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Review Core

  • 94 installs
  • 325 repo stars
  • Updated August 2, 2026
  • athola/claude-night-market

Kick off any deep review so your agent captures context, evidence, and deliverables in a consistent, comparable report format.

About

Review-core is procedural scaffolding for agent-led deep reviews—architecture, APIs, math, or code—so every run follows the same path from context through evidence to a structured report. Solo builders install it when ad-hoc review threads produce inconsistent severity, missing citations, or incomparable outputs across PRs. The skill does not substitute domain checklists; it enforces preflight todos, scope inventory, evidence logging, deliverable structure, grounding verification, and documented contingencies. Use at the start of a detailed review workflow in the night-market stack or standalone whenever you want findings another human or agent can diff against prior runs. Intermediate complexity because it assumes you already know what you are reviewing and can maintain TodoWrite discipline through six steps.

  • Six-step workflow: context, scope inventory, evidence, deliverables, grounded verification, contingencies
  • TodoWrite gates at each step for traceable review progress
  • Evidence capture and structured reporting for comparable findings across review types
  • Explicit check that findings stay grounded in captured evidence
  • Contingency planning before sign-off on review conclusions

Review Core by the numbers

  • 94 all-time installs (skills.sh)
  • Ranked #455 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/athola/claude-night-market --skill review-core

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Listed on Skillselion
Installs94
repo stars325
Security audit2 / 3 scanners passed
Last updatedAugust 2, 2026
Repositoryathola/claude-night-market

What it does

Kick off any deep review so your agent captures context, evidence, and deliverables in a consistent, comparable report format.

Files

SKILL.mdMarkdownGitHub ↗

Core Review Workflow

Table of Contents

1. When to Use 2. Activation Patterns 3. Required TodoWrite Items 4. Step 1 – Establish Context 5. Step 2 – Inventory Scope 6. Step 3 – Capture Evidence 7. Step 4 – Structure Deliverables 8. Step 5 – Verify Findings Are Grounded 9. Step 6 – Contingency Plan

When To Use

  • Use this skill at the beginning of any detailed review workflow (e.g., for architecture, math, or an API).
  • It provides a consistent structure for capturing context, logging evidence, and formatting the final report, which makes the findings of different reviews comparable.

When NOT To Use

  • Diff-focused analysis - use diff-analysis

Activation Patterns

Trigger Keywords: review, audit, analysis, assessment, evaluation, inspection Contextual Cues:

  • "review this code/design/architecture"
  • "conduct an audit of"
  • "analyze this for issues"
  • "evaluate the quality of"
  • "perform an assessment"

Auto-Load When: Any review-specific workflow is detected or when analysis methodologies are requested.

Required TodoWrite Items

1. review-core:context-established 2. review-core:scope-inventoried 3. review-core:evidence-captured 4. review-core:deliverables-structured 5. review-core:findings-verified 6. review-core:contingencies-documented

Step 1 – Establish Context (review-core:context-established)

  • Confirm pwd, repo, branch, and upstream base (e.g., git status -sb, git rev-parse --abbrev-ref HEAD).
  • Note comparison target (merge base, release tag) so later diffs reference a concrete range.
  • Summarize the feature/bug/initiative under review plus stakeholders and deadlines.

Step 2 – Inventory Scope (review-core:scope-inventoried)

  • List relevant artifacts for this review: source files, configs, docs, specs, generated assets (OpenAPI, Makefiles, ADRs, notebooks, etc.).
  • Record how you enumerated them (commands like rg --files -g '*.mk', ls docs, cargo metadata).
  • Capture assumptions or constraints inherited from the plan/issue so the domain-specific analysis can cite them.

Step 3 – Capture Evidence (review-core:evidence-captured)

  • Log every command/output that informs the review (e.g., git diff --stat, make -pn, cargo doc, web.run citations). Keep snippets or line numbers for later reference.
  • Track open questions or variances found during preflight; if they block progress, record owners/timelines now.

Record Lessons Learned (decision journal)

If this work involved rework, a failed approach, or a blocker, record it to docs/lessons-learned.md so the insight survives past the session (draft and confirm):

  • If leyline is installed, invoke Skill(leyline:decision-journal) and append

a lesson entry (what_happened, what_didnt_work, root_cause, action; set phase to review). Show the draft; append on confirmation.

  • Fallback (leyline absent): append to docs/lessons-learned.md using the

in-file ENTRY TEMPLATE; assign the next LL-NNN id.

Step 4 – Structure Deliverables (review-core:deliverables-structured)

  • Prepare the reporting skeleton shared by all reviews:
  • Summary (baseline, scope, recommendation)
  • Ordered findings (severity, file:line, principle violated, remediation)
  • Follow-up tasks (owner + due date)
  • Evidence appendix (commands, URLs, notebooks)
  • validate the domain-specific checklist will populate each section before concluding.

Step 5 – Verify Findings Are Grounded (review-core:findings-verified)

Every finding must be falsifiable: a citation a second pass can mechanically re-read and confirm. Findings that fail verification do not ship.

  • Use the grounded-finding schema from Skill(imbue:structured-output):

each finding carries a Location (file:line) and a verbatim Anchor snippet copied from that line.

  • Write the findings to .review/findings.json (one object per finding:

id, file, line, anchor, severity, category, recommendation, evidence_refs).

  • Run the verifier:
  python plugins/imbue/scripts/citation_verifier.py \
    --findings .review/findings.json --repo-root .

Exit 0 means every citation resolved; exit 1 lists each finding whose path, line, or anchor did not match the source.

  • Drop or label UNVERIFIED any finding the verifier failed; only

verified findings enter the report. Attach the verifier output to the evidence appendix.

  • If the script is unavailable, fall back to re-reading each cited

file:line by hand and confirming the anchor text is present; note the manual fallback in the contingency section.

Step 6 – Contingency Plan (review-core:contingencies-documented)

  • If a required tool or skill is unavailable (e.g., web.run), document the alternative steps that will be taken and any limitations this introduces. This helps reviewers understand any gaps in coverage.
  • Note any outstanding approvals or data needed to complete the review.

Exit Criteria

  • All TodoWrite items complete with concrete notes (commands run, files listed, evidence paths).
  • Every reported finding carries a Location + verbatim Anchor and was confirmed by citation_verifier.py (or a documented manual re-read); no unverified findings ship.
  • .review/findings.json exists and the verifier exited 0, or every failed finding was dropped or labeled UNVERIFIED.
  • Domain-specific review can now assume consistent context/evidence/deliverable scaffolding and focus on specialized analysis.
  • Any rework, failed approach, or blocker uncovered during evidence capture is recorded to docs/lessons-learned.md (or the in-file template).

Related skills

FAQ

Is Review Core safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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