ea-run
Skill3 starsChanged 3 months agoMITView on GitHub
Orchestrate the EA pipeline - route a request to the right ea-* skill and keep the stage order honest. Use when a request spans multiple stages ("model our architecture from these documents"), when unsure which ea-* skill applies, or when resuming work in an EA repository whose state you have not inspected yet.
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 14
Every command in these skills is easkills <command>.
- Very specific: line 33
When a services/ catalog exists, route catalog-first: an incoming ask that matches an offering becomes a recorded request (ea-service) and …
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Routing the pipeline
The stages depend on each other; the most common failure is starting downstream of missing input. Establish the repository's state first, then route.
Every command in these skills is easkills <command>. With the ea-skills plugin
enabled, easkills is on the Bash tool's PATH; from a clone of ea-skills,
python -m easkills is the same command with the same arguments and exit codes. If
neither resolves, the tooling is not installed -- say so; do not reason your way to a
verdict the tooling exists to compute.
State check (one minute, always)
easkills validate-facts --root . # is there a verified fact register?
easkills validate --root . --zone approved
easkills validate --root . --zone staging
easkills validate-gov --root . # governance records healthy?
easkills delta --root . # unmodelled entities / unused facts
easkills align --root . # is there a reference yardstick, and where does it stand?Routing table
When a services/ catalog exists, route catalog-first: an incoming ask that
matches an offering becomes a recorded request (ea-service) and is fulfilled
through that offering's path -- the ask defines the scope, and the scope caps the
work (on-demand discipline: model the slice the request needs, nothing more).
Everything else routes by situation:
| Situation | Skill |
| Someone asks EA for something an offering covers | ea-service (record it, then fulfil via the offering) |
| New raw documents; empty or stale fact register | ea-intake |
| Facts exist, no capability map | ea-capability-map (the spine comes first) |
| Facts + capability map; elements/relations to add | ea-model |
| Staging has content; human asks to publish | ea-approve -- the human invokes it; it never loads on your initiative |
| Who is this for / concerns unclear / ISO findings | ea-stakeholders |
| Views needed or stale | ea-views; then ea-docs for the description |
| New info against an existing model | ea-delta-ingest |
| A change request arrives | ea-change-triage first, then its route |
| Standards, waivers, decisions, assessments | ea-standards-base / ea-dispensation / ea-adr / ea-compliance |
| "Is this done / complete?" · gap analysis against a reference model or industry blueprint | ea-align (there is no answer without a yardstick) |
| Regulation, control frameworks, ICT third-party risk, "the DORA register" | ea-regulatory — control gaps are align against a kind: control pack; the register is dora-register, and it generates rather than attests |
| Security architecture, threat modelling, "what could an attacker reach", data classification, zero trust, SBOMs | ea-security -- exposure walks the reach from public elements, propose --from threats writes the assessments, control coverage is align against nist-sp-800-53-r5 |
| AI systems, "the AI Act", risk classification, the AI inventory, AI risk acceptance | ea-ai-governance — same mechanism one register over: gaps are align against nist-ai-rmf-1.0, the inventory is ai-act-register, and accepted AI risk is a dispensation with an expiry |
| "Make a backlog out of these findings" · gaps into requirements or work packages | propose --from align|readiness|overlap|time|coverage|threats writes the skeletons; the words, owner and review date are the human half (ea-align, ea-adr) |
| Downstream repo needs its constraints · an ad-hoc question about the model · another agent or MCP host needs the architecture | ea-context -- context packs, query, and serve (read-only MCP) |
| "What does the target state change?" · "what changed since the last board?" · "are we getting better?" | diff --plateau, diff --ref, trend via ea-board / ea-health |
| Diagrams for a wiki, a pull request or slides · colour a view by lifecycle or owner | ea-views -- export --format mermaid|plantuml, render --heatmap |
| Periodic health / board preparation | ea-health / ea-board |
| "How mature is our architecture practice?" | maturity via ea-health — five dimensions, never one number; the blockers are the answer |
| "Do two systems do the same thing?" · application rationalization | ea-health reads the debt overlap items; the verdict goes to ea-change-triage (undecided overlap classifies up) or ea-board, and deliberate redundancy ends in ea-adr |
| Skill behaviour changed | ea-eval |
Order for a from-scratch engagement
ea-intake → ea-stakeholders → ea-capability-map → ea-model (layer by layer,
staging) → ea-validate → human review → ea-approve → ea-views → ea-docs →
governance records as they arise. Do not compress stages: modelling without a fact
register produces unevidenced concepts the validator will reject; views before
stakeholders produce diagrams nobody asked for.
Rules that hold across every route
- Deterministic gates decide, skills propose. If a gate blocks, fix inputs -- never bypass flags in anything you present as done.
- Three-repair cap everywhere; after that, escalate the open question.
approved/changes only viapromote, on explicit human instruction.- Report at the end of any multi-skill run: which stages ran, gate verdicts, what is staged awaiting review, and the open questions -- one consolidated summary, not one per skill.
In this repository25 other files it gives its agents.
- ea-adrSkill
- ea-ai-governanceSkill
- ea-alignSkill
- ea-approveSkill
- ea-boardSkill
- ea-capability-mapSkill
- ea-change-triageSkill
- ea-checkSkill
- ea-complianceSkill
- ea-contextSkill
- ea-delta-ingestSkill
- ea-dispensationSkill
- ea-docsSkill
- ea-evalSkill
- ea-healthSkill
- ea-importSkill
- ea-intakeSkill
- ea-modelSkill
- ea-regulatorySkill
- ea-securitySkill
- ea-serviceSkill
- ea-stakeholdersSkill
- ea-standards-baseSkill
- ea-validateSkill
- ea-viewsSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

