Agent-Harness-Kit: Skill for Claude Code

.agents/skills/graph-execution/SKILL.md

graph-execution is a skill for Claude Code, Codex from Eduardo-Salvador/Agent-Harness-Kit. It costs 24 tokens per session (980 once invoked), scanned A, original, MIT.

A workflow for breaking approved technical work into dependent tasks and assigning ready tasks for execution. A task graph maps which pieces of work depend on others.

In plain words
What is it for?
Use it to plan and execute multi-step coding work, including test-driven bug fixes, where tasks need exclusive ownership, verification, and follow-up review.
Why use it?
It keeps specifications, dependencies, progress, reviews, and completion evidence recorded and prevents specialists from improvising when requirements are unclear.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; installed under .agents/ (shared by several agents); mentions Codex.

This is Eduardo-Salvador/Agent-Harness-Kit's own configuration. It tells Claude Code and Codex how to work on Agent-Harness-Kit itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Agent-Harness-Kit configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Eduardo-Salvador/Agent-Harness-Kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Eduardo-Salvador/Agent-Harness-Kit/main/.agents/skills/graph-execution/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Eduardo-Salvador/Agent-Harness-Kit

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for graph-execution

README.md
[![agentmods](https://agentmods.dev/badge/skills/eduardo-salvador/agent-harness-kit/graph-execution.svg)](https://agentmods.dev/skills/eduardo-salvador/agent-harness-kit/graph-execution)
Your own site
<a href="https://agentmods.dev/skills/eduardo-salvador/agent-harness-kit/graph-execution"><img src="https://agentmods.dev/badge/skills/eduardo-salvador/agent-harness-kit/graph-execution.svg" alt="Measured on agentmods" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 980 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00024 $0.00980
Opus 5 $0.00012 $0.00490
Sonnet 5 $0.00005 $0.00196
Haiku 4.5 $0.00002 $0.00098

Measured 3d ago against content hash 171f91807dd8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

graph-execution 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 3d 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/graph-execution/SKILL.md · 15 lines

How it starts

The opening of the file, as written. The whole thing — 15 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Graph execution

Apply ../../../docs/ADAPTIVE-EXECUTION.md: lane and assurance are orthogonal; resume probes real state before artifacts; same-context nodes use inline spec/transition; packets exist only for actual separate consumers; planned units target 15–30 active minutes; tests climb the five-rung ladder; and in-scope technical recovery needs no fresh approval.

After verification, for assurance: light|full only, automatically launch the distinct reviewer in a fresh context—prefer a proven subagent—and send only the pinned SPEC-led packet, never prompt or conversation memory. Same-context review is invalid.

Every technical event is persisted in a new TASK-GRAPH.md revision before it is reported: dispatch/start, material progress, dependency changes, block/unblock, remediation, completion, lease/context changes, and newly ready nodes. Never record technical movement only in PENDING.md; revise pending state only for a related human action or macro outcome and backlink the new graph revision. Before graph creation or dispatch, follow writing-plans: non-simple work requires a ready implementation plan and every node requires a self-contained executable task spec. A specialist executes that spec; contradictions, missing decisions/dependencies/paths, unevaluable acceptance, or materially oversized work return needs-replan rather than improvisation. Code behavior and bug-fix tasks also load test-driven-task; completion requires meaningful RED before production code, GREEN with the same focused test, and proportional regression evidence. When automatic model routing is human-approved, do not stop at model_tier: resolve the current Codex model/effort, pass both into the actual task/message/subagent dispatch, and pin adapter confirmation through harness.model-dispatch/v1 before activation. Same-context self-switch claims and silent host defaults are invalid.

For a first-call status or resume, follow ../../../harness/playbooks/status-resume.md before any broad scan. PENDING.md owns human actions and macro incomplete areas; TASK-GRAPH.md owns technical order, dependencies, and execution. For user-pending questions, report human-owned items first and group human/technical pending items by workstream. Otherwise read only the relevant neutral playbook: discovery-to-graph.md, task-dispatch.md, context-routing.md, model-routing.md, or parallel-execution.md. Load the pinned context revision, pending-work authority, local graph neighborhood, task brief, scoped rules, approved capabilities, approved model-routing revision, and linked execution budget. Prefer the node's read_set over a broad scan, lease only its write_set, and use its impact_set for proportional regression checks. Verify derived relationships in source and record context_provenance; an approved, fresh Graphify index may enrich these fields but never becomes a second operational graph. Keep different workstreams in distinct execution contexts except a bounded integration node; create visible threads or internal subagents only when current capability evidence permits it. Respect dependency readiness, exclusive write sets, isolation evidence, routing escalation triggers, and orchestrator-only graph mutation. Enforce ../../../docs/EXECUTION-BUDGET.md: reconcile the goal-lineage counters before another cycle or context expansion; never reset them by changing model, agent, task, review, decomposition, or session; at a ceiling persist evidence and stop-and-replan. Enforce ../../../docs/REVIEW-ROUNDS.md for every dispatched reviewer: one initial review, at most one focused remediation review, and no third loop. Follow ../../../docs/STATUS-AND-COMPLETION.md and ../../../docs/contracts/STATUS.md: explicit status views and milestone closeouts label current stage, progress, continuing-without-user-action work, human and macro pending items from PENDING.md, active/ready/blocked nodes and technical pending work from TASK-GRAPH.md, blockers, next action, and inspectable paths. Routine progress updates may be concise result/evidence, human action, and next action; do not reread artifacts or create a full status form without a state/status need. Mark passing work completed, report it, release ownership, dispatch only nodes whose product and technical gates pass, and run declared assurance review automatically as non-blocking work. Follow ../../../docs/ACCOMPANIED-DELIVERY.md: demonstrate client milestones and genuinely await approval before affected expansion. Every new spec has explicit completion conditions, mirrored in graph acceptance_criteria; current criterion-level, required TDD, and affected-flow evidence must pass before technical completion. Write durable results through shared templates; do not store state inside .agents/.

Read the full file on GitHub · 15 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. 3d ago Changed · +2 lines 171f91807dd8
  2. 8d ago First seen · 13 lines · 24 tokens per session scan A f0501a88714e

Subscribe to this mod's changes

graph-execution is a skill published in the GitHub repository Eduardo-Salvador/Agent-Harness-Kit (6 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 980 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.

Related

Other skills, from other repositories

qa

Run scalable, isolated live QA for nac development. The top-level local orchestrator must parse n (default 4), dispatch one setup worker with this skill, copy its n assignment contracts verbatim into exactly n parallel test workers with this skill, then dispatch one aggregate worker with this skill using all test…

arcee-ai/nac · 100 tokens

release

Cut and publish a full stable NAC release after main, release-PR, and publication CI pass. Use when a maintainer asks for a stable version bump, tag, or GitHub Release. Never use for release candidates; NAC RC releases are automated.

arcee-ai/nac · 53 tokens

openrig-user

Use when a specific rig command, subcommand, or flag is already known and you need its exact syntax, JSON shape, defaults, or error meaning. NOT for natural capability discovery, open-ended how-do-I questions, or choosing which OpenRig move applies.

mvschwarz/openrig · 58 tokens

orienting-to-an-inherited-seat

Use when you have just been primed into an EXISTING seat through a planned handover — a different agent retired and handed you the seat plus its earned context — and you need a world model of what just happened to you. Covers how a handover differs from compaction and from a fresh launch, the…

mvschwarz/openrig · 207 tokens

triage

Triage a GitHub repository's open issues by finding exact duplicates, rejecting evidenceably off-base requests, requesting concrete clarification, applying only existing labels, and opening a linked root-cause issue when multiple reports share one underlying invariant failure. Use when a maintainer asks to triage…

arcee-ai/nac · 65 tokens

forming-an-openrig-mental-model

Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do not know how skills reach you…

mvschwarz/openrig · 117 tokens