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please split prs by features. |
This was referenced Sep 21, 2026
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Thanks — split by feature as requested. This combined PR is superseded by:
Each PR cites the GPT-6 Astra skills-and-prompts article and carries its own validation. All PRs except #73 target |
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Local edits, status questions, and already-scoped research tasks could trigger full workflows, repeated approval menus, and unrelated context loading. This change routes skills by the requested outcome while retaining shared interaction defaults: a grammar-only review stays local, an autoresearch status request stays read-only, and an authorized survey/report continues through its requested deliverable. New exploration and manuscript planning retain guidance; broad paper polish defaults to a marked proposal before application.
The design follows OpenAI’s Rethinking skills and prompts for GPT-6 Astra, especially precise decision boundaries, progressive disclosure, and explicit completion criteria. The README also records this reference.
Changes
Validation
python3 scripts/validate_skills.py: all 16 skills pass; skill-creatorquick_validate.pyalso passes for all 16.python3 -m pytest -q: 261 passed, 1 skipped.npm pack --dry-run --json: all new skill references included.git diff --check: clean.write-paper; that route now delegates toreview-paperand was rechecked.claude-opus-4-7. Paired single-response checks against baseline905ea3eexercised advisor discovery, a specified derivation, paper polish, accepted findings, and guided versus preapproved drafting. A toy training-paper fixture was not counted as a valid new-research checkpoint test; a completed computational-study fixture retained venue/story discussion in the revised version.main.proposed.md, leftmain.mdbyte-for-byte unchanged, and delivered numbered changes for acceptance; no shell diff or render was claimed. Content quality of the suggestions was not scored as a pass.Limits
These are instruction and reference changes; executable research helpers and public skill names are unchanged. The forward checks are bounded examples, not broad model benchmarks or proof of Claude-wide non-regression. Claude ran with customizations disabled and synthetic data; the first-response checks had no tools, and the file checks had only Read/Write/Edit. They do not exercise normal plugin discovery, full literature acquisition, compilers, or a complete manuscript lifecycle. The samples also contain content errors: one revised derivation omitted a minus sign in an optional derivative, and both preapproved teaching drafts overgeneralized the noncontraction case by overlooking the zero initial condition. These are recorded as failures of generated content, not passed scientific validation or evidence that the skill change caused a regression. Live literature acquisition, publisher checks, and slide creation were not exercised. No external sharing or submission is authorized by the revised workflows alone.