semantic-slicing

semantic-slicing is a skill for Codex from vincentkoc/dotskills. It costs 43 tokens per session (1,453 once invoked), scanned A, original, MIT.

A method for dividing a large software repository into smaller review areas using code maps, security findings, visual information, and project discussions. A repository is the complete set of files and history for a project.

In plain words
What is it for?
Use it to prepare focused reviews of large repositories, rank risky files, group related issues, and compare code with issue or support data.
Why use it?
It helps reviewers focus their limited time on the parts most relevant to security, regressions, architecture, or maintenance.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions AGENTS.md; mentions Codex; built for openclaw.

Good fit Use it to prepare focused reviews of large repositories, rank risky files, group related issues, and compare code with issue or support data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vincentkoc/dotskills/semantic-slicing
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add vincentkoc/dotskills --skill semantic-slicing
Clone the repo
git clone --depth 1 https://github.com/vincentkoc/dotskills

Made for: Codex.

Its marketplace also offers this one on its own, as the plugin semantic-slicing/plugin install semantic-slicing after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for semantic-slicing

README.md
[![agentmods](https://agentmods.dev/badge/skills/vincentkoc/dotskills/semantic-slicing.svg)](https://agentmods.dev/skills/vincentkoc/dotskills/semantic-slicing)
Your own site
<a href="https://agentmods.dev/skills/vincentkoc/dotskills/semantic-slicing"><img src="https://agentmods.dev/badge/skills/vincentkoc/dotskills/semantic-slicing.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,453 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Agent Snooping · line 83
    Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.
    Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
  • medium Rogue Agent · line 27
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.01453
Opus 5 $0.00022 $0.00727
Sonnet 5 $0.00009 $0.00291
Haiku 4.5 $0.00004 $0.00145

Measured 8d ago against content hash 6db91d303477, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

semantic-slicing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/semantic-map.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/semantic-slicing/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Semantic Slicing

Purpose

Turn a large repo into reviewable semantic slices with evidence. Use code shape, threat candidates, issue clusters, and support chatter together so review budget lands on the right parts of the system.

Default stance: map locally first, rank second, spend agent/security-review budget last.

When to use

  • Setting up or running openclaw/clawpatch against a target repo.
  • Setting up or running vercel-labs/deepsec against a target repo.
  • Producing a local visual map of feature slices, risky files, ownership clusters, or review targets.
  • Cross-checking code slices against gitcrawl issue/PR data or discrawl Discord/support data.
  • Planning a focused security, regression, architecture, or maintainer-review pass for a large repo.

Workflow

  1. Create a scratch run directory outside the target checkout, usually ~/.semantic-slicing/<repo>/<timestamp>.
  2. Read target repo instructions before scanning. For OpenClaw, read root AGENTS.md; subtree guides matter when reviewing a slice.
  3. Verify tool setup:
    • clawpatch: clone/build openclaw/clawpatch, then run clawpatch init, clawpatch map, clawpatch status.
    • deepsec: clone/build vercel-labs/deepsec, scaffold a scratch workspace, then run deepsec scan.
    • gitcrawl: run gitcrawl doctor --json, then pull clusters/threads for related issue evidence.
    • discrawl: run discrawl doctor --json and discrawl status --json; use search/digest only when support chatter is relevant.
  4. Run deterministic maps before AI review:
    • Clawpatch feature map for entrypoints/packages/config/test slices.
    • Deepsec regex scan for candidate threat surfaces.
    • Optional repo git overlay for CODEOWNERS routing, tracked files, code/test/doc shape, and recent churn.
    • Optional gitcrawl/discrawl lookups for historical pain around the same files, components, or symptoms.
  5. Run scripts/semantic-map.mjs to merge the local artifacts into semantic-map.html and semantic-map.json.
    • Sparse mode is on by default and omits dotfile/config trees, docs, changelog files, and mobile app trees so core review stays focused.
    • Use --no-sparse or --sparse false for the full repo; use --sparse-exclude <csv> and --sparse-include <csv> to tune the filter.
  6. Review the board in product order:
    • review lanes first: semantic shape, ownership routing, development pressure, security pressure, issue pressure, support pressure,
    • focus controls second: lens and system filters that narrow the matrix without duplicating rows,
    • overall lens matrix third: the single slice-row table with comparable bars for semantic, ownership, development, security, issue, and support lenses,
    • agent handoff packet fourth: compact JSON for follow-up agents,
    • evidence tables last: raw-ish overlays for audit, not the primary reading path.
  7. Choose a cost size before running AI stages:
    • low: deterministic maps only; no deepsec process or real clawpatch review.
    • medium: one to three explicit files/features with high-risk slugs, batch size 1, concurrency 1, and a turn cap.
    • high: broader AI processing or multiple feature reviews; requires an explicit budget/time decision.
  8. Run AI only at the chosen size:
    • clawpatch review --feature <id> or a small --limit.
    • deepsec process --files <csv> or tightly scoped --filter plus --only-slugs.
  9. Report exact artifact paths, run IDs, counts, cost size, exclusions, and skipped expensive stages.

Read the full file on GitHub · 93 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 93 lines · 43 tokens per session scan A 6db91d303477

Subscribe to this mod's changes

semantic-slicing is a skill published in the GitHub repository vincentkoc/dotskills (102 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 1,453 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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