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 skills add strikersam/autonomous-ai-agency --skill feature-flaggit clone --depth 1 https://github.com/strikersam/autonomous-ai-agencyWrote 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/strikersam/autonomous-ai-agency/feature-flag)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/feature-flag"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/feature-flag/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/skills/strikersam/autonomous-ai-agency/feature-flag"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/feature-flag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.00709 |
| Opus 5.5 | $0.00014 | $0.00284 |
| Sonnet 5.5 | $0.00007 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
feature-flag 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 7d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: feature-flag
Purpose
Implement new features behind feature flags to enable safe, gradual rollouts and easy rollbacks without code deployments.
Trigger
Use when:
- Implementing a risky or large feature
- A feature needs gradual rollout
- A/B testing is desired
- The feature might need to be disabled quickly in production
Process
Step 1: Assess Flag Need
Determine if a feature flag is appropriate:
- Always flag: breaking changes, major UI changes, experimental features
- Consider flagging: new integrations, significant logic changes
- Skip flagging: bug fixes, minor improvements, refactors
Step 2: Define the Flag
# Flag definition
FLAG_NAME = "feature_[descriptive_name]"
FLAG_DEFAULT = False # default to OFF for new features
FLAG_DESCRIPTION = "[what this flag controls]"
FLAG_OWNER = "[team or person responsible]"
FLAG_CREATED = "[date]"
FLAG_PLANNED_REMOVAL = "[date or milestone when flag should be cleaned up]"
Step 3: Implement the Guard
# Python example
from agent.config import get_feature_flag
def some_feature():
if not get_feature_flag("feature_new_thing"):
return existing_behavior()
return new_behavior()
// TypeScript example
import { isFeatureEnabled } from './config/flags';
function someFeature() {
if (!isFeatureEnabled('feature_new_thing')) {
return existingBehavior();
}
return newBehavior();
}
Step 4: Test Both Paths
Write tests for BOTH flag states:
def test_feature_disabled(monkeypatch):
monkeypatch.setenv("FEATURE_NEW_THING", "false")
assert some_feature() == expected_old_behavior
def test_feature_enabled(monkeypatch):
monkeypatch.setenv("FEATURE_NEW_THING", "true")
assert some_feature() == expected_new_behavior
Step 5: Document the Flag
Add to docs/feature-flags.md:
| Flag | Default | Description | Owner | Remove By |
|------|---------|-------------|-------|-----------|
| feature_new_thing | false | [description] | [owner] | [milestone] |
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.
- 7d ago Changed · +4 lines · +35 tokens per session 9fb78c6c968f
- 1mo ago First seen · 94 lines · 0 tokens per session scan A 7af8072e58fd
feature-flag is a skill published in the GitHub repository strikersam/autonomous-ai-agency (11 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 709 once invoked, about $0.0001 per session on Opus 5.5. 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.
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