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/blueprintos/analysis-to-delivery/using-superpowersnpx skills add BlueprintOS/analysis-to-delivery --skill using-superpowersgit clone --depth 1 https://github.com/BlueprintOS/analysis-to-deliveryWrote 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/skills/blueprintos/analysis-to-delivery/using-superpowers)<a href="https://agentmods.dev/skills/blueprintos/analysis-to-delivery/using-superpowers"><img src="https://agentmods.dev/badge/skills/blueprintos/analysis-to-delivery/using-superpowers.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00563 |
| Opus 5 | $0.00016 | $0.00282 |
| Sonnet 5 | $0.00007 | $0.00113 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
using-superpowers 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 6d 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
Using Superpowers — 开发实施入口
Contract
- 输入: 已签字的设计产物,或明确的实施目标
- 输出: 推荐的 superpowers bridge skill 与起始点
- 门控: 设计阶段已签字,或用户明确请求进入实施
- Required rules:
stage-gate - Required paths: 无
- 下一步:
/brainstorming、/design-an-interface、/domain-modeling、/writing-plans、/tdd、/executing-plans或/verification-before-completion
本 skill 是 桥接层,不复制 superpowers 官方内容,具体纪律以
<SUPERPOWERS_SKILL_ROOT>/<name>/SKILL.md为准。 完整 superpowers 体系文档:https://github.com/obra/superpowers
我该用哪个?
| 我想做的事 | 用这个 superpowers skill |
|---|---|
| 反复提问澄清,产出设计稿 | /brainstorming |
| 设计接口契约 | /design-an-interface |
| 梳理领域模型 | /domain-modeling |
| 把 spec 拆成可执行计划(每个 ≤ 2h) | /writing-plans |
| 按测试驱动开发(红绿循环) | /tdd |
| 逐步按计划执行 + 复盘 | /executing-plans |
| 完成任务前验证(铁律) | /verification-before-completion |
与 analysis-to-delivery 的衔接
- 设计完成(跑完
/analysis-delivery-workflow或/dev-design后)→ 从这里进入实施 - 实施过程纪律(stage-gate / 设计回测 / 任务复盘)来自
disciplines/stage-gate,由 superpowers 链路自动加载 - 实施完成 → 回到
/handoff出交接文档
调用规则
我会先问你 1-2 个澄清问题,然后告诉你从哪个 superpowers skill 开始。
⚠️ 本 skill 不直接执行开发,只做"告诉你从哪个 superpowers skill 开始 + 串接 9 阶段 ↔ 5 步实施"的导航。
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.
- 6d ago First seen · 46 lines · 33 tokens per session scan A 9dfa98bb6438
using-superpowers is a skill published in the GitHub repository BlueprintOS/analysis-to-delivery (26 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 563 once invoked, about $0.0002 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
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly…
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
statistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required…
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
hypothesis-generation
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating…