code-factory-2-forge 0.10.5

ForgeLine — the autonomous outer loop for AI software factories. A CLI-backed state machine that drives intent -> spec -> plan -> code -> adversarial review -> ship, orchestrating SpecLine (spec governance) and Harness Software Factory (compiled decisions), with self-improving skills and architecture-as-a-CI-gate.

ForgeLine 🔨

The autonomous outer loop for AI software factories. ForgeLine is the tier above spec-writing and code-generation: a CLI-backed state machine that carries a feature from vague intent to shipped code through confidence gates, runs a generate → adversarial review → refine loop, enforces architecture as a hard CI gate, and gets smarter every run via an evolving skill memory.

It orchestrates its siblings — SpecLine (spec governance) and Harness Software Factory (compiled decisions) — into one pipeline you drive from Claude Code or Codex.

Stop writing the code. Build — and supervise — the machine that writes it.

Workflow at a glance

flowchart LR
    A["Feature intent"] --> B["Expand use cases"]
    B --> C["Human confidence gate"]
    C --> D["Architect with SSAT"]
    D --> E["Scaffold from architecture"]
    E --> F["Fill implementation"]
    F --> G["Adversarial review"]
    G -->|"findings"| H["Refine and record lesson"]
    H --> F
    G -->|"pass"| I["Architecture CI gate"]
    I --> J["Reverse-classical test verification"]
    J --> K["Runtime smoke gate"]
    K --> L["Ship with receipt"]

INTENT ─► EXPAND ─► ARCHITECT(SSAT) ─► SCAFFOLD ─► FILL ─┐
   ▲                                                      │
   │        ┌── grumpy adversary + judge + arch-erosion ──┤
   └── refine┤          (records a skill lesson)           │ pass
             └──────────────◄─────────────────────────────┘
                                     │
                        ARCH-CI-GATE ─► SHIP

Install (any OS, 60 seconds, no API keys)

One command, works everywhere:

python install.py          # Windows / macOS / Linux

or double-click install.sh (macOS/Linux) / install.bat (Windows), or:

pip install -e ".[dev]"

Then:

forge demo                 # 60-sec: watch it catch its own bad output, learn, ship
forge init
forge agent claude         # wire your agent (see full list below)
forge status <feature>     # the state machine names the ONE next action
forge optimize-pr <feature> # bounded PR hardening loop plan
pytest -q                  # full ForgeLine suite

PR hardening loop

forge optimize-pr <feature> is the senior-engineer review loop in executable form. It reads the current diff, flags paths that require approval, recommends the QA/lesson/factory evidence commands, and fixes the stop conditions up front: ready, blocked, approval required, exhausted after five iterations, or stagnated after no improvement.

ForgeLine still does not merge, publish, deploy, or send external messages by itself. The loop makes one reversible change at a time, verifies with receipts, and hands the final proof bundle to Factoryline for factory pr-pack.

Works with every major coding agent

One command wires the entry-point skill wherever your agent reads it:

forge agent claude      # CLAUDE.md + .claude/skills/forge.md
forge agent codex       # AGENTS.md
forge agent opencode    # AGENTS.md + .opencode/forge.md
forge agent cursor      # .cursorrules + .cursor/rules/forge.md
forge agent aider       # CONVENTIONS.md
forge agent gemini      # GEMINI.md
forge agent windsurf    # .windsurfrules
forge agent generic     # AGENT.md  (unknown tools fall back to AGENTS.md)

The contract is plain text; any harness that reads a project file can run the factory.

What makes it more than a wrapper

A real state machine with confidence gates. Eight states, legal-transition enforcement (E_ILLEGAL_TRANSITION), and two human confidence gates (use-case signoff, architecture signoff) where a person must approve before agents proceed. Every transition is a hash-sealed receipt on disk — so the loop survives context resets (the disk is the truth).

SSAT — architecture as code, not a document. A Semantic Software Architecture Tree (YAML) declares modules, signatures, allowed dependency edges, and invariants. The same artifact generates the scaffold AND serves as the CI gate: check_erosion() detects signature drift (E_SIG_DRIFT), illegal dependencies (E_ILLEGAL_DEP), and invariant violations (E_INVARIANT) — structural erosion caught before merge.

Scaffolding is fail-closed. forge architect will not overwrite an existing module. Start with a plan:

forge architect <feature> <ssat> --dry-run --root .

The JSON report separates created, skipped, conflicts, and overwritten files and includes before/after SHA-256 values. Existing targets are reported as conflicts and leave both the files and feature state unchanged. To bring an already-implemented feature under the contract without modifying its code, use --adopt-existing: ForgeLine validates declared signatures, dependencies, and invariants; records the unchanged target hashes as adopted; and scaffolds only modules that are absent. A mismatch returns E_SCAFFOLD without changing files or feature state. An intentional replacement requires --force; ForgeLine first writes timestamped backups under .forge/scaffold-backups/, validates every generated file, and restores every modified target if any replacement fails. Python, JavaScript (including ESM .mjs), TypeScript, and TSX scaffolds use extension-specific generators and structure checks; unsupported extensions are rejected rather than receiving Python syntax.

Feature-scoped, language-aware QA. forge qa <feature> --ssat <contract> only grades files declared by that contract. Dependency, build, cache, VCS, and reparse-point trees are pruned. Python is parsed with Python; JavaScript/ESM is syntax-checked by Node and inventories exported and local functions; TypeScript uses its matching parser. An unavailable parser is reported as PARSER_UNSUPPORTED, never disguised as a syntax error. A slice without discoverable functions is explicitly inventory-only, not behavioral-proof green.

Fresh proof inputs. forge verify-tests hashes its SSAT, declared source, smoke manifests, tests, command, and tool version. Any mismatch invalidates the prior reverse-classical receipt and reruns the proof. Inspect the active binary with forge version --json; it reports version, origin, build hash, runtime, and whether source identity is complete.

The grumpy adversary. A review agent that assumes your code is broken and insecure and makes the generator prove otherwise. Executable heuristics catch eval/exec/shell-injection/hard-coded-secrets/bare-excepts, and it refuses to pass any code that ships without tests (A_NO_PROOF — "prove it works"). No LLM required to be useful; an LLM adversary layers behind the same interface.

A self-improving skill flywheel. Every gate failure records a structured lesson to skills/lessons.jsonl. Lessons are injected into the next attempt's context, and lessons seen ≥3 times graduate into hard constraints (conventions-into-constraints) — promoted straight into SSAT invariants. The factory literally learns your team's rules from its own mistakes.

Decision handoff to the factory. Specs carrying a decision table route to HSF for one-time compilation into gated, deterministic code — the outer loop knows the difference between tissue (agents write it) and decisions (the factory compiles them, never improvised twice).

The entry-point skill (Claude Code / Codex)

forge agent claude writes CLAUDE.md + .claude/skills/forge.md; forge agent codex writes AGENTS.md. The contract turns any agent into a disciplined factory worker: read the skill, run forge status, do the one named phase, run its gate, repeat — context resetting between phases. The agent never free-codes; it advances a state machine. That's the whole point.

Commands

forge init                          scaffold the factory
forge adopt <feature> --root .      inspect an existing repo and write a reviewable baseline
forge agent claude|codex            wire the entry-point skill
forge status <feature>              current state + the ONE next action
forge expand <feature>              draft use cases (→ human gate)
forge gate architected <feature>    record explicit architecture approval
forge architect <feature> <ssat> --dry-run  show create/conflict/overwrite plan without writes
forge architect <feature> <ssat>            generate new files; existing files fail closed
forge architect <feature> <ssat> --adopt-existing  validate and receipt implemented files; scaffold only missing targets
forge architect <feature> <ssat> --force    back up then intentionally replace existing targets
forge fill <feature> <ssat>         prove bodies are implemented; enter FILLED
forge review <feature> <ssat>       judge + grumpy adversary + arch erosion (refine loop)
forge arch-gate <feature> <ssat>    architecture CI gate
forge verify-tests <feature> <ssat>  prove smoke checks fail on generated stubs
forge verify-tests-ts <feature>      prove existing TypeScript tests fail on reviewed source mutants
forge challenge <feature> <ssat>     write a Factory Passport challenge receipt
forge qa <feature> --ssat <ssat> --strict  run scoped language-aware QA
forge version --json                 print install and build provenance
forge smoke <feature>                runtime behavior gate
forge ship <feature>                seal it
forge handoff <feature> <spec>      route decision tables to HSF
forge lessons                       show the skill memory + promotable constraints
forge demo                          the 60-second story

v0.3 — the Intent Thread (PRD → production traceability)

ForgeLine now consumes SpecLine's sealed Intent Envelope and verifies the FINAL shipped code against the ORIGINAL rationalized intent — not the plan, not the drifted spec, but the intent that was sealed at the plan gate. This closes the last translation-loss gap: does the shipped thing actually satisfy what we rationalized we wanted?

Every assumption SpecLine surfaced (auth exists, currency is single-source, dependencies can fail) becomes a checkable obligation. The ship gate blocks if the code shows no handling for an assumption the intent depended on, and the sealed intent hash proves the intent wasn't quietly swapped underneath the build. Full PRD-to-production traceability, enforced — not documented.

v0.2 — deeper QA + recursive learning

ForgeLine now audits quality quantitatively and learns from its own runs:

Deep QA audit (forge qa <feature> --ssat <feature.ssat.yaml>) grades the declared feature slice on coverage-intent (do tests actually call the functions?), cyclomatic complexity, a scored security surface (eval/exec/shell/secrets/weak-crypto), and documentation — a composite A–F grade that gates shipping. A pretty build with untested, over-complex, or insecure code cannot pass.

Recursive learning kernel (forge policy, forge demo-learning) closes the loop the skill memory only started:

• observe — a failure is recorded (as before)
• promote — a failure seen ≥3× becomes an enforced active constraint
• validate — when that constraint catches the same failure again, it's marked effective; the policy tracks prevention counts
• self-prune — a promoted rule that never fires again goes to probation, so the policy doesn't ossify

The factory's own run history becomes its QA policy, and that policy is measured, not assumed. forge demo-learning shows the full observe→promote→validate→prune cycle in 15 seconds.

Escalating refine loop — the review loop now gets stricter each attempt (normal → elevated "fix all findings" → final "human review required") instead of a flat retry cap.

v0.10 - Safe feature QA and provenance

ForgeLine now treats an SSAT as the ownership boundary for a gate. forge review uses only its declared source modules for adversarial and QA findings; unrelated repository observations do not decide a feature result. Run the same scoped check directly with:

forge qa my-feature --ssat specs/my-feature.ssat.yaml --root . --strict

forge qa --repo-wide --root . remains available for an explicit inventory, but is labeled repo_wide in its receipt and is not a substitute for reviewed feature evidence. Dependency, build, cache, virtual-environment, and generated receipt trees are pruned before either scan. Disappearing optional paths are reported as skipped instead of crashing the gate.

Source parsing is extension-aware: Python uses its AST; JavaScript and TypeScript use Node/TypeScript when available. An unavailable parser is reported as parser_unsupported, never misreported as Python syntax. Complexity above the hard threshold (10) cannot receive a passing grade.

Regex invariants must declare a reviewed bounded scope: SSAT module, symbol, and max_lines. An unscoped pattern blocks as E_INVARIANT_SCOPE; a match in the bounded symbol blocks as E_INVARIANT. This prevents a broad regex from accidentally judging unrelated code.

Every installed CLI can identify the running package without guessing from a checkout:

forge --version --json
forge version --json

The JSON includes package/version, install origin, Python version, and explicit null values when a source commit or build hash was not embedded in the wheel.

The five-brick factory

Repo	Tier	Owns
FactoryLine	proof baseplate	protocol compatibility, traces, passports, challenge aggregation
ForgeLine	outer loop	intent→ship state machine, adversarial gates, skill flywheel, arch-as-CI
SpecLine	spec governance	EARS specs, atomic task packets, token-lean context, intent-drift guard
HSF	decision compiler	ordered business rules → gated deterministic code, zero tokens/decision
Prestige	design proof	Visual DNA, source audit, rendered desktop/mobile evidence

Same doctrine at every tier: gate everything, receipts or it didn't happen, compile what shouldn't be reasoned twice.

License

Dual-licensed under either Apache-2.0 OR MIT at your option — the permissive standard for broad adoption. Pick whichever your project prefers.

---

v0.4 — Runtime Smoke Gate (behavior-by-inspection)

ForgeLine's earlier gates all verify code against specifications — the judge checks consistency, the QA audit grades static quality, the intent thread proves the shipped code honors the sealed envelope. That is correctness by construction.

None of it answers the question a per-PR preview deployment answers: does the built thing actually RUN and behave correctly? A change can pass every static gate and still crash on import or produce the wrong output. v0.4 closes that gap at solo-builder scale — no ephemeral per-PR environments required.

New state + gate

The SDLC gains a SMOKED state between ARCH_GATED and SHIPPED:

… → reviewed → arch_gated → smoked → shipped

forge smoke <feature> runs every behavioral check declared in smoke/<feature>.json in an isolated subprocess with a timeout, and blocks ship on any runtime failure. ship now refuses to run until the smoke gate has passed — you cannot ship unverified runtime behavior.

The smoke manifest

smoke/<feature>.json makes "correct runtime behavior" a reviewed artifact:

{
  "checks": [
    {
      "name": "formatter_runtime",
      "kind": "python",
      "run": "from slices.notifier.formatter import format_message\nassert format_message({'kind':'ping','text':'hi'}) == 'ping: hi'\nprint('OK')",
      "expect_exit": 0,
      "expect_stdout": "OK",
      "timeout_s": 15
    }
  ]
}

kind is python (runs a snippet) or command (runs a shell command). Each check asserts an exit code and, optionally, a stdout substring.

Fail-closed by design

• No manifest → BLOCK. You cannot ship runtime behavior you never verified.
• Empty manifest → BLOCK. A manifest with zero checks ships nothing verified.
• Any check fails / times out / crashes → BLOCK, with the failing check named and the last lines of its output captured as a receipt.

Why this and not full preview deployments

Per-PR ephemeral environments are real DevOps infrastructure (provisioning, isolation, teardown, secrets, cost) that pays off at team scale with many parallel PRs and human reviewers. The smoke gate captures the core value — verify the artifact runs and behaves before merge — deterministically, at the cost of a subprocess. When team scale justifies it, the manifest model extends naturally to spinning real environments; the behavioral contract is already written.

v0.6 - Reverse-Classical test verification

ForgeLine now refuses tests that prove nothing.

forge verify-tests <feature> <ssat.yaml> regenerates the SSAT scaffold into an isolated temp root and runs every behavioral smoke check against those empty stubs. Each behavioral check must fail on the stub before ForgeLine trusts it against the real implementation. A check that passes against an empty stub is classified as HOLLOW_TEST and blocks the feature.

The ordering is now:

... -> reviewed -> arch_gated -> tests_verified -> smoked -> shipped

Structural checks such as imports may declare "must_fail_on_stub": false in smoke/<feature>.json, but the field defaults to true. Omission never buys leniency, and a manifest where every check is exempt blocks as HOLLOW_MANIFEST.

This gate is deterministic: no model, no git snapshot, no new runtime service. It reuses the same SSAT scaffold generator as the normal SCAFFOLDED state, so the mutant is the real generated stub.

Failure attribution and refinement

ForgeLine 0.5 reports review, architecture, QA, smoke, and intent failures at their smallest actionable unit. forge qa <feature> --ssat <feature.ssat.yaml> includes function-level metrics and attribution in its JSON output.

The refinement engine accepts exactly one proposed edit at a time. Structural edits precede configuration and parameter changes. An edit is retained only when its targeted stage improves and no other stage regresses; rejected edits are reverted and written with before/after rates to .forge/rejection_ledger.jsonl. Two consecutive non-wins stop the loop.