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/mountainunicorn/add/specnpx skills add MountainUnicorn/add --skill specgit clone --depth 1 https://github.com/MountainUnicorn/addWhat 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 | $0.00014 | $0.01103 |
| Opus 5 | $0.00007 | $0.00551 |
| Sonnet 5 | $0.00003 | $0.00221 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
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 2d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADD Spec Command v{{VERSION}}
Create a feature specification through a structured interview. The spec becomes the source of truth for implementation.
Pre-Flight
- Verify
docs/prd.mdexists. If not, tell the user to run/add:initfirst. - Read
docs/prd.mdto understand the project context - Read
.add/config.jsonto understand environment and quality settings - If
--from-prd-sectionis provided, pre-populate answers from that PRD section - If
feature-nameargument is provided, use it. Otherwise, ask.
Phase 1: Feature Interview
Estimate questions upfront. Typical spec interview is 6-10 questions, ~5 minutes.
Let's define a specification for this feature.
This will take approximately {N} questions (~5 minutes).
The spec will include acceptance criteria, user test cases,
data models, and everything needed to start TDD.
Core Questions (ask 1-by-1)
Q1: "Describe the feature in one or two sentences. What should it do?" → Captures: feature description, feature name/slug
Q2: "Who uses this feature, and what's their goal?" → Captures: user story (As a {role}, I want {what}, so that {why})
Q3: "What are the must-have behaviors? List the things that MUST work for this feature to be complete." → Captures: acceptance criteria (AC-001, AC-002, etc.)
Q4: "Walk me through the happy path — step by step, what does the user do and see?" → Captures: primary user test case (TC-001)
Q5: "What should happen when things go wrong? Think about invalid input, network errors, missing data." → Captures: error handling, edge cases, additional test cases
Q6: "What data does this feature need? Think entities, fields, relationships." (Default: "I'll infer from the acceptance criteria") → Captures: data model
Q7 (if applicable): "Does this feature need API endpoints? If so, what operations?" → Captures: API contract
Q8 (if UI): "Describe the key UI states — loading, empty, error, success." → Captures: UI behavior, screenshot checkpoints
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.
- 2d ago First seen · 118 lines · 14 tokens per session scan A 18834e446e0a
spec is a skill published in the GitHub repository MountainUnicorn/add (11 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 1,103 once invoked, about $0.0001 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
swarm
Launching multi-agent parallel work with the Agentic SDLC. Use when a task benefits from decomposition into parallel subtasks.
finish
Completing a development branch for merge readiness. Use when implementation and tests pass and the branch needs formal preparation for review and merge.
grill
Interrogating requirements to validate before building. Use before swarm decomposition, design decisions on ambiguous features, or when scope creep risk is high.
team
Referencing the agent roster, roles, coordination model, and dispatch modes. Use when spawning agents or checking permissions.
ticket
Associate every PDS task with a GitHub issue. Orchestrator finds or creates the ticket, posts plan and acceptance criteria as a checkbox list, updates it as work progresses. Use at Phase 1 of every swarm.
triage
Triage insights into actionable GitHub issues across repos. Use after running /insights to convert analysis into tracked work.