pegrex-specs
Generate AI-consumable spec schemas (multi-view analysis with prioritized, machine-checkable tasks).
Skill by ovoxnox
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: productivity, agent-building
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/pegrex-specs
What's inside
- Skill name
pegrex-specs- Description
- Generate AI-consumable spec schemas (multi-view analysis with prioritized, machine-checkable tasks). Use when starting a refactor, onboarding to a codebase, or preparing agent task lists.
- License
- MIT
- Instructions
- 23 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# pegrex spec generation
## Command
python pegrex.py spec <source.py> <name> [--out specs]
python pegrex.py spec <dir> <name> --dir # recursive batch
## What you get
`<out>/<name>.spec.json`:
- `tasks[]` — prioritized (P0–P4), each with `acceptance_criteria` and
MACHINE-CHECKABLE `verify` commands — these are your TDD loop steps
- `analysis.finding_counts` — per-analyzer, per-severity histograms
- `analysis.arch_index` — containers/modules/owns for navigation
- `analysis.opus` — per-perspective summaries (security/refactor/testing/
documentation/architecture)
- `contract_check` — manifest self-validation (should be [])
## Conventions
- Tasks are sorted by priority; execute P0/P1 first.
- `verify` commands are the loop exit criteria: run them after each task.
- Specs are deterministic (sorted keys) — diff two runs to see drift.
## Contract-derived repository planning
- `RepositoryMemory` persists stable API, invariant, dependency, architecture,
and limitation summaries with content-defined identities.
- `SemanticDependencyGraph` maps modules, functions, contracts, and invariants;
use `dependencies()` and `impact()` for forward and reverse traversal.
- `plan_changes` ranks candidates by benefit, reachable impact, uncertainty, and
proof effort. Invalid or missing inputs remain unranked UNKNOWN rows; a plan
never substitutes for verification or proof-gated application.
- `compile_agent_context` compresses repository memory without silently dropping
UNKNOWN markers. LLM `propose_spec` output remains a conjecture until an
independent checker supplies proof or a confirmed counterexample; otherwise it
is UNKNOWN. A2A tasks must carry budget, deadline, evidence, cancellation, and
partial-result state.
## Formal protocol evidence
- `parse_protocol` strictly handles JSON, URL, CSV, INI, S-expressions, and
properties/configuration text. Duplicate keys, malformed escapes, inconsistent
rows, and trailing input remain UNKNOWN rather than permissively repaired.
- A PROVED protocol result covers only documented well-formedness and the supported
declarative schema subset; it is not an application-security or semantic proof.
## Semantic PR budgets
- `summarize_semantic_change` separately accounts for public contracts, security
surface, dependencies, complexity, and UNKNOWN regions. Set explicit nonnegative
limits and retain every exceeded metric.
- A complete change summary is not merge approval. Missing parser/flow/complexity
evidence stays an UNKNOWN region rather than a zero-change entry.From ovoxnox/pegrex, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 24, 2026 (8 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/ovoxnox/pegrex
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
pegrex-specs skill. Agent Mart. https://agentmart.dev/skills/ovoxnox-pegrex-specs (last updated 2026-10-24)