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 commands/jayvee/aigon/feature-evalgit clone --depth 1 https://github.com/jayvee/aigonWrote 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.
[](https://agentmods.dev/commands/jayvee/aigon/feature-eval)<a href="https://agentmods.dev/commands/jayvee/aigon/feature-eval"><img src="https://agentmods.dev/badge/commands/jayvee/aigon/feature-eval.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00010 | $0.00932 |
| Opus 5 | $0.00005 | $0.00466 |
| Sonnet 5 | $0.00002 | $0.00186 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
feature-eval 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 3d 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.
This is a copy
98% identical to afe — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aigon-feature-eval
Evaluate a feature implementation. Works in Drive mode (code review) and Fleet mode (comparison).
Argument Resolution
If no ID is provided or doesn't match an active feature, run aigon feature-list --active, filter to matches, and ask the user which.
Step 1: Run the CLI
aigon feature-eval {{args}}
# optional: --allow-same-model-judge # suppress same-family bias warning
This records the evaluation lifecycle state, refreshes the generated view, creates ./docs/specs/features/evaluations/feature-{{args}}-eval.md, detects mode (Drive or Fleet), warns on same-family evaluator/implementer, and commits owned artifacts. The spec body is printed inline — use that copy; do not re-run aigon feature-spec.
Step 2: Review the implementation(s)
Drive Mode (code review)
- Read the implementation log:
./docs/specs/features/logs/feature-{{args}}-*-log.md git diff main...feature-{{args}}-*- Check spec compliance, code quality, testing, documentation, security.
Fleet Mode (comparison)
For each agent worktree at ../feature-{{args}}-<agent>-*:
- Read the implementation log from the worktree
- Run
git diff main...HEADin each worktree - Check spec compliance
Bias guard:
feature-evalwarns automatically on same-family eval. Pass--allow-same-model-judgeto suppress if intentional.
Step 3: Write the evaluation
Update ./docs/specs/features/evaluations/feature-{{args}}-eval.md.
Drive Mode
Complete the checklist (Spec Compliance, Code Quality, Testing, Documentation, Security) and add Strengths, Areas for Improvement, and an Approval decision (Approved / Needs Changes).
Fleet Mode
Use this exact structure — scoring table, then summary table, then Strengths/Weaknesses, then Recommendation.
| Criteria | cx | ag |
|---|---|---|
| Code Quality | /10 | /10 |
| Spec Compliance | /10 | /10 |
| Performance | /10 | /10 |
| Maintainability | /10 | /10 |
| **Total** | **/40** | **/40** |
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.
- 3d ago First seen · 88 lines · 10 tokens per session scan A 01b9b943a3f4
feature-eval is a command published in the GitHub repository jayvee/aigon (25 stars, last pushed 5d ago), licensed Apache-2.0. It adds 10 tokens to every session and 932 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to afe, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
pr-ready
Run the project's pre-commit review loop to determine whether the current branch is ready to push — lint, tests, parallel pr-review-toolkit agents plus an over-engineering audit, fix-and-re-run until convergence.
audit-mirage-analyze
Phases 2-3 and 7 of auditing-green-mirage: systematic line-by-line audit, the Green Mirage Patterns, named assertion shapes, and the fix-verification Test Adversary prompt.
mach12:pr-validation-assessment
Independently assess executable PR candidates and commit accepted proofs.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
test
Run the repository's actual test suite: every ecosystem's canonical runner — NOT run is never green.
advanced-code-review-context
Advanced Code Review Phase 2: Context Analysis - load previous reviews, PR history, declined items.