Agent skill

Graph Engineering

by Mark393295827 in Mark393295827/third-brain-v7-skills

A skill your agent uses when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.

MITAuto-check passed

Install Graph Engineering

skills CLI
$ npx skills add Mark393295827/third-brain-v7-skills --skill graph-engineering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Mark393295827/third-brain-v7-skills graph-engineering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Mark393295827/third-brain-v7-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/graph-engineering .claude/skills/graph-engineering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
graph-engineering
GitHub stars
141
Token cost
~1.9k tokens
SKILL.md length
851 words
Files
5 (incl. scripts, references)
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.

  • Works in 4 steps: Identify which steps actually consume… → Estimate independent width, critical… → Keep one-shot or Loop execution when… → …
  • A workflow has explicit data dependencies
  • SKILL.md covers Usage Template, Workflow, Failure Protocol and Output Contract, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Graph Engineering is an agent skill from Mark393295827/third-brain-v7-skills. Use when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/diamond-graph-example.json` and `references/graph-contract.md`).

The repository describes itself as: agent wiki +engineering skills. The licence is MIT.

When your agent uses it

  • A workflow has explicit data dependencies
  • Independently executable branches
  • Node-local recovery needs that justify a bounded static dependency graph

Example prompts

  • “/graph-engineering”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Identify which steps actually consume another step's output.
  2. Estimate independent width, critical path, scheduler overhead, and review
  3. Keep one-shot or Loop execution when work is mainly sequential, small, or
  4. Limit V8.1 to a static DAG: sequence, pipeline, diamond, maker-checker, or

What it can do on your machine

Read from SKILL.md and the folder at commit 5a64514. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Graph Engineering loads about 1.9k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 851 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Mark393295827/third-brain-v7-skills at commit 5a64514, republished under its MIT licence (© Mark393295827). 851 words, ~1,862 tokens.

Download SKILL.mdSave it as .claude/skills/graph-engineering/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
graph-engineering
description
Use when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.
metadata.version
8.1.0
metadata.updated
2026-08-18
metadata.profile
high-risk
metadata.assumes
Nodes, payloads, writers, verifiers, budgets, and permission boundaries can be declared before execution.
metadata.conflicts_with
Dynamic or cyclic expansion, overlapping writers, whole-graph retries, hidden joins, or external effects without approval and compensation.

Graph Engineering

<skill_contract> <input>A dependency-heavy objective with candidate nodes, data schemas, owners, effects, verifiers, joins, budgets, and durable state paths.</input> <output>A validated static DAG contract with typed edges, explicit joins, node-local recovery, and graph-level receipts.</output> <done>Static invariants and terminal acceptance checks pass with fresh node, join, budget, permission, and state evidence.</done> <non_goals>Temporal loop design, worker-team command, runtime-kernel implementation, dynamic graphs, or universal parallelism.</non_goals>

Use Graph Engineering for dependency width. Use loop-engineering for repeated execution through time, agent-teams-command for process ownership and IPC, and harness-engineering for scheduler, permission, lease, and observability infrastructure. A graph node may contain a bounded Loop or Agent Team.

Usage Template

Provide: objective/non-goals, candidate nodes, real data dependencies, payload schemas, owners and write territories, join semantics, node/terminal verifiers, effects and permissions, artifact/state paths, budgets, stop conditions, and recovery. Load references/graph-contract.md for the full schema and boundary; start from references/diamond-graph-example.json.

Workflow

<intake>

Run the admission gate before drawing a graph:

  1. Identify which steps actually consume another step's output.
  2. Estimate independent width, critical path, scheduler overhead, and review load. Require measurable payback or stronger independent evaluation.
  3. Keep one-shot or Loop execution when work is mainly sequential, small, or cheaper to review serially.
  4. Limit V8.1 to a static DAG: sequence, pipeline, diamond, maker-checker, or bounded subgraph. Put repetition inside a loop node; reject graph cycles and dynamic expansion.
</intake>

<unknowns_gate>

Return NEEDS_INPUT when objective, graph owner, dependency direction, payload schema, writer, verifier, permission boundary, budget, join, or recovery is missing and cannot be discovered safely. Probe candidate independence with a small dry run. Do not invent an edge merely because two steps are adjacent.

</unknowns_gate>

<execute>
  1. Write the JSON contract and run scripts/validate_graph_contract.py <contract.json> --strict.
  2. Give every node one owner, typed inputs/outputs, explicit reads/writes, verifier, timeout, attempt/tool caps, effect class, idempotency, and compensation.
  3. Add only data, control, verification, failure, or compensation edges. Schema-bearing edges must match both endpoint contracts.
  4. Enforce one writer per target. Agent workers use isolated artifacts or worktrees; the integration owner controls shared schemas and final writes.
  5. Declare a join for every multi-input node. Choose all, reduce, first-success, quorum, barrier-verifier, or human-gate; name the exact input set and verifier.
  6. Schedule only READY nodes whose dependencies are verified. Persist every transition and edge payload reference before releasing successors.
  7. Retry the failed node or smallest invalid subgraph after a changed diagnosis. Preserve verified branches; never replay the whole graph merely for convenience.
  8. Require human approval, a compensation route, and verified rollback before any external, shared, destructive, published, credentialed, or financial effect. In strict contracts, name the external node ID in approval_required, feed it a typed approval receipt directly from a human-gate, and list each exact write target as allowed and not denied.
  9. At terminal nodes, verify the end-to-end objective and graph guardrails; node success alone cannot certify graph success.
</execute>
<evaluate>

Check static integrity: known endpoints, compatible schemas, reachability, acyclicity, single writers, complete joins, finite budgets, and compensated effects. Check runtime integrity: deterministic readiness, duplicate-delivery idempotency, checkpoint replay, permission denial without mutation, smallest-unit recovery, terminal evidence, and cleanup. Use an independent reviewer for consequential graph behavior.

</evaluate>

<retry_policy>

max_attempts comes from each node and never exceeds the graph cap. Retry only after changing diagnosis, input, owner, tool, or strategy. Stop on a repeated signature, incompatible edge, permission denial, invalid checkpoint, exhausted review budget, or NO_PROGRESS. Whole-graph retry is forbidden in strict V8.1.

</retry_policy>

<state_contract>

Persist {run_id, graph_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus contract/implementation hashes, node states, edge payload locators, join decisions, writer leases, approvals, checkpoints, compensations, terminal receipts, and cleanup. Use append-only events and an atomic current checkpoint; chat history is not graph state.

</state_contract>

Show full SKILL.md (239 more words)Show less

Failure Protocol

  • NEEDS_INPUT: a mandatory graph contract or authority field is unresolved.
  • BLOCKED_DEPENDENCY: keep affected nodes WAITING; run only independent ready nodes.
  • BLOCKED_PERMISSION: deny the effect, preserve state, and request approval.
  • VERIFY_FAILED: reject the node/join artifact and recover the smallest unit.
  • NO_PROGRESS: the same failure repeats after a changed attempt. max_attempts: 2 by default and always finite.
  • BUDGET_STOP: stop scheduling, checkpoint, compensate active effects, and return a partial graph receipt.

Output Contract

Return status, result (terminal decision and accepted artifacts), evidence (validator, node, join, terminal, budget, approval, and cleanup receipts), unknowns, and next_action (stop, retry node, compensate, approval, or handoff).

Edge Cases

  • Two workers both write report.md: strict validation fails the single-writer invariant; isolate worker artifacts and let one reduce node own the report.
  • A branch passes but its sibling times out: preserve the verified branch, retry only the failed node within cap, and do not release the join until its declared mode and verifier pass.

Success Metrics

  • The strict graph validator passes before execution.
  • Graph admission shows bounded value beyond orchestration and review cost.
  • Every node, edge, join, effect, and terminal claim has fresh evidence.
  • Recovery replays the smallest failed unit from durable state.

Quality Gates

  • Static DAG scope and Loop/Teams/Harness boundaries are explicit.
  • Owners, payload schemas, writers, joins, verifiers, and budgets are exact.
  • State replay and duplicate delivery preserve graph invariants.
  • External effects have independent review, approval, compensation, and rollback.
  • Terminal verification supports the end-to-end claim.

</skill_contract>

© Mark393295827, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/graph-engineering of Mark393295827/third-brain-v7-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/diamond-graph-example.json
  • references/graph-contract.md
  • scripts/validate_graph_contract.py

Open the folder on GitHubat commit 5a64514

Compare with similar skills

Graph Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Graph Engineering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Graph Engineering this skillMark393295827/third-brain-v7-skills141—~1.9kAutomated safety check: PassMIT
Executealirezarezvani/claude-skills28k—~831Automated safety check: PassMIT
Dependency Scanningsickn33/agentic-awesome-skills47k1 repos~2.4kAutomated safety check: PassMIT
Dependency Checkruvnet/ruflo74k—~258Automated safety check: PassMIT
Graphagenticnotetaking/arscontexta3.5k1 repos~4.9kAutomated safety check: NotesMIT
Parallel Execution Optimizeraffaan-m/ECC274k1 repos~712Automated safety check: PassMIT

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Questions about Graph Engineering

What does Graph Engineering do?

A skill your agent uses when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph. Graph Engineering is an agent skill from Mark393295827/third-brain-v7-skills. Use when a workflow has explicit data dependencies, independently executable branches, typed joins, or node-local recovery needs that justify a bounded static dependency graph.

When should I use Graph Engineering?

Graph Engineering fits situations like: A workflow has explicit data dependencies; independently executable branches; Node-local recovery needs that justify a bounded static dependency graph.

How do I install Graph Engineering in Claude Code?

Run `npx skills add Mark393295827/third-brain-v7-skills --skill graph-engineering -a claude-code`. Or copy the skill folder (skills/graph-engineering in Mark393295827/third-brain-v7-skills) into .claude/skills/graph-engineering in your project. Claude Code loads it when a task matches its description.

How do I install Graph Engineering in Codex?

Run `npx skills add Mark393295827/third-brain-v7-skills --skill graph-engineering -a codex`. Or copy the skill folder (skills/graph-engineering in Mark393295827/third-brain-v7-skills) into .agents/skills/graph-engineering in your project. Codex loads it when a task matches its description.

Can I use Graph Engineering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mark393295827/third-brain-v7-skills --skill graph-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/graph-engineering, .gemini/skills/graph-engineering, .github/skills/graph-engineering and .opencode/skills/graph-engineering in your project.

What does Graph Engineering need to run?

Going by SKILL.md and its folder, Graph Engineering needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Graph Engineering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Graph Engineering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Graph Engineering use?

Graph Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Graph Engineering use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.

What are the alternatives to Graph Engineering?

Skills that share tags, products or a category with Graph Engineering: Execute (alirezarezvani/claude-skills, 28k stars), Dependency Scanning (sickn33/agentic-awesome-skills, 47k stars), Dependency Check (ruvnet/ruflo, 74k stars) and Graph (agenticnotetaking/arscontexta, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Graph Engineering?

Mark393295827 (a GitHub user) maintains it in Mark393295827/third-brain-v7-skills, which has 141 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 19, 2026.

Source: Mark393295827/third-brain-v7-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.