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 skills/danielvm-git/bigpowers/delegate-tasknpx skills add danielvm-git/bigpowers --skill delegate-taskgit clone --depth 1 https://github.com/danielvm-git/bigpowersWhat 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 | $0.00069 | $0.00883 |
| Opus 5 | $0.00034 | $0.00441 |
| Sonnet 5 | $0.00014 | $0.00177 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
delegate-task 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
story: e45s30
Delegate Task
HARD GATE — HARD GATE — Delegated work must have clear success criteria and verification commands. The delegate must be able to verify completion independently.
Delegate a single complex task to a subagent with a two-stage review gate before accepting the result. Use when oversight of a single task matters more than speed.
Distinct from dispatch-agents: This skill runs one subagent sequentially with a mandatory review. dispatch-agents runs multiple subagents in parallel without inter-task review gates.
Subagent depth tiers (e45s30)
Select brief depth from task risk: and skill effort: before spawning:
| Tier | When | Brief includes |
|---|---|---|
full_maturity |
P0 stories, multi-file refactors, security work | Full template + CONVENTIONS excerpts + threat model if present |
standard |
Default implementation tasks | Goal, scope, out-of-bounds, constraints, verify, prior decisions |
minimal_decisive |
Light probes, read-only audits | Goal, verify, explicit file list (≤15 lines total) |
State depth: <tier> in the Agent tool description field.
Process
1. Define the task
Before spawning the agent, read specs/state.yaml if it exists. Then write a minimal self-contained brief using this template (brief size directly controls token cost and hallucination risk — do not pad):
Goal: [one sentence — specific, measurable outcome]
In scope: [explicit file or module list]
Out of bounds: [what NOT to do]
Constraints: [relevant CONVENTIONS.md rules, existing patterns, test requirements]
Verify: [runnable command]
Prior decisions: [relevant entries from specs/state.yaml — omit section if none apply]
Do not include full file contents, full conversation history, or decisions unrelated to this task.
2. Spawn the subagent (iterative retrieval, max 3 cycles)
Use the Agent tool with a fresh context per spawn. Pass prior decisions only via specs/state.yaml.
Cycle: dispatch → evaluate output vs goal → refine brief → re-spawn if needed (max 3 cycles).
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 · 92 lines · 69 tokens per session scan A 1ec07ee77d9c
delegate-task is a skill published in the GitHub repository danielvm-git/bigpowers (156 stars, last pushed 26d ago), licensed MIT. It adds 69 tokens to every session and 883 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 2026-08-30.
Other skills, from other repositories
pm/competitive-analysis
竞品调研和分析方法,通过系统性分析竞品的功能、体验和策略,发现差异化机会.
macrothink
User-invoked read-only pass for checking whether the current direction is tunnel-visioned: strip the session's bait, fan out 2 to 5 same-model fresh reads, and report divergence first without treating convergence as proof.
nba
Read the live cycle state and return the single highest-leverage next best action, not a menu. Use when a project is between phases, the author asks what to do next, too many valid threads are open, or the work needs re-entry into frame, build, drive, re0-memo, hate, re0-work, or ship.
bmad-dev-story
Execute story implementation following a context filled story spec file. Use when the user says "dev this story [story file]" or "implement the next story in the sprint plan".
bmad-domain-research
Conduct domain and industry research. Use when the user says "lets create a research report on [domain or industry]".
harness-engineering
Use when an agent workflow needs production-like runtime controls for context, tools, permissions, observability, scheduling, evaluation, recovery, or maintenance.