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/elaborate-specnpx skills add danielvm-git/bigpowers --skill elaborate-specgit 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 | $0.00055 | $0.00910 |
| Opus 5 | $0.00028 | $0.00455 |
| Sonnet 5 | $0.00011 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
elaborate-spec 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elaborate Spec
Turn a rough idea into a clear specification through focused dialogue. No code is written during this skill — the output is shared understanding and a refined problem statement.
HARD GATE — Do NOT proceed with planning or implementation until the problem space is clearly understood. Success criteria, actors, and scope must be explicit before drafting a plan.
Process
1. Listen first
Let the user describe their idea in their own words. Do not interrupt or redirect. Take notes on:
- The core problem they're trying to solve
- Who is affected (actors)
- What success looks like to them
- Any constraints they've already identified
2. Ask clarifying questions
Ask one question at a time. Work through these areas:
Problem clarity
- What is the current behavior (or lack of behavior) that prompted this?
- Who experiences this problem? How often?
- What's the cost of not solving it?
Solution boundaries
- What is explicitly IN scope?
- What is explicitly OUT of scope?
- Are there existing solutions (internal or external) this replaces or integrates with?
Success criteria
- How will you know this is done?
- What does the happy path look like end-to-end?
- What are the key failure modes to handle?
Constraints
- Any performance requirements?
- Any compatibility constraints (existing APIs, data formats)?
- Any non-negotiable implementation decisions already made?
2.5. Multiple Interpretations (HARD GATE)
HARD GATE — If the request admits ≥2 valid interpretations, do NOT guess. You must list them and ask the user to choose before proceeding. Proceeding with unresolved ambiguity is a failure of integrity.
Present the options clearly:
"I see two ways to read this:
- [Interpretation A] — my recommendation because [reason]
- [Interpretation B] Which is closer to what you mean?"
3. Surface hidden assumptions
Once the user has answered the main questions, probe for assumptions:
- "You mentioned X — does that mean Y is also true?"
- "What happens when Z fails?"
- "Is this for internal users, external users, or both?"
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 · 102 lines · 55 tokens per session scan A 91bb7f8a0584
elaborate-spec is a skill published in the GitHub repository danielvm-git/bigpowers (156 stars, last pushed 26d ago), licensed MIT. It adds 55 tokens to every session and 910 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.
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