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 commands/glassbead-tc/widescreen-research/feature-implementation-gamegit clone --depth 1 https://github.com/glassBead-tc/widescreen-researchWrote 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/glassbead-tc/widescreen-research/feature-implementation-game)<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/feature-implementation-game"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/feature-implementation-game.svg" alt="Measured on agentmods" 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 | $0.03716 |
| Opus 5 | $0.00000 | $0.01858 |
| Sonnet 5 | $0.00000 | $0.00743 |
| Haiku 4.5 | $0.00000 | $0.00372 |
Grade C, and why
feature-implementation-game scanned grade C with 1 finding 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 6d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
# rm -rf .feature-implementation-game How it starts
The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/feature-implementation-game
Ship new product capabilities with discipline. An OR-inspired shipping protocol that minimizes risk, preserves developer velocity, and bakes in observability, docs, and rollout strategy from the start.
Usage
/feature-implementation-game $ARGUMENTS [ship_deadline] [risk_level] [rollout_strategy]
Arguments
$ARGUMENTS(required): High-information feature specification. Paste or reference the spec directly; see Variables belowship_deadline(optional): ISO 8601 deadline (default: 1 week from now)risk_level(optional): "low" | "medium" | "high" (default: "medium")rollout_strategy(optional): "flagged" | "canary" | "dark_launch" | "big_bang" (default: "flagged")comments(optional): Additional context/constraints
Variables
- SPEC_DOCUMENT: $ARGUMENTS
- Accepted forms: inline Markdown PRD/RFC, GitHub/GitLab issue URL, file path, YAML/JSON brief
- Recommended sections for maximum signal:
- Problem statement, goals, and non-goals
- Acceptance criteria and success metrics
- API/SDK contracts (types, errors, pagination, retries)
- UX notes or wireframes
- Non-functional requirements (performance, security, availability)
- Rollout and rollback constraints
- Telemetry requirements (metrics, events, traces)
- Docs & examples scope
Algorithm
Phase 0: Initialize Shipping Space
# Create feature game state
mkdir -p .feature-implementation-game/{artifacts,checks,rollouts,docs}
cat > .feature-implementation-game/state.json << EOF
{
"feature": "$FEATURE_NAME",
"deadline": "$SHIP_DEADLINE",
"risk_level": "$RISK_LEVEL",
"rollout_strategy": "$ROLLOUT_STRATEGY",
"phase": 0,
"budgets": {
"error_budget": 0.02,
"performance_budget_ms": 50,
"oncall_load_budget_pts": 3
},
"slo": {
"availability": 0.999,
"p95_latency_ms": 250
},
"flags": {
"name": "feature.${FEATURE_NAME// /_}",
"default": false,
"kill_switch": true
},
"stakeholders": ["product_owner", "tech_lead", "sre", "qa", "docs", "security"],
"artifacts": {}
}
EOF
echo "🎮 Feature Implementation Game initialized for: $FEATURE_NAME"
echo "⏰ Deadline: $SHIP_DEADLINE | ⚠️ Risk: $RISK_LEVEL | 🚀 Rollout: $ROLLOUT_STRATEGY"
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.
- 6d ago First seen · 441 lines · 0 tokens per session scan C 0258aaeb50a9
feature-implementation-game is a command published in the GitHub repository glassBead-tc/widescreen-research (6 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,716 tokens. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.