Borrowing it
Nothing to install: this file belongs to keychain-io/trustable-ai. 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/keychain-io/trustable-ai/main/.claude/commands/feature-implementation.mdgit clone --depth 1 https://github.com/keychain-io/trustable-aiWrote 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/commands/keychain-io/trustable-ai/feature-implementation)<a href="https://agentmods.dev/commands/keychain-io/trustable-ai/feature-implementation"><img src="https://agentmods.dev/badge/commands/keychain-io/trustable-ai/feature-implementation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/keychain-io/trustable-ai/feature-implementation"><img src="https://agentmods.dev/badge/commands/keychain-io/trustable-ai/feature-implementation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.03587 |
| Opus 5.5 | $0.00000 | $0.01435 |
| Sonnet 5.5 | $0.00000 | $0.00717 |
| Haiku 4.5 | $0.00000 | $0.00359 |
Grade A, and why
feature-implementation 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 2mo 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 — 546 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Implementation Workflow (Adversarial Verification)
Project: trusted-ai-development-workbench Workflow: Feature Implementation with Adversarial Verification Purpose: Implement features with procedural safeguards against AI-generated bugs
Output Formatting Requirements
IMPORTANT: Use actual Unicode emojis in reports, NOT GitHub-style shortcodes.
The Problem This Workflow Solves
AI-generated code often contains subtle bugs. AI-generated tests often miss those same bugs because they share the same blind spots. This workflow uses adversarial multi-agent verification with fresh context windows for each specialized agent.
┌─────────────────────────────────────────────────────────────────────────┐
│ This Workflow (Adversarial Verification with Agent Slash Commands) │
│ │
│ 1. /senior-engineer → Creates API contract │
│ 2. /senior-engineer → Implements feature │
│ 3. /tester → Tests from SPEC ONLY (no code!) │
│ 4. /tester → Tries to break code │
│ 5. /tester → Verifies tests can fail │
│ 6. /tester → Resolves code/test/spec conflicts │
│ │
│ Each agent command spawns a FRESH CONTEXT WINDOW via Task tool │
└─────────────────────────────────────────────────────────────────────────┘
Prerequisites
- Work item ID with clear acceptance criteria
- Specification file at
docs/specifications/(from sprint-planning) - Project codebase with existing test infrastructure
Initialize Workflow
First, collect the work item information:
# Initialize work tracking adapter
import sys
sys.path.insert(0, ".claude/skills")
from work_tracking import get_adapter
adapter = get_adapter()
print(f"📋 Work Tracking: {adapter.platform}")
work_item_id = input("Enter work item ID (e.g., 1001): ")
sprint_number = input("Enter sprint number: ")
# Load work item from adapter
try:
work_item = adapter.get_work_item(int(work_item_id))
print(f"✅ Loaded work item #{work_item_id}: {work_item.get('title', 'Untitled')}")
# Extract fields
work_item_title = work_item.get('title', '')
work_item_description = work_item.get('description', '')
acceptance_criteria = work_item.get('acceptance_criteria', [])
except Exception as e:
print(f"❌ Failed to load work item #{work_item_id}: {e}")
print(" Make sure the work item exists in azure-devops")
exit(1)
# Load specification file (if it exists, otherwise use work item description)
from pathlib import Path
spec_file = Path(f"docs/specifications/sprint-{sprint_number}/{work_item_id}-spec.md")
if spec_file.exists():
specification = spec_file.read_text()
print(f"✅ Loaded specification from {spec_file}")
else:
print(f"⚠️ Specification file not found: {spec_file}")
print(" Using work item description as specification")
specification = work_item_description
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.
- 2mo ago First seen · 546 lines · 0 tokens per session scan A e8ccb9b67380
feature-implementation is a command published in the GitHub repository keychain-io/trustable-ai (2 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,587 tokens. 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 commands, from other repositories
prototype
You are building a proof-of-concept for the current Grainulator sprint. Read CLAUDE.md for sprint context and claims.json for existing research claims.
qa-changes
This skill should be used when the user asks to "QA a pull request", "test PR changes", "verify a PR works", "functionally test changes", or when an automated workflow triggers QA validation of code changes. Provides a structured methodology for setting up the environment, exercising changed behavior, and reporting…
verify
Run repository verification using the verification-loop skill.
modernize-transform
Transform one legacy module to the target stack — idiomatic rewrite with behavior-equivalence tests.
constraints
A command for defining a project's quality standards through a short interview and a written CONSTRAINTS.md file. It records checks, thresholds, exceptions, and commands for enforcing them.
check-dev
Type-check a Z specification with fuzz.