specsmith 0.3.0a1.dev2

Applied Epistemic Engineering toolkit — forge epistemically-governed scaffolds, stress-test belief systems, and run AEE pipelines.

specsmith

Applied Epistemic Engineering toolkit for AI-assisted development.

Intelligence proposes. Constraints decide. The ledger remembers.

specsmith treats belief systems like code: codable, testable, and deployable. It scaffolds epistemically-governed projects, stress-tests requirements as BeliefArtifacts, runs cryptographically-sealed trace vaults, and orchestrates AI agents under formal AEE governance.

It also co-installs the standalone epistemic Python library for direct use in any project:

from epistemic import AEESession         # works in any Python 3.10+ project
from epistemic import BeliefArtifact, StressTester, CertaintyEngine

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What is Applied Epistemic Engineering?

AEE treats beliefs — requirements, hypotheses, decisions, constraints — like code:

• Codable: every claim is a BeliefArtifact with propositions and boundaries
• Testable: the StressTester applies adversarial challenges to surface failure modes
• Deployable: beliefs that survive stress-testing can be sealed with cryptographic proof

The 4-step AEE core method: Frame → Disassemble → Stress-Test → Reconstruct

The 5 foundational axioms:

1. Observability — every belief must be inspectable (hidden assumptions = stop condition)
2. Falsifiability — every belief must be challengeable (unchallenged claims = dogma)
3. Irreducibility — beliefs decompose to atomic primitives (compound claims hide Logic Knots)
4. Reconstructability — every failed belief can be reconstructed (scope may narrow)
5. Convergence — S+R iteration always converges to Equilibrium E

---

The Problem

AI coding agents produce knowledge claims (requirements, code, decisions) but have no mechanism to assess their epistemic quality. Without governance:

• Requirements are vague, compound, or untestable
• Conflicting claims (Logic Knots) silently accumulate
• Confidence in critical requirements is never measured
• Decisions lack tamper-evident audit trails

specsmith solves this by making the governance layer epistemically aware: requirements become BeliefArtifacts, audits run stress-tests, decisions seal to the trace vault.

What specsmith Does

For new projects: specsmith init generates a complete epistemically-governed scaffold with governance files, CI/CD, AEE belief registry, and agent integration files.

For existing projects: specsmith import generates governance overlay files without modifying source code. Existing files are preserved.

For AEE workflows: specsmith stress-test runs adversarial challenges against requirements. specsmith epistemic-audit runs the full AEE pipeline. specsmith trace seal creates tamper-evident decision records.

As a Python library: from epistemic import AEESession — zero specsmith coupling, works in any Python 3.10+ project (research, compliance, AI alignment, etc.).

As an agentic client: specsmith run — AEE-integrated REPL supporting Claude, GPT, Gemini, and local Ollama models, with skills, hooks, and tool loops.

Every governed project follows: propose → check → execute → verify → record.

Install

pip install specsmith             # core + epistemic library
pip install specsmith[anthropic]  # + Claude support (specsmith run)
pip install specsmith[openai]     # + GPT/O-series support

Quick Start

# New project
specsmith init

# Adopt an existing project
specsmith import --project-dir ./my-project

# Check governance health
specsmith audit --project-dir ./my-project

# Run AEE stress-test on requirements
specsmith stress-test --project-dir ./my-project

# Run full epistemic audit
specsmith epistemic-audit --project-dir ./my-project

# Start the agentic REPL (requires a provider installed)
specsmith run --project-dir ./my-project

# Run a single task non-interactively
specsmith run --task "run audit and fix issues"

Using the epistemic library in any Python project

from epistemic import AEESession

session = AEESession("my-project")
session.add_belief(
    artifact_id="HYP-001",
    propositions=["The API always returns valid JSON"],
    epistemic_boundary=["Valid auth token required"],
)
session.accept("HYP-001")
result = session.run()
print(result.summary())

See epistemic library documentation for full API reference and examples including linguistics research (glossa-lab), compliance pipelines, and AI alignment workflows.

Starting an AI Agent Session

The universal pattern for any specsmith-governed project:

/agent AGENTS.md

This works in Warp, Claude Code, Cursor, and any agent that reads markdown context files. The agent loads AGENTS.md (the governance hub), reads LEDGER.md for session state, and picks up from the last recorded action.

50+ CLI Commands

Command	Purpose
init	Scaffold a new epistemically-governed project
import	Adopt an existing project
audit	Drift detection and health checks
stress-test	AEE adversarial stress-tests on requirements
epistemic-audit	Full AEE pipeline (stress-test + certainty + recovery)
belief-graph	Render belief artifact dependency graph
trace seal/verify/log	Cryptographic decision sealing (STP-inspired)
integrate <tool>	Epistemic impact analysis before tool integration
run	Start AEE-integrated agentic REPL
agent providers/tools/skills	Configure agentic client
validate	Governance consistency checks
export	Compliance report with REQ↔TEST coverage
architect	Interactive architecture generation
credits	AI credit tracking, analysis, budgets
exec / ps / abort	Tracked process execution with timeouts
commit / push / sync	Governance-aware VCS operations
req list/trace/gaps	Requirement management
ledger add/list/stats	Change ledger management

33 Project Types

Software: Python, Rust, Go, C/C++, .NET, JS/TS, mobile, monorepo, microservices, DevOps/IaC, data/ML, browser extensions.

Hardware: FPGA/RTL, Yocto BSP, PCB design, embedded systems.

Documents: Technical specifications, user manuals, research papers, API specifications, requirements management.

Business/Legal: Business plans, patent applications, legal/compliance frameworks.

Each type gets: tool-aware CI (correct lint/test/security/build tools), domain-specific directory structure, governance rules in AGENTS.md, and pre-populated requirements and test stubs.

40+ CLI Commands

Command	Purpose
init	Scaffold a new governed project
import	Adopt an existing project (merge mode)
audit	Drift detection and health checks (--fix to auto-repair)
architect	Interactive architecture generation
validate	Governance consistency + H11 blocking-loop detection
compress	Archive old ledger entries
upgrade	Update governance to new spec version
status	CI/PR/alert status from VCS platform
diff	Compare governance against templates
export	Compliance report with REQ↔TEST coverage
doctor	Check if verification tools are installed
self-update	Update specsmith (channel-aware)
credits	AI credit tracking, analysis, budgets, and rate-limit pacing
exec / ps / abort	Tracked process execution with PID tracking and timeout
commit / push / sync	Governance-aware VCS operations
branch / pr	Strategy-aware branching and PR creation
ledger	Structured ledger add/list/stats
req	Requirements list/add/trace/gaps/orphans
session-end	End-of-session checklist

7 Agent Integrations

AGENTS.md (cross-platform standard), Warp/Oz, Claude Code, GitHub Copilot, Cursor, Gemini CLI, Windsurf, Aider.

3 VCS Platforms

GitHub Actions, GitLab CI, Bitbucket Pipelines — all with tool-aware CI generated from the verification tool registry. Dependabot/Renovate configured per language ecosystem.

Governance Rules (H1–H12)

specsmith-governed projects enforce 12 hard rules. Two were added in v0.2.3 for agentic workflows:

• H11 — Every loop or blocking wait in agent-written scripts must have a deadline, a fallback exit, and a diagnostic message on timeout. specsmith validate enforces this automatically.
• H12 — On Windows, multi-step automation goes into a .cmd file, not inline shell invocations or .ps1 files.

See Governance Model for the full rule set.

Proactive Rate Limit Pacing

specsmith ships a rolling-window scheduler that paces AI provider requests before dispatch:

• Built-in RPM/TPM profiles for OpenAI, Anthropic, and Google models (including wildcard fallbacks)
• Pre-dispatch budget check: sleeps until the 60-second window refills instead of overshooting
• Parses OpenAI-style "Please try again in 10.793s" messages and obeys them
• Adaptive concurrency: halved after a 429, gradually restored after consecutive successes
• Local overrides always take precedence over built-in defaults

specsmith credits limits defaults          # list built-in profiles
specsmith credits limits defaults --install  # merge into project config
specsmith credits limits status --provider openai --model gpt-5.4

See Rate Limit Pacing for full details.

Documentation

specsmith.readthedocs.io — Full user manual with tutorials, command reference, project type details, tool registry, governance model, rate-limit pacing, troubleshooting.

Links

• PyPI
• Documentation
• Changelog
• Contributing
• Specification
• Security

License

MIT — Copyright (c) 2026 BitConcepts, LLC.