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.
git clone --depth 1 https://github.com/AKSoftCode/aicrewWrote 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/aksoftcode/aicrew/benchmark)<a href="https://agentmods.dev/commands/aksoftcode/aicrew/benchmark"><img src="https://agentmods.dev/badge/commands/aksoftcode/aicrew/benchmark/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/aksoftcode/aicrew/benchmark"><img src="https://agentmods.dev/badge/commands/aksoftcode/aicrew/benchmark.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.00380 |
| Opus 5.5 | $0.00006 | $0.00152 |
| Sonnet 5.5 | $0.00003 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
benchmark 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 1mo 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
Default output: caveman/lean — terse by default.
/normalor/lean offfor verbose.
/benchmark
Measure aicrew skill quality, trigger accuracy, token efficiency, and pipeline timing.
Equivalent actions
| Method | Command |
|---|---|
| CLI | aicrew benchmark (planned) |
| Codex skill | aicrew-benchmark |
| This slash command | /benchmark [type] |
Benchmark types
If not in $ARGUMENTS, ask:
What should we benchmark?
- trigger — does the right skill fire for a given prompt?
- quality — does a skill produce the expected sections/format?
- tokens — lean vs normal mode token counts for the same task
- timing — wall-clock time for each /dev or /fix phase
- all — full harness benchmark
Wait for answer.
Output format
BENCHMARK:
Skill/Phase | Trigger | Quality | Tokens | Timing
--------------|---------|---------|--------|-------
aicrew-dev | ✓ | ✓ | 1200 | 4.2s
aicrew-fix | ✓ | ✓ | 800 | 2.1s
aicrew-quick | ✓ | ✓ | 600 | 1.8s
...
Checkpoint
After each benchmark type: report results and ask "Continue with next type?"
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.
- 1mo ago First seen · 48 lines · 16 tokens per session scan A aa9d8d2cb023
benchmark is a command published in the GitHub repository AKSoftCode/aicrew (3 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 380 once invoked, about $0.0001 per session on Opus 5.5. 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
plan
Execute the implementation planning workflow using the plan template to generate design artifacts.
implement
Execute implementation by processing atomic task files one at a time with Context Pinning (Atomic Traceability Model).
cleanup
Detect and remove orphaned code, unused components, dead routes, and stale database artifacts.
clarify
Pre-plan interview — resolves spec ambiguity (v0.1 contract), pins the architectural lurkers from .specify/knowledge/architectural-lurkers.yaml, fires trigger-driven probes from .specify/knowledge/triggers.yaml, walks compliance scope, and writes decisions to specs/defaults/registry.yaml with provenance tagging.
specify
Create or update the feature specification from a natural language feature description.
registry
Discover, create, or update the Project Defaults Registry. Scans project manifests (package.json, pyproject.toml, Cargo.toml, go.mod, etc.), batches findings for HITL confirmation, then writes specs/defaults/registry.yaml with a full audit trail in changelog.md.