Borrowing it
Nothing to install: this file belongs to mcwalrus/oppertunity-party. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mcwalrus/oppertunity-party/main/.agents/skills/to-spec/SKILL.mdgit clone --depth 1 https://github.com/mcwalrus/oppertunity-partyWrote 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/mcwalrus/oppertunity-party/to-spec)<a href="https://agentmods.dev/skills/mcwalrus/oppertunity-party/to-spec"><img src="https://agentmods.dev/badge/skills/mcwalrus/oppertunity-party/to-spec.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 | $0.00030 | $0.00637 |
| Opus 5 | $0.00015 | $0.00318 |
| Sonnet 5 | $0.00006 | $0.00127 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
to-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 5d 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.
This is a copy
94% identical to to-spec — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill takes the current conversation context and codebase understanding and produces a spec (you may know this document as a PRD). Do NOT interview the user — just synthesize what you already know.
The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.
Process
-
Explore the repo to understand the current state of the codebase, if you haven't already. Use the project's domain glossary vocabulary throughout the spec, and respect any ADRs in the area you're touching.
-
Sketch out the seams at which you're going to test the feature. Existing seams should be preferred to new ones. Use the highest seam possible. If new seams are needed, propose them at the highest point you can. The fewer seams across the codebase, the better - the ideal number is one.
Check with the user that these seams match their expectations.
- Write the spec using the template below, then publish it to the project issue tracker. Apply the
ready-for-agenttriage label - no need for additional triage.
Problem Statement
The problem that the user is facing, from the user's perspective.
Solution
The solution to the problem, from the user's perspective.
User Stories
A LONG, numbered list of user stories. Each user story should be in the format of:
- As an , I want a , so that
This list of user stories should be extremely extensive and cover all aspects of the feature.
Implementation Decisions
A list of implementation decisions that were made. This can include:
- The modules that will be built/modified
- The interfaces of those modules that will be modified
- Technical clarifications from the developer
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 76 lines · 30 tokens per session scan A 267638edd513
to-spec is a skill published in the GitHub repository mcwalrus/oppertunity-party (0 stars, last pushed 13d ago), licensed MIT. It adds 30 tokens to every session and 637 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to to-spec, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
next-action-operator
Purpose: choose the next best operator action for a tracked FYI request using local state, snapshot analysis, and follow-up packs.
timeline-operator
Use this skill to review the event and snapshot history of a tracked FYI request, identify stale requests, and prepare concise operator notes.
update-status
Use when the user reports progress on a role and wants the board updated. Triggers: /update-status, "move X to tech interview", "I got an HR screen with Y", "Z rejected me", "got an offer from W", "schedule my interview with V". Moves a vacancy through the pipeline, appends a timestamped statusDetail (preserving…
launch-readiness
Interrogates a planned launch — what is unowned, the rollback plan, the comms and enablement gaps, what breaks if adoption is 10x or 0.1x of expectation, and the explicit kill criteria. Produces the gap list specific to this launch; never a generic checklist.
neural-tasks
Manage the full task lifecycle in the neural knowledge graph — list, create, update status and priority. Tasks connect to code nodes and appear in every prompt via the context hook.
neural-add-task
Manually log a task or phase into the neural memory knowledge graph. Links the task to related code files so it appears in semantic search results.