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69score
GH · NousResearch/hermes-agent
SaaS subscription
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CI and template-aware secret policy engine

Create a policy engine for secret detection that understands placeholders, workflow variables, DSN templates, and project-specific safe patterns. It would reduce false positives in CI and code-generation environments without turning off redaction entirely.

5 channels30-day mention trend: latest 1, peak 1, 30-day series
View on Reddit
Discovered Jun 26, 2026

Why this matters

You want secret protection in generated code and CI files, but simple pattern matching treats harmless references as if they were live credentials. Workflow variables that only resolve at runtime get masked, placeholder strings are rewritten, and connection templates are misclassified as leaks. Turning redaction off is risky, but leaving it on breaks automation and fills issue trackers with edge cases. A policy engine tailored to modern templating and CI syntax would let teams declare what is safe to preserve, what must be hidden, and how detection should behave in different file types and execution stages.

  • · Built for DevOps teams, security engineers, and platform teams managing CI pipelines, config generation, and policy-based secret handling.
  • · Most likely monetization: SaaS subscription.

The Pain · Narrative

You want secret protection in generated code and CI files, but simple pattern matching treats harmless references as if they were live credentials. Workflow variables that only resolve at runtime get masked, placeholder strings are rewritten, and connection templates are misclassified as leaks. Turning redaction off is risky, but leaving it on breaks automation and fills issue trackers with edge cases. A policy engine tailored to modern templating and CI syntax would let teams declare what is safe to preserve, what must be hidden, and how detection should behave in different file types and execution stages.

Score Breakdown

Pain Intensity8/10
Willingness to Pay6/10
Ease of Build7/10
Sustainability6/10

Market Signal

30-day mention trendPeak: 1
Sparkline: latest 1, peak 1, 30-day series
Channels covered
front_pagerunatlantis/atlantiskubernetesselfhostedNousResearch/hermes-agent

Go-to-Market

Exact target user

DevOps and security engineers at software teams with heavy CI/CD templating and generated configuration files

Estimated user count

a few hundred thousand potential users inside broader engineering organizations

Primary acquisition channel

Product Hunt

Price anchor

$29/month

First milestone

50 installs and 10 paying teams using custom policies for CI and config-generation workflows

MVP Scope · 1–2 weeks

Week 1
  • Define a policy schema for placeholders, CI variables, DSN templates, and language contexts
  • Build a parser for YAML, JSON, shell, and Python files with file-type-aware policies
  • Add a simulation CLI that highlights what would be redacted and why
  • Implement safe defaults for common CI variable syntaxes and template placeholders
  • Create sample policies for popular pipeline and config scenarios
Week 2
  • Launch a hosted UI for rule management and policy sharing
  • Add API endpoints for policy validation during CI runs
  • Support project-specific exceptions with audit history
  • Generate recommendations from observed false positives
  • Run pilots with teams using generated workflows and config modules
MVP Features: Policy rules for placeholders, templated variables, and deferred secret injection syntaxes · Project-level allowlists and safe context definitions · Simulation mode showing which rules would redact or ignore content

Differentiation

Existing solutions
Built-in secret redaction in AI-agent tooling
Our angle
There is an unmet need for syntax-preserving, context-aware secret protection and verification tooling built specifically for AI-assisted coding and automation workflows.

Why This Might Fail

Self-rebuttal — the most important trust signal

  1. 1The market overlaps with established secret-scanning and policy platforms that can extend into this use case.
  2. 2Teams may prefer one-off allowlists inside existing tools rather than adopting a new policy engine.
  3. 3The false-positive problem may be most painful in a relatively narrow segment of AI-assisted and generated-code workflows.

Evidence Summary

How AI synthesized this insight — no verbatim quotes

Around five comments focus on non-secret content being mistakenly masked, including workflow references, identifier names, and string templates. The repeated mention of pattern-based false positives suggests users need configurable context rules rather than one universal regex set. This supports a policy-oriented product, especially for CI-heavy environments.

1 1 post analyzed5 5 channelsAI · AI synthesized · no verbatim

Action Plan

Validate this opportunity before writing code

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Headline

CI and template-aware secret policy engine

Sub-headline

Create a policy engine for secret detection that understands placeholders, workflow variables, DSN templates, and project-specific safe patterns. It would reduce false positives in CI and code-generation environments without turning off redaction entirely.

Who It's For

For DevOps teams, security engineers, and platform teams managing CI pipelines, config generation, and policy-based secret handling

Feature List

✓ Policy rules for placeholders, templated variables, and deferred secret injection syntaxes ✓ Project-level allowlists and safe context definitions ✓ Simulation mode showing which rules would redact or ignore content

Where to Validate

Share your landing page in r/GitHub · NousResearch/hermes-agent — that's exactly where these pain points were discovered.

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Report & PRDBUSINESS

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Frequently asked questions

Who feels this pain?
DevOps teams, security engineers, and platform teams managing CI pipelines, config generation, and policy-based secret handling
Is this a real opportunity?
This opportunity scores 69/100 on Pain Spotter's composite metric (pain intensity, willingness to pay, technical feasibility and sustainability). Validate further before committing engineering time.
How should I validate it?
Run 5 customer-discovery conversations with the target audience, post a landing page with a waitlist, and check the linked source post for recent activity before building.