conch-review-deep-review

name	conch-review-deep-review
description	Run an evidence-heavy deep review of a specific Conch subsystem, operation, or failure class. Use when the user wants a focused investigation such as lifecycle correctness, snapshot behavior, cleanup bugs, parity drift, docs drift, or trust-boundary analysis.

Conch Review Deep Review

Use this skill when the question is no longer "where should we look?" but "what is actually wrong in this specific area?"

Inputs

• target_repo: path to the Conch repository
• focus: required subsystem, operation, or failure class
• optional scope: narrower file or directory set
• optional reviewers: explicit reviewer subset if already known

Default target repo: /home/stopire/Conch

Load These Assets

• policies/severity.md
• policies/report-template.md

Load reviewers based on focus:

• lifecycle -> reviewers/lifecycle-reviewer.md
• image or snapshot -> reviewers/image-snapshot-reviewer.md
• cleanup -> reviewers/resource-leak-reviewer.md
• concurrency -> reviewers/concurrency-reviewer.md
• parity -> reviewers/api-sdk-parity-reviewer.md
• docs -> reviewers/docs-implementation-auditor.md
• safety -> reviewers/security-boundary-reviewer.md
• runtime validation -> reviewers/sandbox-e2e-validator.md

Use these scripts:

• scripts/discover_conch.py
• scripts/collect_entrypoints.py
• scripts/run_smoke.py when runtime evidence is needed
• scripts/normalize_findings.py when you need a unified findings envelope

Workflow

1. Require a clear focus

If the user did not provide a real focus, stop and ask for one concise target such as:

• sandbox create/delete lifecycle
• build -> unpack -> snapshot chain
• cleanup after failed create
• SDK/API parity
• docs versus implementation

Do not silently degrade into a broad health review.

2. Discover the repo shape

Run:

python3 scripts/discover_conch.py <target_repo>

3. Collect likely entrypoints

Run:

python3 scripts/collect_entrypoints.py <target_repo>

Use the result to narrow the evidence set before dispatching reviewers.

4. Select and dispatch reviewers

Pick the reviewer subset that matches the focus. Use reviewer boundaries strictly. Deep review means tracing the path with enough surrounding code and documentation to support a strong conclusion.

5. Add runtime evidence when needed

If static evidence is not enough, run:

python3 scripts/run_smoke.py <target_repo> --mode probe-only

or, if appropriate:

python3 scripts/run_smoke.py <target_repo> --mode execute

Use runtime evidence to support the review, not to replace static reasoning.

6. Synthesize a focused report

Use policies/report-template.md as the output skeleton.

Produce:

• clear focus definition
• participating reviewers
• evidence-backed findings grouped by concern
• open questions
• recommended next action: fix, investigate further, or validate more deeply

Constraints

• Preserve reviewer boundaries.
• Call out uncertainty explicitly.
• Do not broaden into a general dashboard.
• If the task turns into pure runtime validation, switch to conch-review-smoke.