ea-context
Skill3 starsChanged 3 months agoMITView on GitHub
Generate scoped EA context packs for development and coding agents working in downstream system repositories (AD-09), query the approved model, or serve it read-only over MCP. Use when asked what constraints, standards or decisions apply to a system, when preparing an AGENTS.md/CLAUDE.md governance section for a consuming repo, before a compliance assessment needs its checklist inputs, for an ad-hoc question about the model, or to give another agent or MCP host access to the architecture.
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
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
- Distinctive: line 29
Freshness on the label: Every pack opens with the review state.
- Distinctive: line 44
Use it instead of reading YAML by hand: the answer is computed from the same loaded model every report reads, so it cannot disagree with …
- Very specific: line 63
Write it to the consuming repo (e.g.
- Very specific: line 75
Do not silently violate a constraint -- if you must deviate now, that is a dispensation request (ea-dispensation), filed before the …
≈ “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
Agent context packs
easkills context --root <ea-repo> --scope app-erp-core
easkills context --root <ea-repo> --scope cap-order-management # expands to realizers
easkills context --root <ea-repo> --scope app-erp-core --out pack.mdOne command produces a scoped extract of the approved model for the agents (and
people) working on one system: the binding requirements/constraints (via appliesTo,
including those inherited through realized capabilities), standards with lifecycle
and any covering dispensations, the decisions that name the element, and the
integration context -- every relationship crossing the scope boundary, with the
neighbour's owner.
What the generator guarantees
- Approved-only, scope-filtered. Agents never read the raw EA repository, and a pack never leaks another system's content beyond the shared boundary.
- Freshness on the label. Every pack opens with the review state; stale or unreviewed content produces a warning banner telling the consumer to treat the pack as advisory. A stale model served as binding constraints carries false authority -- the banner is not optional and you must not remove it.
- Waivers with end dates. A dispensation shows up with its expiry and the explicit note that the waiver is void afterwards.
Questions no report answers: query
easkills query --root <ea-repo> --type ApplicationComponent --where lifecycle=active --show owner
easkills query --root <ea-repo> --where regulatoryScope~dora --show provider
easkills query --root <ea-repo> --from app-erp-core --hops 2 --rel Serving --json q.jsonUse it instead of reading YAML by hand: the answer is computed from the same loaded
model every report reads, so it cannot disagree with them. ~ is membership in a
space-joined value -- the only correct way to ask about regulatoryScope -- and key=
finds elements missing a property. Quote the command with the answer, so the reader can
re-run it.
The model over MCP: serve
With the plugin enabled, the approved model is already an MCP server (easkills): the
reports are tools (kpi, query, impact, context, diff_plateaus, the registers
and more) and the architecture description and context packs are resources. Point a
downstream agent at it instead of copying packs around when it can reach the server;
anywhere else, easkills serve --root <ea-repo> runs the same server for any MCP host.
It is read-only and approved-only by construction -- there is no tool that writes, and a
request to change the model through it is answered by the repository's own path:
staging, then promote.
How to deploy a pack into a consuming repository
Write it to the consuming repo (e.g. docs/ea-context.md) and reference it from
that repo's AGENTS.md/CLAUDE.md, or paste the relevant sections directly into a
governance section there. Always keep the generated header and freshness line.
Refresh whenever the EA repository's approved zone changes -- a pack is a snapshot,
and its "reviewed up to" line says which one.
The feedback path (this is the point)
The pack's footer tells the consuming agent: if reality no longer matches, report the drift back, do not work around it. When you are the agent in the consuming repository and you find the pack wrong or stale:
- Do not silently violate a constraint -- if you must deviate now, that is a
dispensation request (
ea-dispensation), filed before the deviation ships. - Report factual drift (a system renamed, an integration added, a standard not
actually followed) as input to
ea-delta-ingest-- a note or export dropped into the EA repo'sfacts/sources/is enough to start the loop.
Reporting back
Say which scope, which model state ("reviewed up to" date), where the pack was written, and anything the pack revealed as missing -- an element with no binding requirements and no standards is usually an un-modelled area, not a free-for-all.
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-delta-ingestSkill
- ea-dispensationSkill
- ea-docsSkill
- ea-evalSkill
- ea-healthSkill
- ea-importSkill
- ea-intakeSkill
- ea-modelSkill
- ea-regulatorySkill
- ea-runSkill
- 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.
No reviews yet
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.

