs are representative enough to evaluate future skill behavior | Value | High | High |
| Reviewers will use replay reports to make better publish decisions | Value | High | Medium |
| Safe tools can execute in isolation without leaking state or causing production side effects | Feasibility | High | High |
| Surrogate evaluation can judge unsafe tool calls well enough to support review | Feasibility | High | High |
| Coverage and confidence are understandable to non-engineer reviewers | Usability | Medium | High |
| Publish gates will reduce risky releases without blocking too many useful skills | Viability | High | Medium |
| Skill preservation can be detected with lightweight section checks in v1 | Feasibility | Medium | Medium |
| Replay latency will be acceptable for review workflows | Usability | Medium | Medium |
| Customers will value replay eval enough to differentiate Beaver from generic Agent tools | Business Viability | High | Medium |
| The team can maintain tool policy as tools/connectors grow | Team Capability | High | Medium |

## Prioritized Assumptions

Priority = Impact x Uncertainty.

### P0 Validate Immediately

| Assumption | Why It Matters | What Could Go Wrong | Suggested Validation |
| --- | --- | --- | --- |
| Safe replay isolation is real, not only conceptual | One accidental external write can break trust | Replay calls production filesystem, connector, or credential paths | Technical isolation test with destructive and external-write tools |
| Replay reports help reviewers make better decisions | Product value depends on review decisions changing | Reports are too raw, ignored, or misunderstood | Reviewer usability test with 5 draft decisions |
| Surrogate evaluation is good enough for unsafe tools | Many enterprise tools cannot execute in replay | It rubber-stamps bad calls or flags good calls | Golden set of unsafe tool scenarios scored by humans vs surrogate |
| Historical accepted cases are adequate for eval | Weak cases create false confidence | Too few accepted runs or repetitive cases | Analyze real run store coverage across skills |

### P1 Important

| Assumption | Why It Matters | Validation |
| --- | --- | --- |
| Publish gate thresholds are calibrated | Prevents both overblocking and underblocking | Run shadow mode for 2 weeks and compare human decisions |
| Preservation checker catches meaningful draft regressions | Revision safety depends on it | Compare section checker with manual diff review |
| Replay latency fits review workflow | Slow eval hurts adoption | Measure p50/p95 per case and per draft |
| Customers understand confidence and coverage language | Trust depends on clear communication | Customer-facing report comprehension test |

### P2 Later

| Assumption | Why It Matters | Validation |
| --- | --- | --- |
| Per-tool policy UI is needed | May not be needed in v1 | Observe support/admin requests |
| Audit export becomes a buying requirement | Useful for enterprise sales | Ask pilot buyers during procurement review |
| Skill quality trend is a major retention driver | Useful after multiple versions exist | Measure repeat reviewer usage after v1 |

## Opportunity Solution Tree

Desired outcome:

> Increase trusted skill publication: at least 80% of approved skill drafts have replay or explicit skipped-provider evidence, zero known production side effects from replay, and reviewer decision time under 10 minutes for common drafts.

```text
Outcome: Trusted skill publication

Opportunity 1: I need proof that a skill draft improves real task behavior.
  Solution 1.1: Baseline vs candidate replay on accepted historical tasks.
    Experiment: Run replay on 10 recent skill drafts and compare with manual reviewer judgment.
  Solution 1.2: Replay readiness score before evaluation starts.
    Experiment: Score existing candidates and check whether low-readiness reports are less useful.
  Solution 1.3: Regression triage queue.
    Experiment: Manually label failed cases for two weeks and measure fix rate.

Opportunity 2: I need replay to avoid unsafe side effects.
  Solution 2.1: Tool mode classification: executed, surrogate, blocked.
    Experiment: Golden tool policy test set covering filesystem, MCP, connectors, delete, send, publish.
  Solution 2.2: Isolated workspace and user-file namespace per arm.
    Experiment: Replay write task and verify no production paths change.
  Solution 2.3: Side-effect journal in each case report.
    Experiment: Security reviewer reads 5 reports and identifies all intended side effects.

Opportunity 3: I need reports I can act on.
  Solution 3.1: Reviewer decision summary with approve/revise/reject guidance.
    Experiment: First-click and decision-time test with reviewers.
  Solution 3.2: Coverage and confidence explanation.
    Experiment: Ask reviewers to explain report meaning after reading it.
  Solution 3.3: Preservation diff for revisions.
    Experiment: Seed dropped-instruction drafts and measure detection rate.
```

## Validation Experiments

| P0 Assumption | Hypothesis | Experiment | Cost | Duration | Success Criteria | Failure Criteria |
| --- | --- | --- | --- | --- | --- | --- |
| Safe replay isolation | Replay can execute safe tools without touching production state | Build a replay fixture that writes, reads, sends, deletes, and publishes through classified tools | Medium | 2-4 days | 100% production paths untouched; destructive calls blocked; external writes surrogate | Any real external write or production path mutation |
| Reviewer decision value | Replay reports improve approval accuracy and speed | Give 5 reviewers 8 historical drafts with and without replay report | Low | 2 days | Decision accuracy +25%; median decision time under 10 minutes | No improvement or reports misunderstood |
| Surrogate quality | Surrogate scoring agrees with human reviewer on unsafe tool calls | Create 30 unsafe-tool scenarios and compare human labels vs surrogate output | Medium | 3-5 days | >=80% agreement on pass/fail; all high-risk bad calls flagged | High-risk false pass |
| Historical case adequacy | Accepted runs provide enough useful replay cases | Audit run store across top 10 skills/candidates | Low | 1 day | >=70% candidates have >=3 meaningful accepted cases | Most candidates have no usable cases |

## Feature Prioritization

### Must Have

| Feature | Impact | Effort | Risk | Strategic Alignment |
| --- | --- | --- | --- | --- |
| Eval report compatibility fields | High | Low | Low | Required foundation |
| Historical accepted case selection | High | Medium | Medium | Required for behavior evidence |
| Baseline vs candidate replay arms | High | High | High | Core value |
| Tool mode classification | High | Medium | High | Core trust boundary |
| Replay coverage and confidence report | High | Medium | Medium | Reviewer decision support |
| Publish gates for failed/low-confidence replay | High | Low | Medium | Governance promise |
| Preservation check for revise/merge drafts | Medium | Medium | Medium | Prevents silent instruction loss |
| Skills UI report summary | High | Medium | Medium | Adoption requirement |

### Should Have

| Feature | Impact | Effort | Risk | Strategic Alignment |
| --- | --- | --- | --- | --- |
| Reviewer decision summary | High | Medium | Medium | Converts evidence to action |
| Preservation diff view | Medium | Medium | Low | Improves reviewer comprehension |
| Replay readiness score | Medium | Medium | Medium | Prevents false confidence |
| Operational metrics dashboard | Medium | Medium | Low | Needed for maintenance |
| Golden tool policy test suite | High | Medium | Medium | Needed for safety assurance |

### Could Have

| Feature | Impact | Effort | Risk | Strategic Alignment |
| --- | --- | --- | --- | --- |
| Audit export | Medium | Medium | Low | Enterprise sales support |
| Skill quality trend | Medium | Medium | Medium | Useful after version history grows |
| Per-tool admin policy UI | Medium | High | Medium | Enterprise customization |
| Replay fixtures download | Low | Medium | Low | Debugging convenience |

### Not Yet

| Feature | Reason |
| --- | --- |
| Full Docker orchestration per replay case | Too heavy for first release; design explicitly scopes it out |
| Production third-party write replay | Violates trust boundary |
| Removing human review | Replay evidence should support review, not replace it |
| Fully customizable policy UI | Add after policy needs are observed |

Features to cut from v1:

- Per-tool policy UI.
- Audit export.
- Skill quality trend.
- Full Docker-per-case orchestration.

Features likely over-engineered for v1:

- Customer-configurable replay policies before default policy is proven.
- Complex statistical scoring before case quality and surrogate accuracy are validated.
- Automatic publish for high-scoring drafts.

## Metrics Dashboard

### North Star Metric

Trusted Skill Publish Rate:

> Approved skill drafts with usable eval evidence and no post-publish regression reports / total approved skill drafts, measured weekly.

Target for v1 pilot: >=80%.

### Input Metrics

| Metric | Definition | Data Source | Visualization | Target | Alert Threshold |
| --- | --- | --- | --- | --- | --- |
| Replay Evidence Coverage | Draft eval reports with mode `replay` or explicit skipped-provider status / all eval reports | Skill eval store | Weekly line | >=80% | <60% for 2 weeks |
| Executed Tool Coverage | Executed tool calls / all replay tool calls | Case reports | Stacked bar | >=50% for safe-tool skills | <25% for safe-tool skills |
| Surrogate Coverage | Surrogate tool calls / all replay tool calls | Case reports | Stacked bar | Transparent, not necessarily low | Sudden +30% week over week |
| Blocked Coverage | Blocked tool calls / all replay tool calls | Case reports | Stacked bar | <10% | >=25% or any blocked_coverage=1.0 |
| Reviewer Decision Time | Time from eval report created to approve/reject/revise | Review events | Median and p95 | Median <10 min | p95 >30 min |
| Replay Regression Rate | Reports with regression_count > 0 / replay reports | Eval store | Weekly line | Investigate, not zero-forced | >20% |

### Leading Indicators

- Number of accepted runs eligible for replay per skill.
- Percentage of candidates with at least 3 replay cases.
- Provider unavailable skip rate.
- Replay error or partial status rate.
- Preservation failures per revised skill draft.

### Guardrail Metrics

| Guardrail | Definition | Alert |
| --- | --- | --- |
| Production Side Effect Incidents | Any replay-caused write to production workspace, user files, credentials, or external systems | Immediate P0 |
| False Pass Incidents | Published draft later confirmed to regress an accepted workflow despite passing replay | Weekly review; P1 if repeated |
| False Block Incidents | Useful draft blocked due to bad policy or low-confidence bug | Weekly review |
| Replay Latency | p95 replay completion time per draft | Alert if p95 >15 minutes in pilot |
| Report Comprehension | Reviewers correctly explain coverage/confidence in usability tests | Rework UI copy if <80% |

### Review Cadence

- Daily during pilot: replay errors, side-effect alerts, provider skips.
- Weekly: publish outcomes, regression rate, reviewer decision time, blocked/surrogate coverage.
- Monthly: threshold calibration and customer feedback.
- Quarterly: policy model, scoring model, and roadmap review.

## Customer Research Plan

No customer interviews or support tickets were provided. Run research before treating demand and usability assumptions as validated.

### Research Participants

- 3-5 internal skill reviewers or admins.
- 3 workflow owners who want accepted tasks converted into reusable skills.
- 2 enterprise/security stakeholders who review AI governance.
- 2 engineers/operators responsible for deployment and incident response.

### Research Questions

- What evidence do reviewers need before approving a reusable skill?
- Which replay report fields are meaningful, and which are noise?
- Do users understand executed vs surrogate vs blocked coverage?
- What level of uncertainty is acceptable for publishing?
- What customer-facing proof is needed for enterprise pilots?
- Which tool categories must never execute during replay?

### Recommended Actions

- Run a moderated reviewer test with current Skills page report.
- Create 5 seeded draft cases: clear improvement, clear regression, unsafe external write, preservation drop, provider unavailable.
- Ask participants to approve/revise/reject each case and explain why.
- Compare their decisions with current publish gate behavior.

## Interview Guide

### Objectives

- Validate whether replay evidence changes approval behavior.
- Identify confusing report language.
- Understand risk tolerance for surrogate and blocked calls.
- Learn what artifacts enterprise buyers need for adoption.

### Warm-Up

- Tell me about the last time you reviewed or approved reusable AI guidance, prompts, tools, or workflows.
- What made the approval easy or hard?
- What happened after it was approved?

### JTBD Questions

- Walk me through the last time an AI workflow worked well enough that you wanted to reuse it.
- What evidence did you have that it would work again?
- What would make you hesitate to publish it for others?
- What does "safe to publish" mean in your environment?

### Behavioral Questions

- Show me how you would decide whether this draft should be approved.
- Which part of this report would you read first?
- What would you ignore?
- What would you ask an engineer to explain?

### Risk Validation Questions

- If a replay report says 70% executed and 30% surrogate, what decision would you make?
- If all important external writes were surrogate-evaluated, is that enough for review?
- Which tools should always be blocked in your environment?
- What kind of failure would make you disable replay eval?

### Note Template

```text
Participant:
Role:
Date:
Last relevant review:
Decision evidence needed:
Confusing report fields:
Risk tolerance:
Must-block tool categories:
Minimum publish evidence:
Unexpected insight:
Follow-up:
```

## Recommended Next 30 Days

1. Validate replay isolation with a golden tool policy suite.
2. Run current backend unit tests around skill learning replay and publish gates.
3. Add a small reviewer decision summary above raw replay details.
4. Run 5-8 reviewer usability sessions using seeded draft cases.
5. Audit accepted run coverage for top skills and identify gaps.
6. Decide v1 gate thresholds for blocked coverage, confidence, and preservation failure.
7. Add operational logging and metrics for replay status, latency, and provider skips.

## Recommended Next 90 Days

1. Replace or augment deterministic surrogate scoring with structured LLM judgment and human-labeled calibration cases.
2. Add replay readiness scoring before eval starts.
3. Improve preservation from section presence to diff-based critical instruction detection.
4. Add customer/exportable audit summary for enterprise pilot conversations.
5. Build a replay operations dashboard.
6. Introduce deployment-level policy profiles only after default policies produce stable data.
7. Track skill quality across versions and post-publish regression reports.

## Biggest Risks

| Risk | Severity | Mitigation |
| --- | --- | --- |
| Replay accidentally mutates production state | Critical | Golden policy tests, isolated namespaces, external writes surrogate by default, P0 alert |
| Surrogate scoring gives false confidence | High | Human-labeled calibration set, show low confidence clearly, no automatic publish |
| Reviewers ignore report complexity | High | Decision summary, comprehension testing, action-oriented UI copy |
| Accepted run data is too sparse | High | Readiness score, fallback to explicit skipped/low-evidence state, collect more accepted cases |
| Publish gates block too many useful skills | Medium | Shadow mode calibration and override with explicit review rationale |
| Evaluation costs or latency grow quickly | Medium | Cap cases, cache web/search, track p95 latency, async background eval |

## Recommended Immediate Actions

1. Treat Skill Replay Eval as a v1 trust gate, not a complete benchmark.
2. Keep human review mandatory for publish.
3. Do not execute production third-party writes during automatic replay.
4. Add reviewer-facing explanations before adding more raw report data.
5. Validate isolation and surrogate quality before broad rollout.
6. Use the first pilot to learn threshold calibration, not to claim perfect quality measurement.
