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@860fce8
by Boboshu2/agentops446 stars
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Create, adapt, consolidate or repair skill packages and projections. Use when: authoring guidance, descriptions or structure; Skill Eval measures behavioral benefit.

Use this Skill: https://skilld.dev/gh/boshu2/agentops/skill-builder

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referencesauthoring-doctrine.md

≈1k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Skill Authoring Doctrine

Use this reference when a sentence, description or reference split creates a concrete authoring uncertainty. The source template and audit checks own structural requirements. Layout is a choice; applicability, necessary inputs, authority/effects, result, completion and failure must be clear in whichever form fits the operation.

Idea provenance: clean-room consideration of authoring ideas at https://github.com/mattpocock/skills (MIT), reconciled with this repository's accepted contract. No upstream prose, names, prompts, scripts or examples are copied. Wording theories below are hypotheses, not established improvements.

Make the instruction change a decision

Prefer an observable action over an intensifier: when a selected operation requires a boundary reference, read that reference before the dependent action. Do not require every reference on every invocation. If necessary material is missing, name it and stop the dependent action; unrelated authorized work can continue. Re-read when contents changed, context was lost or the next decision requires it, rather than once per invented phase.

A phrase's benefit depends on the task, model and host. Preserve a necessary obligation even when a detector dislikes its wording. A comparison with the unchanged task and a plausible wrong outcome is evidence; author preference or word count is not.

State authority and a usable failure path

Use direct positive instructions where they are clearer. Keep explicit prohibitions when they define an authority, disclosure or mutation boundary, and name a safe alternative or incomplete response when useful. The theory that negation primes forbidden behavior needs task-specific evidence; it is not a reason to remove a necessary ban or to require paired wording everywhere.

Completion must cover the promised result. Add intermediate checkpoints only when final completion cannot protect a consequential action or handoff. A reference supplying judgment criteria need not invent workflow phases, and a concise adapter can express success and failure in one paragraph.

Use concrete language before compressed cues

Familiar domain terms can save repetition when their meaning is shared. A leading word's effect on behavior remains a hypothesis; words are not free context and a vague cue must not replace an input, stop condition or authority boundary. Define unfamiliar terms only when the operation needs them. Test actual outcomes before claiming a wording change improves execution.

Separate description, invocation policy and content

A description says what the skill does and when it applies. For implicit selection, test distinctive task states, neighboring jobs and false activation; for explicit selection, describe scope without synonym padding. Neither a tier nor user-invocable proves what a particular host discovers or loads.

Use the existing source/host invocation fields, including Codex agents/openai.yaml policy where needed; see Codex parity. Explicit-only policy does not prove zero catalog context cost or prohibit composition. Verify loaded bytes and policy on each claimed host. Canonical source and generated portable packages have separate profiles.

Measure actual descriptions, bodies, references, repeated reads and tool output on the task path. A shorter root can cost more overall. Split only when a conditional operation or independently useful route makes navigation clearer; optional folders and assets earn no quality credit.

Interpret audit advice as evidence to inspect

The default v2 audit reports located authoring suspicions separately from conformance and unmeasured behavioral evidence. Historical noop-phrase, negation-without-positive and step-missing-done-condition detectors remain available through audit.sh --legacy for compatibility. Their tokens, headings and phrase counts neither prove quality nor mandate prose repairs. Keep legacy consumer compatibility separate from the decision to retain or revise a skill.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub3d

    The skill-builder is a robust developer tool for managing AI agent skill packages. It incorporates strong security practices, including clean-room verification to prevent indirect prompt injection, containment guards to prevent accidental file deletion, and safe parsing of YAML metadata.

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  • Snyk3d

    Risk: LOW · No issues

Signed by skilld at 860fce8. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

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