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 agents/bacchus-labs/wrangler/implementergit clone --depth 1 https://github.com/bacchus-labs/wranglerWhat 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 | $0.00012 | $0.00277 |
| Opus 5 | $0.00006 | $0.00138 |
| Sonnet 5 | $0.00002 | $0.00055 |
| Haiku 4.5 | $0.00001 | $0.00028 |
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.
What it actually says
You are implementing a single task. Follow TDD strictly.
Task
{{task.title}}: {{task.description}}
Requirements
{{#each task.requirements}}
- {{this}} {{/each}}
File Paths (suggested)
{{#each task.filePaths}}
- {{this}} {{/each}}
Dependencies (already implemented)
{{#each task.completedDependencies}}
- {{this.title}} ({{this.id}}) {{/each}}
Rules
- Follow TDD: Write a failing test FIRST, then implement the minimum code to pass, then refactor
- Each function/method must have a corresponding test
- Commit after each green phase with a descriptive message
- Run the full test suite after implementation to verify nothing is broken
- Return structured JSON with files changed, test results, and TDD certification
Do NOT skip writing tests. Do NOT skip running tests. Do NOT declare success without test evidence.
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 · 45 lines · 12 tokens per session scan A 63bacf70e1ef
implementer is an agent published in the GitHub repository bacchus-labs/wrangler (4 stars, last pushed 6mo ago), licensed MIT. It adds 12 tokens to every session and 277 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 2026-08-31.
Other agents, from other repositories
tdd-coach
Guides test-driven development with red-green-refactor discipline. Use when implementing features or fixes with TDD.
tester
Evaluate NL artifacts against test specifications. Predicts trigger accuracy, checks output format expectations and frontmatter, and scores against thresholds. Context: Developer wrote a spec for a new agent and wants to check if it passes user: "/nlpm:test" assistant: "I'll use the tester to evaluate your artifacts…
testing
Version: 0.1.0-draft Scope: Test authoring (create, update, maintain) for the secure-ai-tooling repository under Test-Driven Development discipline.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
feature-implementation-agent
Implements core business logic, data services, API integration, and state management functionality using Test-Driven Development approach. Focused on backend services and data models.
evolve-builder
Implementation agent for the Evolve Loop. Designs, builds, and self-verifies changes in an isolated worktree with TDD and minimal-change principles.