omp-flow-execute

A workflow for implementing approved work Concepts, which are structured descriptions of planned changes, with a separate review of the result.

In plain words
What is it for?
Use it to coordinate coding tasks, delegate implementation, run checks, and pass completed work to an independent reviewer.
Why use it?
It keeps implementation tied to approved scope and prerequisites, records the work, and requires verification and a linked handoff before the operation is finished.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/andyduck-ops/omp-flow/omp-flow-execute
Any agent
npx skills add Andyduck-ops/omp-flow --skill omp-flow-execute
Clone the repo
git clone --depth 1 https://github.com/Andyduck-ops/omp-flow

Made for: Claude Code, Codex.

Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,029 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00025 $0.01029
Opus 5 $0.00013 $0.00515
Sonnet 5 $0.00005 $0.00206
Haiku 4.5 $0.00003 $0.00103

Measured 2d ago against content hash 1853b48bbea0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

omp-flow-execute scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/omp-flow-execute/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OMP-Flow Execute

Implementation Loop

  1. Read work/index.md or the Bundle links that communicate the accepted work grouping.
  2. Select work whose prerequisites are semantically satisfied. Parallelize only an authored group whose code/output scopes do not conflict.
  3. Start each operation with explicit task, work entry, implementer role, actor-id, bounded objective, handoff/code output, and any relevant predecessor.
  4. Dispatch the native executor/omp-flow-implement role with the complete assignment string returned by that operation start, exactly unchanged. Keep its strict v1 ompFlowDispatch JSON as the first non-blank line; do not parse, reserialize, prepend, append, infer, or drop fields. Set native item id to the returned operation's actor_id/descriptor actorId, and keep the item role equal to descriptor role.
  5. Require the Implementer to inspect existing patterns, stay in scope, run work verification, and write or update the promised linked handoff Concept.
  6. Finish the operation with the same actor ID only after the native result and promised output exist. A failure remains a visible failed operation and does not manufacture semantic status.

Independent Review Loop

  1. Start a fresh review operation with the same work Concept as entry, an independent reviewer actor ID, a descriptive Review Concept output, and the completed implementation receipt as predecessor.
  2. Forward that review operation's returned assignment unchanged with native item id = actor_id = actorId and matching reviewer role. Its descriptor carries the predecessor receipt and predecessor output; do not reconstruct or remove either.
  3. Runtime correlation must reject a reviewer who is the implementation actor or whose predecessor is not completed.
  4. Reviewer follows the work Concept to its handoff, inspects the real changed code and design, runs independent checks, and writes the promised Review Concept.
  5. Finish with the same reviewer actor ID. Read the review's findings and verdict as knowledge; Python does not parse or duplicate them into Evidence or row status.
  6. Classify every finding with all applicable labels:
    • HARD_BLOCKER violates a non-negotiable safety, authority, identity, integrity, assignment/receipt/source-binding, or irreversible-effect boundary;
    • PRINCIPAL_BLOCKER concretely prevents the next authored principal product checkpoint;
    • WORK_FAIL violates the bounded Work's own acceptance criteria or a test that exercises them; it may overlap either stronger label;
    • ADVISORY is any other recorded quality, robustness, completeness, or elegance risk. A failed prerequisite Work required by the next principal checkpoint is also PRINCIPAL_BLOCKER. HARD_BLOCKER and PRINCIPAL_BLOCKER pause the principal path and return to the owning work/design Concept for explicit routing. WORK_FAIL makes that Work FAIL and enters a recorded owning-Work backlog; absent a hard/principal label it must not autonomously create repair/review work, reopen accepted Work, or preempt the principal checkpoint. ADVISORY remains recorded residual risk and has none of those effects.
  7. A hard/principal failure requires explicit human/design routing. When the principal checkpoint produces continue, narrow, or stop, present every recorded WORK_FAIL backlog to the human/design decision for explicit repair, deferral, removal, narrowing, or stop; none of those routes is autonomous. Material design changes repeat the applicable QbD decision.

Read the full file on GitHub · 77 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 77 lines · 25 tokens per session scan A 1853b48bbea0

Subscribe to this mod's changes

omp-flow-execute is a skill published in the GitHub repository Andyduck-ops/omp-flow (5 stars, last pushed 7d ago), licensed MIT. It adds 25 tokens to every session and 1,029 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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