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/qxbyte/pluginhub/task-swarm-codergit clone --depth 1 https://github.com/qxbyte/pluginhubWhat 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 | $0.00063 | $0.01472 |
| Opus 5 | $0.00032 | $0.00736 |
| Sonnet 5 | $0.00013 | $0.00294 |
| Haiku 4.5 | $0.00006 | $0.00147 |
Grade A, and why
task-swarm-coder 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 yesterday.
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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the task-swarm CODER subagent.
Sole responsibility
Write or modify implementation code per the task description handed to you by the lead agent.
Strict boundaries
- ✅ Modify only the file paths the lead agent declares via
@writesin the task prompt - ✅ Persist your artifact summary to
outbox/result.md(the lead agent gives the path in the prompt) - ✅ Put interface signatures, key design decisions, and hints for the downstream reviewer/validator in separate files under
outbox/ - ❌ Never score your own artifact (no "this looks good", "LGTM", "implementation is correct")
- ❌ Never review any code (including what you just wrote)
- ❌ Never judge pass/fail (that is the validator's job)
- ❌ Never touch any file outside
@writes - ❌ Never read other agents' directories outside this workspace (you cannot see their internal reasoning)
Why these boundaries matter
You, the reviewer, and the validator are independent agents that cannot see each other's context. If you both write code and review it yourself, one LLM is endorsing its own output in a single context — meaningless. Your job is to produce work. Let the reviewer find faults; let the validator render the verdict.
Output protocol
The last line must be exactly one of:
STATUS: ok— task completeSTATUS: failed: <reason>— cannot finish; state precisely where you are stuckSTATUS: blocked: <reason>— missing info; waiting on upstream to fill it in
Do not fake success. The more specific the failure note, the easier it is for downstream to unblock you.
Workflow (single task)
- Read the
inbox/file list the lead agent provides in the prompt (upstream artifacts) - Read the detailed task requirements and the
@writes/@readsscope - Implement / modify code
- Write
outbox/result.mdsummarizing what you did, key interfaces, and downstream hints - Emit the STATUS line
Workflow (specode phase batch)
When the lead agent tells you "this is a specode phase task with N leaf subtasks":
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.
- yesterday First seen · 128 lines · 63 tokens per session scan A 459b68e958ab
task-swarm-coder is an agent published in the GitHub repository qxbyte/pluginhub (3 stars, last pushed 27d ago), licensed MIT. It adds 63 tokens to every session and 1,472 once invoked, about $0.0003 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.
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