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/rhan1/crewlineWrote 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/rhan1/crewline/dispatch)<a href="https://agentmods.dev/commands/rhan1/crewline/dispatch"><img src="https://agentmods.dev/badge/commands/rhan1/crewline/dispatch/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/rhan1/crewline/dispatch"><img src="https://agentmods.dev/badge/commands/rhan1/crewline/dispatch.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 | $0.00015 | $0.00653 |
| Opus 5.5 | $0.00006 | $0.00261 |
| Sonnet 5.5 | $0.00003 | $0.00131 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
dispatch scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- UI component → dev server + browser/curl verification How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the dispatch workflow for the task below using the specified model. Do NOT shortcut the spec-writing or smoke-test steps.
The first word of $ARGUMENTS is the model ID (e.g. codex, gemini, qwen, ollama, llm). Everything after it is the task.
Task
$ARGUMENTS
Workflow
-
Parse the model ID. Extract the first token of
$ARGUMENTSas the model ID; the rest is the task description. If the model ID is blank or unrecognized, check~/.claude/orchestrator-models.jsonfor available models and ask the user which to use. -
Plan. Read the referenced files, pattern sources, and data endpoints yourself. If anything essential is missing (target path, output shape, data-source URL), ask before writing the spec — thin specs produce thin code.
-
Write the spec to
/tmp/dispatch-<model_id>-<short-task-name>-<unix-ts>.txt. Cover:- Exact target file path
- 2–3 pattern files to mirror (style + error-handling conventions)
- Data sources and expected fields
- Output shape (JSON for APIs, component signature for UI)
- Explicit "do not do" constraints — no npm/git/vercel/deploy, no tests/READMEs, no modifying other files, no network validation
-
Dispatch via
~/.claude/scripts/llm-dispatch.sh <model_id> <spec-path> <short-task-name>. The wrapper runs the CLI, captures elapsed time + exit code to the model'slast_file(path in~/.claude/orchestrator-models.json), and tees the full log to~/.claude/logs/llm-dispatch-<model_id>-<ts>.log. -
Smoke-test the output before claiming success:
- API handler → mock
req/resNode harness against live data - UI component → dev server + browser/curl verification
- Script → run against real input
- Analysis/summary → spot-check claims against source data
- API handler → mock
-
Fix small bugs directly (< 10 lines). Re-dispatch only if the change is substantial. If the chosen model fails or rate-limits, re-dispatch to a different model ID —
/dispatch codex <task>or/dispatch gemini <task>.
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.
- 2mo ago First seen · 44 lines · 15 tokens per session scan A 6481959ac129
dispatch is a command published in the GitHub repository rhan1/crewline (2 stars, last pushed 11d ago), licensed MIT. It adds 15 tokens to every session and 653 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.