engineer-agentic-ai
Skill0 starsChanged 6 months agoMITView on GitHub
Translate natural-language requests about AI-agent behavior, skills, MCP tools/servers, prompts, routing, context loading, memory, hooks, plugins, permissions, delegation, portability, or other agentic-system behavior into the concrete runtime mechanism and change surface. Use when a request is phrased as what the AI should understand/do rather than how the target harness actually observes, triggers, routes, persists, or enforces that behavior.
In this repository13 other files it gives its agents.
- change-consistencySkill
- format-markdownSkill
- information-representation-designSkill
- living-artifactsSkill
- messaging-frameworkSkill
- python-testingSkill
- relevance-first-information-designSkill
- reviewable-branch-diffsSkill
- review-ready-codeSkill
- securitySkill
- software-designSkill
- swe-methodSkill
- thoughts-to-artifactSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 27
Use this as the single end-to-end process:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 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
🛠️ Engineer Agentic AI
Translate desired agent behavior into mechanisms the target runtime can observe, trigger, execute, persist, and verify.
🗂️ Contents
- 🎯 Purpose and applicability
- 🧭 Engineering framework
- 📚 Reference routing
- ✅ Quality gates
<a id="purpose"></a>
🎯 Purpose and applicability
Use this skill when a request describes what an agent should understand, decide, remember, trigger, prevent, or accomplish, but the concrete runtime mechanism is missing or uncertain. It applies to instructions, skills, routing, tools, MCP servers, hooks, permissions, plugins, automations, persistence, delegation, and related agent-system behavior.
The outcome is an implemented and behaviorally verified mechanism—or an explicitly retained problem-space requirement when the runtime chain cannot yet be closed.
<a id="framework"></a>
🧭 Engineering framework
Use this as the single end-to-end process:
- Preserve the problem-space intent. State the observable outcome without prematurely turning it into prompt wording or a provider feature.
- Route the investigation. Load only the narrowest references required by the current uncertainty using Reference routing.
- Operationalize the behavior. Connect observable signal and evidence → trigger/lifecycle event → decision rule → actuator → persistence → verification. When the target runtime actually involves cooperating agents or surfaces, map each participant's available context, instructions, authority, and actions; design the required handoffs and truthful result attribution. Do not turn one application's workflow steps into universal agent behavior. Include reliability and miss-cost requirements. If a required link is unavailable, keep the outcome in the problem space rather than calling it implemented.
- Choose the mechanism and surface. Select the lightest available mechanism that can meet the required reliability; verify current provider capabilities when the choice depends on them.
- Implement canonically. Change the authoritative instruction, skill, hook, tool, permission, plugin, script, automation, or configuration surface and validate its propagation path.
- Validate real behavior. Exercise the mechanism through a representative agent interaction or runtime event. Test the observable outcome, not only syntax, file presence, or plausible prose.
- Diagnose and iterate. On failure, trace guidance freshness, trigger match, visible evidence, rule specification, routing/selection, actuation, permissions, persistence, and propagation; adjust the mechanism or test and run it again. Use positive, negative, boundary, and repeated cases when behavior is model-mediated or routing-dependent.
- Promote and hand off. Promote only after the required behavioral evidence exists. Refresh deployed/generated copies, record remaining unknowns and tested scope, and retire or mark any superseded wishlist item.
The detailed operationalization, validation, debugging, and promotion methods live in the Agent engineering process; do not recreate parallel procedures in this file.
<a id="reference-routing"></a>
📚 Reference routing
Load a reference only when its condition matches the current decision or failure.
| When | Read | Use it for |
| Translating, validating, debugging, or promoting an agent behavior | Agent engineering process | Operationalization, mechanism selection, behavioral testing, failure diagnosis, and wishlist promotion. |
| Establishing how context, tools, hooks, state, sessions, compaction, or delegation behave | Agent runtime model | Runtime mechanics and Codex-specific lifecycle evidence. |
| Diagnosing selection, metadata, interface boundaries, or missing enforcement | Interface, routing, and control design | Pre-selection routing versus post-selection execution and control strength. |
| Reasoning about interaction surfaces, cooperation among distinct agents, permissions/approvals, interruptions, background work, or result delivery | Interaction runtime model | Map the actual participating actors, their capabilities and handoff contracts, action attribution, lifecycle, and return paths; keep example workflows non-normative. |
| Designing a reusable artifact across identities, machines, operating systems, or providers | Portability and provider mapping | Concept-first portability and provider-neutral wording. |
| Needing an exact provider filename, directory, manifest, or settings location | Provider path reference | Current provider-specific path mappings and sources. |
| Analyzing context size, tokens, caching, compaction cost, allowances, credits, or billing | Token usage and cost model | Usage evidence, cache mechanics, product accounting, and optimization. |
<a id="quality-gates"></a>
✅ Quality gates
A change is ready only when:
- the
operationalization chainis complete, or the missing link is explicitly retained as an unresolved requirement; - the chosen mechanism and routed references support the required reliability without competing canonical rules;
- each instruction targets an agent with the required observations and actuator, or explicitly tells the recipient to request another agent's action; user-visible claims truthfully attribute the actor and distinguish requests, attempts, and verified outcomes;
- concrete task sequences (such as editor navigation or user checkpoints) are required only when the specific outcome calls for them, not inherited from unrelated examples;
behavior validationproves the intended outcome on the stated surface and scope;- the canonical source, propagation path, and deployed/generated state are verified; and
- the handoff names remaining unknowns and completes the applicable
wishlist-to-production lifecycle.
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.
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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.

