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/missingpackage/nightshift/spec-firstnpx skills add MissingPackage/nightshift --skill spec-firstgit clone --depth 1 https://github.com/MissingPackage/nightshiftWhat 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 | $0.00057 | $0.00481 |
| Opus 5 | $0.00028 | $0.00241 |
| Sonnet 5 | $0.00011 | $0.00096 |
| Haiku 4.5 | $0.00006 | $0.00048 |
Grade A, and why
spec-first 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 yesterday.
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
Spec First
Overview
The spec shape below is salvaged from this workspace's own best specs (the Feb 2026 MVP voice pipeline, a research-site brief) — the SpecKit engine is retired, the shape earned its keep. A spec is the PI's judgment made editable; the draft saves typing, the edit IS the judgment.
The shape (write to specs/<slug>.md, or .harness/goals/<slug>/SPEC.md when goal-bound)
# <Feature name>
## Overview — one paragraph: what and why now
## User stories — each: As/I want/so that + Acceptance criteria that are
OBSERVABLE BEHAVIORS (a tester could check each box)
## Read first — table: What | Where | Why (files the implementer must
understand before writing code)
## Non-goals — what this slice deliberately does NOT do
## Hard constraints — tech, UX, budget, deadline ("demo tra X giorni" goes HERE,
not in a firefight message later)
## Open questions — every inference marked [ASSUMED: …]; unresolved = not approved
Rules
- Acceptance criteria must be checkable without interpretation ("routing happens silently — no voice prompt about choosing a mode" is checkable; "good UX" is not).
- Draft fast, mark every guess
[ASSUMED], and hand back for editing. Never proceed to implementation with unresolved[ASSUMED]markers — the user's edit of those markers is the load-bearing judgment, not a formality. - The Read-first table is for the NEXT agent, not the user: real paths, one-line why each.
- Slice scope: if the stories exceed what SDD can verify in a few tasks, split the spec.
When NOT to use
Bug fixes (skill: root-cause), one-file changes, research experiments (predictions + directive files own that lane).
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.
- yesterday First seen · 43 lines · 57 tokens per session scan A 94479b9f5a66
spec-first is a skill published in the GitHub repository MissingPackage/nightshift (2 stars, last pushed 16d ago), licensed MIT. It adds 57 tokens to every session and 481 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 2026-08-31.
Other skills, from other repositories
baton-setup
One-time setup and health check for fable-baton. Use when the user asks to set up, configure, verify, or troubleshoot fable-baton - sets the default model to "best" (Fable 5 with Opus fallback) in /.claude/settings.json and verifies the plugin is fully installed.
licensing-tiers-data-governance
Implement subscription tiers with field-level access control, feature gating, rate limiting, and compliance tracking. Design data governance systems that enforce different access levels, retention policies, and regulatory requirements based on user subscription tier (Free, Pro, Enterprise).
data-infrastructure-at-scale
Build data infrastructure that scales from prototype to production. Use when architecting data pipelines, choosing data stores, planning for high throughput, or migrating to distributed systems. Covers caching, replication, sharding, message queues, and data lake architecture.
data-provenance
Track data lineage and provenance from source to consumption. Use when auditing data flows, debugging data quality issues, ensuring compliance (GDPR, SOX), or understanding data dependencies. Covers lineage tracking, impact analysis, data catalogs, and metadata management.
multi-source-data-conflation
Merge and reconcile data from multiple sources into a unified view. Use when integrating APIs, consolidating databases, building data warehouses, or creating master data. Covers entity resolution, conflict resolution, data quality, and real-time vs batch conflation.
software-architecture
Design scalable software systems with proven architectural patterns (MVC, microservices, event-driven), SOLID principles, system design trade-offs, and architectural decision records (ADRs).