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/nguyenvanphituoc/shapeup-sdlc-plugin/evalgit clone --depth 1 https://github.com/nguyenvanphituoc/shapeup-sdlc-pluginWhat 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 | $0.00018 | $0.00203 |
| Opus 5 | $0.00009 | $0.00102 |
| Sonnet 5 | $0.00004 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
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.
What it actually says
Use the spec-evaluator skill on $ARGUMENTS.
The single judge. Two modes, chosen by the arguments:
--task TASK-NNN— grade one task against its acceptance criteria.--spec <folder> --feature <slug> --single-pass— the once-per-round verdict on the whole board.
Round mode is watched: a PreToolUse hook (GATE L2) warns while any task on the board is
unfinished, naming the offenders — advisory since ADR-0001, so the call proceeds, but a verdict
taken now grades a partial board and the warning is recorded. The correct response is to route
back to /build and finish them — do not shrug the warning off, and do not use --task as a
loophole to simulate a round verdict piecemeal.
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 · 17 lines · 18 tokens per session scan A d6297876b91b
eval is a command published in the GitHub repository nguyenvanphituoc/shapeup-sdlc-plugin (2 stars, last pushed 13d ago), licensed MIT. It adds 18 tokens to every session and 203 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
project
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toh-help
Display all Toh Framework commands and quick usage guide.
feature
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update-workflow
Regenerate an existing custom workflow's shortcut command wiring from the current swarm template.
test-feature
Test a React Native feature on the running simulator/emulator. Verifies UI, user flows, and internal state. Generates a persistent Maestro test file.
deep-audit.skeleton
Aciklama: Bu bolum Bootstrap tarafindan manifest verileriyle doldurulur. Gerekli manifest alanlari: project.description, stack.primary, project.structure, project.subprojects, stack.orm, stack.authmethod Ornek cikti.