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/implementernpx skills add MountainUnicorn/add --skill implementergit 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 | $0.00019 | $0.02727 |
| Opus 5 | $0.00010 | $0.01363 |
| Sonnet 5 | $0.00004 | $0.00545 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade A, and why
implementer 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADD Implementer Skill v{{VERSION}}
Write minimal production-quality code to make failing tests pass. This is the GREEN phase of TDD.
Overview
The Implementer takes failing tests (from test-writer) and writes the smallest amount of code necessary to make them all pass. The goal is:
- Make tests green with minimal code
- No over-engineering or premature optimization
- Production-quality implementation (no shortcuts)
- Defer non-essential features for future work
- Maintain clean architecture and separation of concerns
Pre-Flight Checks
- Verify test files exist and fail
- Read test file(s) from path provided by test-writer
- Run tests to confirm they fail:
npm testorpython -m pytest - Capture baseline: count of failing tests
- Halt if tests don't exist or already pass
1b. Read the impact hint (v0.9.0 — AC-022)
-
The tdd-cycle orchestrator emits a files-likely-affected block via
core/lib/impact-hint.sh. Consume it as part of this skill's context:## Files likely to need changes - app/auth.py - app/session.py ## Files to be careful around (recent anti-pattern learnings exist) - app/session.py [L-042] -
Use the first list as the starting set for Step 2 (Design Implementation)
-
Treat the second list as warnings: those paths have recent anti-pattern learnings — read the learning (by ID) before editing
-
If the hint reports "No source files implied by RED diff" (AC-024), fall back to the spec's acceptance criteria for implementation targets
-
Read the feature spec
- Load spec file from argument
- Extract feature name, acceptance criteria, requirements
- Understand the "what" before implementing
-
Load test mapping
- Read tests/{feature}-mapping.md (created by test-writer)
- Understand which tests map to which ACs
- Ensures implementation covers all required ACs
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 · 379 lines · 19 tokens per session scan A acea0c239c1c
implementer is a skill published in the GitHub repository MountainUnicorn/add (11 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 2,727 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.