oma-academic-writing
Draft and revise academic prose against a rubric, evidence, and
Create or revise OMA skills and their references. Use for skill
$ npx -y skills add first-fluke/oh-my-agent --skill oma-skill-creation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/oma-skill-creationContext preview
The summary Claude sees to decide when to auto-load this skill.
Create or revise OMA skills and their references. Use for skill
name: oma-skill-creation description: Create or revise OMA skills and their references. Use for skill routing, execution contracts, conditional loading, and authoring validation.
Create or revise a usable OMA skill with clear routing, one execution path, domain-specific recovery, and proportionate context.
Skill purpose, triggers, boundaries, execution model, and existing commands/resources or user edits to preserve.
A four-section SKILL.md, only necessary supporting resources, and validation results with unresolved limitations.
`resources/ssl-lite-template.md` for the skeleton and `resources/validation-checklist.md` for acceptance criteria. Use native file tools and available `oma skill` validators. Follow repository rules for source ownership and generated copies.
| Failure | Recovery | |---|---| | Overbroad routing | Narrow triggers and add a concrete When NOT to use cross-route | | Vague execution | Add actual objects, paths, command flags, and evidence to the canonical path | | Repeated procedure in multiple sections | Keep the canonical procedure; retain only distinct branches or state transitions elsewhere | | Required tool unavailable | Use the documented fallback; otherwise report which result cannot be verified | | Conflicting source and generated copies | Use the repository's generation flow; do not hand-edit protected definitions |
A completed skill has valid structure, useful routing, executable steps, recoverable failures, valid references, and applicable validation evidence. Missing tools or unresolved assumptions must remain explicit.
1. Read the target, its invoked resources, and relevant repository rules. Identify which entry points run independently and which content the runtime already injects. 2. Select command-heavy, judgment-heavy, or reference-heavy behavior. Use the template's minimal skeleton and optional sections only where they add information. 3. Write the canonical procedure once. Preserve exact command/output contracts, failure mechanisms, side effects, and harmful-action limits; remove generic restatements and redundant examples. 4. Index resources once under References with load conditions. Keep source-adjacent evidence one hop from SKILL.md where possible. 5. Run `oma skill lint --skill <name>` and `git diff --check`. If routing descriptions changed, run `oma skill audit`. Apply the content checks in `resources/validation-checklist.md` that automated lint does not cover. 6. Report the changes, checks, and remaining limits. Do not claim reduced token usage merely from removing duplicate files; measure the context actually loaded when making that claim.
Skill work reads and may change local definitions/resources and generated vendor copies through the authorized repository flow. User data and credentials are not skill examples. Commits and publishing require the corresponding authorization.
1. Keep YAML `name` and a routing-grade `description`. Preserve the four top-level headings: Scheduling, Structural Flow, Logical Operations, References. 2. Keep one canonical command/workflow path, an Intent signature, When to use/When NOT to use, input/output expectations, failure recovery, and relevant effects/guardrails. Section titles beyond these are optional when their information is already represented. 3. Entry, Scenes, Actions, and tool/scope tables must add distinct information. Do not repeat a procedure to fill the template. 4. Keep SKILL.md under 500 body lines. Load supporting resources conditionally; do not create README/changelog/install documents inside a skill merely to explain the skill. 5. Keep parsed output/schema examples. Do not prescribe decorative report layouts or add generic self-review loops; use runnable validators and explicit evidence requirements. 6. Process bulk data through deterministic tools and return summaries/artifact paths instead of streaming raw data into context. 7. Preserve unrelated user edits. Apply the shared execution policy to clarification, authorization, and verification.
Agents narrate success. oh-my-agent checks the artifacts. Spawning parallel agents is the easy part. The hard part is knowing whether they actually did the work.
Repo: first-fluke/oh-my-agent
Draft and revise academic prose against a rubric, evidence, and
Evaluate system boundaries and architectural tradeoffs. Use for
Implement server APIs, authentication, and application data access.
Coordinate assigned specialist tasks and handoffs manually. Use