forgekit v0.12.3 · MIT · zero runtime dependencies

Give every coding agent the same working memory.

Forgekit turns one source of truth into native configuration for AI coding tools, then adds proof-carrying lessons, blast-radius prediction, reuse search, and pre-action guardrails so agents act from shared evidence instead of fresh guesses.

Read the docs See the workflow

npm install -g @codewithjuber/forgekit && forge init

118 msfull pre-action gate from repository benchmarks

0.43 msblast-radius lookup from benchmark reports

62.1%measured routing-stage cost saved

0runtime dependencies in package.json

Platform

A cognitive substrate, packaged like developer tooling.

The site uses only repository-backed claims: README, CHANGELOG, package metadata, benchmark reports, and optional GitHub API counters on the status page.

Cross-tool config

Author rules once, then compile native config layers for the agents your team already uses.

Proof-carrying memory

Every durable lesson includes evidence, provenance, confidence, decay, and merge behavior.

Impact foresight

Before editing, Forge predicts affected files and tests so agents can plan smaller, safer changes.

Reuse-first retrieval

Agents are pushed toward existing code paths and prior fixes before inventing another solution.

Guardrails that run

Scope, assumptions, anchoring, cost, and secret-redaction checks are enforced by CLI hooks.

Team memory through git

Knowledge moves with the repository, not with a single chat transcript or vendor workspace.

Workflow

Install once. Gate every meaningful agent action.

The CLI stays small and dependency-free, while optional live status data is fetched with timeouts, retries, jitter, and HTTP cache validators.

Initialize the substrate

Run the installer and generate project-local agent instructions, hooks, and memory stores.

Let agents plan with evidence

Forge assembles context, reuse candidates, assumptions, scope, and impact predictions before edits.

Promote only verified lessons

Tests and human corrections update memory; self-graded claims do not become trusted facts.

forge substrate

$ forge substrate "Change auth validation and update tests"
✓ loaded team rules from AGENTS.md
✓ found reuse candidates before implementation
✓ predicted blast radius and test targets
! assumption requires citation before action
✓ emits native config for agent tools

Evidence

Professional, but honest about limits.

The strongest design choice is restraint: measured numbers are labeled as measured, targets stay targets, and generated pages document their sources.

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Approximate impact atlas

Import-graph predictions are useful for gating and planning, not presented as perfect dependency analysis.

Cost claims stay scoped

The page shows the measured routing-stage savings rather than blending measured data with aspirational targets.

Data sources

No mock marketing data.

Every metric and product claim on this site comes from local repository files or the optional public GitHub repository endpoint used by the generated status page.

package.json
README.md
CHANGELOG.md
reports/benchmarks.md
https://api.github.com/repos/CodeWithJuber/forgekit