Agent skill

Logic Review

by sickn33 in sickn33/agentic-awesome-skills

Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).

MITAuto-check passed

Install Logic Review

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill logic-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills logic-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/logic-review .claude/skills/logic-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
logic-review
GitHub stars
47k
Used in
1 other repo
Token cost
~3.5k tokens
SKILL.md length
1,640 words
Files
2
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).

  • Works in 3 steps: Read ../_shared/common.md only for… → Read only the relevant step in… → Load ../_shared/logic-risks.md,…
  • SKILL.md covers When to Use, Output Skeleton Contract, Setup and Process, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Logic Review is an agent skill from sickn33/agentic-awesome-skills. Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `logic-review-guide.md`).

The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

Example prompts

  • “/logic-review”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Read ../_shared/common.md only for language, Iron Law, Logic Score, scope management, Remedy discipline, config fields, and loading budget.
  2. Read only the relevant step in logic-review-guide.md as you reach it.
  3. Load ../_shared/logic-risks.md, ../_shared/semiformal-guide.md, ../_shared/semiformal-checklist.md, and ../_shared/report-template.md on…

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Logic Review loads about 3.5k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 1,640 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 1,640 words, ~3,454 tokens.

Download SKILL.mdSave it as .claude/skills/logic-review/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
logic-review
description
Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).
risk
critical
source
https://github.com/hyhmrright/logic-lens/tree/main/skills/logic-review
source_repo
hyhmrright/logic-lens
source_type
community
date_added
2026-07-01
license
MIT
license_source
https://github.com/hyhmrright/logic-lens/blob/main/LICENSE

Logic-Lens — Logic Review

When to Use

Use this skill when you need find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Trigger when a user shares code and suspects something is wrong without naming a concrete failure — phrases like "review this", "does this look right",...

Output Skeleton Contract

The downstream grader (scripts/grade-iteration.py) and other Logic-Lens skills consume this report by substring-matching literal tokens defined in ../_shared/common.md §1 (header map), §2 (mandatory field labels + Logic Score), and ../_shared/report-template.md (skeleton). Paraphrasing those tokens — even with a synonym that reads fine to a human — breaks the contract regardless of analysis quality.

Three failure modes observed in benchmark that deserve specific callout beyond the general rule:

  • Synonym substitution for field labels whose substituted form omits the required substring — replacing Premises / 前提 with 前置条件构建 / 前置条件 (eval-201), or Divergence / 偏差 with 根因 / 核心缺陷 / 结论 (eval-252). Each substitution reads fine to a human and may even appear as a section heading or table column, but the substituted word does NOT contain the required substring, so grader and cross-skill consumers see the document as missing the field entirely. Use the literal token from common.md §1; you can still add a descriptive subtitle alongside it.
  • Demoting a confirmed L-code finding to ### 附加观察(非 Finding) / ### Additional observation — if Premises→Trace→Divergence holds, the finding belongs inside ## Findings with the five literal fields, even at Suggestion severity. This was a recurring cause of eval-279 (quicksort L4) failing on Sonnet runs.
  • Omitting Divergence: / 偏差: field entirely — the single most frequent failure mode. Many outputs correctly analyze the bug but write the divergence as prose, in a table cell, or under headings like 根因, 故障点, 核心问题, 缺陷. The Divergence: field is the specific label for "the point where actual behavior diverges from the premise." It is NOT optional and has no acceptable synonym. For no-bug findings use Divergence: None — [why the premise holds] (中文 偏差:无——[原因]).

Correctly formatted finding — use as template:

### 🔴 Critical
**[L4] — Mutation during iteration skips elements**
Premises: `users` is `list[User]` passed by reference; `list.remove()` shifts subsequent elements left; the `for` iterator advances by index.
Trace: [1] index=0, user is inactive → `remove()` shifts list. [2] Iterator advances to index 1, which now holds the element originally at index 2 — the original index-1 element is skipped. Rebuttal check: PASSED — no defense found.
Divergence: `remove_inactive([inactive₁, inactive₂, active])` returns `[inactive₂, active]` (2 elements) instead of `[active]` (1 element) — the second inactive user is never visited.
Trigger: `remove_inactive([User(False), User(False), User(True)])` → expected 1, actual 2.
Remedy: Replace loop body with `return [u for u in users if u.is_active]`. Dry-run: ✅ divergence eliminated.

Each finding block MUST contain all five literal labels (Premises: / Trace: / Divergence: / Trigger: / Remedy: or 前提: / 追踪: / 偏差: / 触发: / 修复:) as line-starting prefixes. Section headers (### Premises, ## Execution Trace) do NOT satisfy this requirement — the labels must appear inside the finding block.

No-bug case: emit ## Findings with a finding block that uses all five field labels, with Divergence: None — [why the premise holds]. This format is REQUIRED — it satisfies both grading and auditing. Example:

### ✅ No Bug
**[No Bug] — defer guarantees unlock on all exit paths**
Premises: `mu.Lock()` acquired at line 12; `defer mu.Unlock()` placed at line 13 (before any conditional branch or early return).
Trace: [1] `defer` registered immediately after `Lock()`. [2] Go spec guarantees deferred calls execute on ALL function exit paths (return, panic, early return). [3] No conditional branch between Lock and defer registration.
Divergence: None — `defer mu.Unlock()` placed unconditionally after acquire guarantees release on every exit path; no lock leak possible.
Trigger: N/A (no bug to reproduce).
Remedy: N/A (code is correct as written).

Setup

Use lazy loading per ../_shared/common.md §13:

  1. Read ../_shared/common.md only for language, Iron Law, Logic Score, scope management, Remedy discipline, config fields, and loading budget.
  2. Read only the relevant step in logic-review-guide.md as you reach it.
  3. Load ../_shared/logic-risks.md, ../_shared/semiformal-guide.md, ../_shared/semiformal-checklist.md, and ../_shared/report-template.md on demand when the current step needs them.

Process

Step 0. Language + scope routing. Detect the user's language per common.md §1; every label and header below must be in that language. Confirm scope is one file or one function — if the user points at a directory, switch to logic-health; if they describe a confirmed failure, switch to logic-locate; if two versions, logic-diff.

Step 1. Establish claimed behavior + review entry points (guide Step 1) — write one sentence describing what the code is supposed to do, then select the concrete entry function(s) that will be traced. If a file exceeds common.md §9 limits, state the selected subset and why.

Step 2. Build premises (guide Step 2) — per the Premises Construction Checklist in semiformal-checklist.md; include caller/callee contracts when the reviewed function depends on another local function.

Step 3. Build the risk path ledger (guide Step 3) — enumerate candidate bug paths across L1–L9 before writing findings. Tag each retained path as Class A (self-evident) or Class B (invariant-dependent). Do not stop after the happy path. Read logic-risks.md Quick Disambiguation Table before assigning any L-code — common misclassifications are catalogued there. L4 priority check: does any function mutate its input AND return the same object? L7 priority check: is shared state accessed across await/yield/thread boundaries without explicit synchronization? L4 vs L7 disambiguation: any state access involving more than one execution context (thread / goroutine / await / yield) is L7, never L4 — including single-threaded asyncio where coroutines interleave at await. L4 is for single-context aliasing only (mutable defaults, in-place mutation footgun, mutation-during-iteration). L4 requires an actual mutation of shared/aliased state as the root cause — variable scoping issues (const/let visibility, constructor scope) are L1, and query-pattern inefficiencies (N+1) are L3.

L1 vs L6 disambiguation: if the root cause is a name/identifier resolving to a different definition than the developer expected (import shadowing, module constant lookup, prototype chain, constructor-scoped const/let not visible to methods), it is L1 even when the symptom is a missing-method error or wrong return value — L6 applies only when the name resolves correctly but the callee's behavior differs from what the caller assumed.

L2 vs L6 disambiguation: if the root cause is an implicit type coercion at the operator level (+/-/*/== triggering string↔number conversion, or as/cast bypassing runtime type checks), it is L2 — L6 requires calling a specific callee whose behavior differs from the caller's assumption. Operators are not callees.

L5 vs L7 disambiguation: if an error code, exit status, or exception is suppressed by a single-context construct (|| true, empty catch, missing set -e, bare except), it is L5 (control flow escape) — L7 requires multiple execution contexts. Error propagation failure within one sequential script/function is L5.

L9 check: if the bug's root cause is timezone/locale/encoding information lost at the data-type level (e.g., TIMESTAMP vs TIMESTAMPTZ, naive vs aware datetime, locale-dependent string sort), it is L9 — not L6 even if it looks like "callee behavior differs from expectation", not L2, not L8.

Step 4. Deep-trace selected paths (guide Step 4) — trace the normal path plus the highest-risk edge paths; resolve every name, state every type, cross callee boundaries, and stop each trace at either a confirmed divergence or a confirmed safe post-condition. Java/C++ DCL rule: for double-checked locking patterns, MUST trace both faces: (a) missing volatile / memory barrier (visibility hazard) AND (b) instance = new X(); instance.init(); as two non-atomic statements — lock-free readers can see non-null instance before init() completes (publish-before-init hazard). Report both; omitting either is an incomplete analysis.

Show full SKILL.md (628 more words)Show less

Step 5. Identify divergences (guide Step 5) — classify each by L1–L9; assign severity; apply the reachability gate (Class A reports directly; Class B requires a probe — enforcement found → drop candidate, not found → assigned severity, partial → cap at Warning with manual verification recommended). Apply the correctness parity principle for no-bug scenarios. No-bug output discipline: when zero divergences remain, still emit the full template skeleton — Mode line, Scope, **Logic Score:** 100/100, ## Findings followed by a finding block that uses Divergence: None — [why the premise holds] (中文 偏差:无——[原因]) with all five field labels present. This makes the reasoning auditable and satisfies the format contract. If analysis actively disproves a suspected bug, explain the defense in the Trace: field (e.g., "Go defer mu.Unlock() guarantees release on all exit paths including early return"). Do not collapse the verdict into free-form prose or omit the structured fields; downstream grading requires the five-field format even for no-bug conclusions.

Step 5.5. Adversarial Red Team (guide Step 5.5) — for each candidate finding, attempt to disprove it by answering three rebuttal questions (premise rebuttal, path rebuttal, consequence rebuttal). Withdraw findings with confirmed defenses; downgrade findings with partial defenses to Suggestion. Design-intent gate: before reporting an L3 Boundary Blindspot, ask "Does the code explicitly return an error / rejection at this boundary rather than attempting to continue past it?" If yes (e.g., errors.New("cache full") at maxSize, 429 Too Many Requests, buffer-full rejection), withdraw — these are correct boundary enforcement, not blindspots. L3 applies only when code attempts to operate past the boundary and silently fails (wrong result, crash, infinite loop). Note: a panic at a boundary is a crash, not a designed error return, and remains a potential L3.

Step 6. Apply Iron Law — Five-Field Discipline (guide Step 6) — confirm all findings have Premises → Trace → Divergence complete; then write Trigger (concrete reproducing input, required for Critical/Warning) and Remedy (paste-ready per common.md §10). Each finding MUST use these literal field labels — English Premises: / Trace: / Divergence: / Trigger: / Remedy:, or Chinese 前提: / 追踪: / 偏差: / 触发: / 修复:. Do not paraphrase. Headers like Execution Path, Issue Found, Core Defect, 执行路径, 发现的逻辑隐患, 核心缺陷 are unacceptable substitutes — they fail downstream grading and break the report contract that other Logic-Lens skills consume. Multi-finding discipline: When there are multiple findings, each finding block inside ## Findings must include all five literal field labels — Premises: / Trace: / Divergence: / Trigger: / Remedy: (or their Chinese equivalents) — with content specific to that finding. Any shared background context may appear as a preamble section, but it does NOT substitute for the per-finding fields. A finding that omits Divergence: (or any other required field) breaks the contract even if Premises: or Trace: appear elsewhere in the report.

Step 6.5. Remedy Dry-Run (guide Step 6.5) — mentally re-trace the Trigger input through the fixed code to confirm: divergence eliminated, no regression introduced, happy path preserved.

Step 7. Score and output (guide Step 7) — compute Logic Score per common.md §6 and emit it as the literal line **Logic Score:** XX/100 (中文 **逻辑评分:** XX/100) directly under **Scope:** — this exact token (not "Score: XX", not "Quality: XX") is required for both grader recognition and cross-skill consumption. Then render the rest of the Report Template with localized headers.

Step 8. Execution Verification Gate (guide Step 8, optional) — when a runtime is available, generate a minimal reproducer script for each Critical/Warning finding, execute it to confirm the bug exists, apply the Remedy and re-execute to confirm the fix works. Withdraw false positives; mark verified findings as ✅ Execution-verified.

Mode line in report: Logic Review (Chinese: 逻辑审查).

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/logic-review of sickn33/agentic-awesome-skills.

  • SKILL.md
  • logic-review-guide.md

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Logic Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Logic Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Logic Review this skillsickn33/agentic-awesome-skills47k1 repos~3.5kAutomated safety check: PassMIT
Mole Bug Patternstw93/Mole70k—~2kAutomated safety check: PassGPL-3.0
Add Bugremotion-dev/remotion63k—~215Automated safety check: PassCustom licence
Bugccch1mneyyy/dsh-TUI4.2k—~342Automated safety check: PassMIT
Bug Bountyawarexone/Agentic-Bug-Hunter5.3k—~20kAutomated safety check: WarnMIT
Logical Propertiesthedaviddias/Front-End-Checklist74k—~526Automated safety check: PassMIT

Similar skills

  • A catalog of recurring bug shapes in the Mole Mac cleaner, used to review safety-sensitive diffs for deletion safety, unbounded commands, shell traps and weak tests.

    70k GitHub stars~2k tokensUpdated today
    DevelopmentAuto-check passed
  • Add Bug

    remotion-dev/remotion

    Official

    Add a new Remotion bug entry to packages/bugs/api/[v].ts. An agent skill from remotion-dev/remotion.

    63k GitHub stars~215 tokensUpdated today
    Media & CreativeAuto-check passed
  • Bug

    ccch1mneyyy/dsh-TUI

    Turn a reported defect into an actionable bug report or issue draft.

    4.2k GitHub stars~342 tokensUpdated today
    Testing & QAAuto-check passed
  • Bug Bounty

    awarexone/Agentic-Bug-Hunter

    Complete bug bounty workflow — recon, pre-hunt learning, vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling…

    5.3k GitHub stars~20k tokensUpdated yesterday
    SecurityAuto-check: warnings
  • Logical Properties

    thedaviddias/Front-End-Checklist

    A skill your agent uses when reviewing stylesheets, component styles, and responsive behavior related to Use CSS logical properties for i18n and RTL support.

    74k GitHub stars~526 tokensUpdated 3 days ago
    Frontend & DesignAuto-check passed
  • Azure Functions

    davila7/claude-code-templates

    Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns.

    32k GitHub starsUsed in 2 repos~344 tokens
    Backend & APIsAuto-check passed

More from sickn33/agentic-awesome-skills

All 1,493 skills in this repo
  • Liuguang Banlan UI

    sickn33/agentic-awesome-skills

    Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • User Thoughts Memory

    sickn33/agentic-awesome-skills

    Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Using LWC Memory and Graphs

    sickn33/agentic-awesome-skills

    Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.

    47k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed
  • Find Complementary Founders

    sickn33/agentic-awesome-skills

    Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.

    47k GitHub starsUsed in 1 repo~4.8k tokens
    Auto-check passed
  • Whatsapp Cloud API

    sickn33/agentic-awesome-skills

    Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.

    47k GitHub starsUsed in 2 repos~4.5k tokens
    Auto-check passed
  • Cline Pilot

    sickn33/agentic-awesome-skills

    Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.

    47k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed

Questions about Logic Review

What does Logic Review do?

Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Logic Review is an agent skill from sickn33/agentic-awesome-skills. Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).

How do I install Logic Review in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill logic-review -a claude-code`. Or copy the skill folder (skills/logic-review in sickn33/agentic-awesome-skills) into .claude/skills/logic-review in your project. Claude Code loads it when a task matches its description.

How do I install Logic Review in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill logic-review -a codex`. Or copy the skill folder (skills/logic-review in sickn33/agentic-awesome-skills) into .agents/skills/logic-review in your project. Codex loads it when a task matches its description.

Can I use Logic Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill logic-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/logic-review, .gemini/skills/logic-review, .github/skills/logic-review and .opencode/skills/logic-review in your project.

What does Logic Review need to run?

SKILL.md names no scripts, command-line tools or credentials: Logic Review is instructions for the agent only.

Does Logic Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Logic Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Logic Review use?

Logic Review is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Logic Review use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Logic Review?

Skills that share tags, products or a category with Logic Review: Mole Bug Patterns (tw93/Mole, 70k stars), Add Bug (remotion-dev/remotion, 63k stars), Bug (ccch1mneyyy/dsh-TUI, 4.2k stars) and Bug Bounty (awarexone/Agentic-Bug-Hunter, 5.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Logic Review?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.