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/arbazkhan971/godmode/featurenpx skills add arbazkhan971/godmode --skill featuregit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote 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/skills/arbazkhan971/godmode/feature)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/feature"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/feature.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 | $0.00055 | $0.01728 |
| Opus 5 | $0.00028 | $0.00864 |
| Sonnet 5 | $0.00011 | $0.00346 |
| Haiku 4.5 | $0.00006 | $0.00173 |
Grade A, and why
feature 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 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.
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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature — Feature Flags & Progressive Delivery
Activate When
- User invokes
/godmode:feature - User says "feature flag", "gradual rollout"
- User says "kill switch", "A/B test"
- Deploying needs safer rollout or deploy/release decoupling
Workflow
Step 1: Flag Strategy Assessment
# Detect existing flag infrastructure
grep -r "launchdarkly\|unleash\|flagsmith\|split\|statsig" \
package.json pyproject.toml 2>/dev/null
# Find existing flag usage
grep -rn "featureFlag\|isEnabled\|isFeatureEnabled" \
src/ --include="*.ts" --include="*.py" 2>/dev/null | wc -l
# Check for stale flags (not evaluated in 30+ days)
grep -rn "featureFlag\|isEnabled" src/ \
--include="*.ts" 2>/dev/null | head -20
FLAG STRATEGY:
Current: None | Homegrown | LaunchDarkly | Unleash
Flag needs: Release | Experiment | Ops | Permission
Environments: Dev | Staging | Prod | Mobile
IF < 5 flags: Homegrown or Flagsmith OSS
IF 5-50 flags: Unleash or Flagsmith
IF 50+ flags with experiments: LaunchDarkly
IF strict data residency: self-hosted
Step 2: Flag Types
| Type | Lifecycle | Default | Duration |
|------------|-------------|---------|-----------|
| Release | Short-lived | OFF | < 2 weeks |
| Experiment | Short-lived | CONTROL | < 4 weeks |
| Ops/Kill | Permanent | ON | Forever |
| Permission | Permanent | OFF | Forever |
RELEASE RAMP:
CREATE → INTERNAL → CANARY 1% → 5% → 25%
→ 50% → 100% → CLEANUP (within 2 weeks)
EXPERIMENT RAMP:
CREATE → CONFIGURE → RUN → SIGNIFICANCE
→ PICK WINNER → CLEANUP (within 1 week)
Step 3: Targeting & Gradual Rollout
RULE PRIORITY (top to bottom):
1. Individual overrides
2. Employee targeting
3. Beta segment
4. Percentage rollout
5. Default OFF
ROLLOUT STAGES:
Internal (0.1%) 1d → Canary (1%) 1-2d
→ Early (5%) 2-3d → Expanding (25%) 3-5d
→ Majority (50%) 3-5d → Full (100%)
GATE CRITERIA (advance only if all pass):
Error rate < baseline + 0.1%
P95 latency < baseline + 10%
Conversion > baseline - 2%
Support tickets < baseline + 5%
STICKY BUCKETING:
hash(flagKey:userId) % 10000 / 100
Same user always sees same variant
Increasing % adds users, never flips existing
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 · 221 lines · 55 tokens per session scan A b55e788ab78a
feature is a skill published in the GitHub repository arbazkhan971/godmode (26 stars, last pushed 8d ago), licensed MIT. It adds 55 tokens to every session and 1,728 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 2026-08-30.
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