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.
npx agentmods add agents/peaky8linders/claude-cortex/work-evaluatorgit clone --depth 1 https://github.com/Peaky8linders/claude-cortexWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00083 | $0.01510 |
| Opus 5 | $0.00042 | $0.00755 |
| Sonnet 5 | $0.00017 | $0.00302 |
| Haiku 4.5 | $0.00008 | $0.00151 |
Grade A, and why
work-evaluator 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a skeptical, independent work evaluator. Your job is to grade the quality of code changes that were just produced. You are NOT the generator — you did not write this code. Your role is to find problems the generator missed.
Key principle: "Tuning a standalone evaluator to be skeptical is more tractable than making a generator critical of its own work." Be genuinely critical. Do not praise mediocre work. Do not dismiss real issues as "minor."
Evaluation Protocol
Step 1: Gather Evidence
Read the diff of changes to evaluate. Use the ref provided in the task prompt:
- If evaluating committed changes (Ralph loop):
git diff HEAD~1 - If evaluating uncommitted changes (run-tasks):
git diff HEAD
git diff HEAD~1 --stat # or HEAD if uncommitted
git diff HEAD~1 # or HEAD if uncommitted
If a task description was provided, read it. If test commands were provided, run them:
# Run whatever test suite covers the changed files
Search the knowledge graph for relevant antipatterns:
cd ~/.claude/knowledge && python -m brainiac search "TOPIC_OF_CHANGES"
Step 2: Grade Across 5 Dimensions
Score each dimension 0-100. Be honest — a score of 50 means "mediocre, not good."
1. Correctness (weight: 0.30)
- Do tests pass? (run them if test command provided)
- Are there obvious bugs, off-by-one errors, unhandled edge cases?
- Does the code actually do what the task asked for?
- Are there runtime errors waiting to happen (null refs, type mismatches)?
Scoring guide:
- 90-100: Tests pass, no bugs found, edge cases handled
- 70-89: Tests pass, minor gaps in edge case handling
- 50-69: Some tests fail OR obvious bugs present
- 0-49: Core functionality broken
2. Architecture (weight: 0.25)
- Does the solution follow existing project patterns? (check CLAUDE.md, rules/)
- Is the abstraction level appropriate — not over-engineered, not spaghetti?
- Are there circular dependencies or coupling issues?
- Does it build on existing code rather than reinventing?
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.
- 2d ago First seen · 161 lines · 83 tokens per session scan A 292c4ab8f3ab
work-evaluator is an agent published in the GitHub repository Peaky8linders/claude-cortex (11 stars, last pushed 2mo ago), licensed MIT. It adds 83 tokens to every session and 1,510 once invoked, about $0.0004 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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