Show Me Your Work Decision Log
cursor/plugins
Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.
Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model.
Install Jev Audit
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OneWave-AI/claude-skills jev-audit --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/jev-audit .claude/skills/jev-audit && rm -rf skills-srcUse ~/.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/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .claude/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/OneWave-AI/claude-skills/tree/main/jev-auditType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OneWave-AI/claude-skills jev-audit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/jev-audit .agents/skills/jev-audit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .agents/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OneWave-AI/claude-skills jev-audit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/jev-audit .cursor/skills/jev-audit && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .cursor/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/OneWave-AI/claude-skills.git --path jev-audit--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OneWave-AI/claude-skills jev-audit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/jev-audit .gemini/skills/jev-audit && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .gemini/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install OneWave-AI/claude-skills jev-auditInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/jev-audit .github/skills/jev-audit && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .github/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add OneWave-AI/claude-skills --skill jev-audit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OneWave-AI/claude-skills jev-audit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OneWave-AI/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/jev-audit .opencode/skills/jev-audit && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "jev-audit" agent skill from https://github.com/OneWave-AI/claude-skills/tree/main/jev-audit into .opencode/skills/jev-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jev-audit", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
jev-audit- GitHub stars
- 336
- Token cost
- ~1k tokens
- SKILL.md length
- 554 words
- Files
- 1
- Skills in repo
- 69
- Repo updated
- First seen
- Licence
- MIT
At a glance
Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model.
- Works in 5 steps: Find the candidates → Measure what it costs today → Price the swap → …
- Asked to cut AI inference cost
- SKILL.md covers 1. Find the candidates, 2. Measure what it costs today, 3. Price the swap and 4. Rank by payoff, not by ease, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Jev Audit is an agent skill from OneWave-AI/claude-skills. Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Autonomous loops. The repository describes itself as: 200+ production-ready Claude Code skills for sales, marketing, design, engineering, and AI agent architecture. Built and maintained by OneWave AI. The licence is MIT.
When your agent uses it
- Asked to cut AI inference cost
- Scoping a performance engagement for a client
- Reviewing an agent loop that feels slow
- Asked where could we use Jev here
Example prompts
- “where could we use Jev here”
- “/jev-audit”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit fc5b785. 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 (its code samples are bash).
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
Jev Audit loads about 1k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 554 words of instructions outside code blocks.
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 OneWave-AI/claude-skills at commit fc5b785, republished under its MIT licence (© OneWave-AI). 554 words, ~1,021 tokens.
Download SKILL.mdSave it as
.claude/skills/jev-audit/SKILL.md (or your agent's skills folder).- name
- jev-audit
- description
- Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.
Inference-cost audit
Most production LLM calls return a label, not prose — a route, a score, a yes/no, a category. Those calls pay for autoregressive generation they do not use. This audit finds them and prices the swap.
Deliverable: a ranked table of candidates with measured deltas. Sellable as a OneWave
engagement; same shape as marketing-brain — a repeatable audit run per client.
1. Find the candidates
Search for LLM calls, then filter to the ones whose output is a label:
grep -rnE "messages\.create|chat\.completions|generateText|\.invoke\(|anthropic\.|openai\." \
--include=*.{ts,tsx,js,py} . | grep -v node_modulesA call is a candidate when all of these hold:
- the prompt asks for one of a known, finite set of answers (≤255)
- the caller parses the response — JSON.parse, a regex, an enum lookup,
.trim() - nothing downstream shows a human the model's reasoning
- it runs often, or a user waits on it
Strong signals in the prompt text: "respond with only", "return JSON", "classify", "choose one of", "rate from 1 to", "answer yes or no", "do not explain".
Disqualifiers: the output is shown to a user, is used as content, needs a citation or justification, or the answer set is open-ended.
2. Measure what it costs today
Do not estimate. Instrument:
- calls/day — from logs or a counter, not a guess
- p50 and p95 latency — p95 is what users feel
- tokens in/out per call → current $/1k calls at the provider's list price
- is anything blocked on it — a user, a page render, an agent's next step
3. Price the swap
Benchmark against a labelled set from that call's real traffic (see jev-eval). Never
project from a vendor benchmark.
Reference numbers, measured on real records, 4 questions per record:
| latency | cost/1k | accuracy | |
|---|---|---|---|
| Jev hosted | 399 ms | $0.023 | matched Claude on a 15-record set |
| Von local | 174 ms | $0.00 | matched only with well-written criteria |
| Claude Haiku 4.5 | 2,322 ms | ~$0.55 | baseline |
Jev pricing: $0.042/MTok input, output free.
Show full SKILL.md (251 more words)Show less
4. Rank by payoff, not by ease
Rank each candidate on:
- volume × unit saving — the actual dollar figure
- is a human waiting — latency wins are worth more than cost wins on user-facing paths
- blast radius if wrong — a misrouted support ticket is cheap; a mis-scored transaction is not
- accuracy delta on the labelled set — anything below parity needs a confidence gate, and a gate has its own cost
Kill anything where volume is low. At a few hundred calls a day the savings round to zero and you have added a vendor.
5. Write it up
Per candidate: file and line, what it decides, calls/day, current latency and cost, projected latency and cost, measured accuracy delta, recommended gate, and a go / no-go with the reason.
Lead the summary with total projected monthly saving and the single biggest latency win.
Always include the limits: eval-set size, that Jev is early access with no SLA, and that a fallback to the existing call must stay wired.
Where this pays in the OneWave stack
- Sage widget routing — build it. A visitor is watching; 400 ms vs 2.3 s is the whole difference.
- RB2B visitor firehose — build it. The only stream with volume where 24x cheaper compounds.
- Lead intake classification — measure first. Correct, but a handful of leads a day.
- Lead watchdog — leave it. Nightly cron, nothing waits on it.
Related
jev-eval produces the accuracy numbers this audit depends on. jev-integrate is the
wiring workflow for anything that gets a go.
© OneWave-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in jev-audit of OneWave-AI/claude-skills.
Open the folder on GitHubat commit fc5b785
Compare with similar skills
Jev Audit 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Jev Audit this skillOneWave-AI/claude-skills | 336 | — | ~1k | Automated safety check: Pass | MIT | |
| Show Me Your Work Decision Logcursor/plugins | 11k | 8 repos | ~1.6k | Automated safety check: Pass | None | |
| Autoresearch Iteration Loopuditgoenka/autoresearch | 6.5k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Install Loop Engineeringcobusgreyling/loop-engineering | 11k | 1 repos | ~648 | Automated safety check: Pass | MIT | |
| LoopyForward-Future/loopy | 3.2k | — | ~3.9k | Automated safety check: Pass | MIT | |
| AI Performance Improvement Plantanweai/pua | 20k | 2 repos | ~6.9k | Automated safety check: Pass | MIT |
Similar skills
- Official
Autoresearch Iteration Loop
uditgoenka/autoresearch
Runs an autonomous modify, verify, keep-or-discard loop against any metric, with subcommands for planning, debugging, fixing, security audits, shipping and more.
Install Loop Engineering
cobusgreyling/loop-engineering
Installs Loop Engineering into a project through the single @cobusgreyling/loop CLI, scaffolding a report-only loop and a readiness score.
Loopy
Forward-Future/loopy
Discover, find, compare, audit, repair, adapt, craft, run, debrief, save, and prepare repeatable AI-agent loops for publication.
AI Performance Improvement Plan
tanweai/pua
Pushes an agent to exhaust every option, investigate before asking and take initiative beyond the literal request, instead of giving up or waiting passively.
LoopX Self Repair
loopx-project/loopx
Diagnoses surprising LoopX behavior, such as stale recommendations or tiny progress, assigns it to the responsible layer and repairs it at the lowest durable level.
More from OneWave-AI/claude-skills
All 69 skills in this repoCRM Data Cleanup
OneWave-AI/claude-skills
Finds duplicate and junk records in a CRM CSV export with fuzzy matching, normalizes fields and writes a reviewable merge plan plus import-ready files without touching the live CRM.
Design Export Repair
OneWave-AI/claude-skills
Repairs broken decks and PDFs exported from Claude Design or similar AI deck generators: clipped text, wrong fonts and corrupted .pptx package structure.
Bi Measure Builder
OneWave-AI/claude-skills
Writes, explains, debugs, and optimizes BI calculations - Power BI / Fabric DAX measures and calculated columns, Tableau calculated fields (FIXED/INCLUDE/EXCLUDE LOD expressions, table…
Bookkeeping Close
OneWave-AI/claude-skills
Categorizes transactions, reconciles bank and card statements to the ledger, works a month-end checklist and prepares a close package, without ever forcing a balance.
CSV and Excel Merger
OneWave-AI/claude-skills
Combines CSV, TSV and Excel files into one verified table with pandas, by stacking or joining, mapping columns, normalizing keys and removing duplicates.
Sec Filing Puller
OneWave-AI/claude-skills
Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table.
Related
Categories
Questions about Jev Audit
What does Jev Audit do?
Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Jev Audit is an agent skill from OneWave-AI/claude-skills. Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model.
When should I use Jev Audit?
Jev Audit fits situations like: asked to cut AI inference cost; scoping a performance engagement for a client; reviewing an agent loop that feels slow; asked where could we use Jev here.
How do I install Jev Audit in Claude Code?
Run `npx skills add OneWave-AI/claude-skills --skill jev-audit -a claude-code`. Or copy the skill folder (jev-audit in OneWave-AI/claude-skills) into .claude/skills/jev-audit in your project. Claude Code loads it when a task matches its description.
How do I install Jev Audit in Codex?
Run `npx skills add OneWave-AI/claude-skills --skill jev-audit -a codex`. Or copy the skill folder (jev-audit in OneWave-AI/claude-skills) into .agents/skills/jev-audit in your project. Codex loads it when a task matches its description.
Can I use Jev Audit 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 OneWave-AI/claude-skills --skill jev-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jev-audit, .gemini/skills/jev-audit, .github/skills/jev-audit and .opencode/skills/jev-audit in your project.
What does Jev Audit need to run?
SKILL.md names no scripts, command-line tools or credentials: Jev Audit is instructions for the agent only.
Does Jev Audit 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 Jev Audit 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 Jev Audit use?
Jev Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Jev Audit use?
About 1k tokens (SKILL.md is roughly 4.1k 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 Jev Audit?
Skills that share tags, products or a category with Jev Audit: Show Me Your Work Decision Log (cursor/plugins, 11k stars), Autoresearch Iteration Loop (uditgoenka/autoresearch, 6.5k stars), Install Loop Engineering (cobusgreyling/loop-engineering, 11k stars) and Loopy (Forward-Future/loopy, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jev Audit?
OneWave-AI (a GitHub organization) maintains it in OneWave-AI/claude-skills, which has 336 GitHub stars. The repository holds 69 skills in this directory. The repository was last updated on October 2, 2026.
Source: OneWave-AI/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.