gasserane/personal-skills

implementation-pack

Generate a post-award implementation / project-management pack (CERV first) from a proposal baseline: applies the awarded grant-agreement figures, then emits a six-tab branded ImplementationTracker.xlsx, a periodic technical-report scaffold, and a README.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/gasserane/personal-skills --skill implementation-pack

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

/implementation-pack — post-award PM pack generator

You generate a standalone, IPPF-branded implementation pack a full project manager runs with no AI dependency. You are the front-half: load baseline, apply awarded figures, build. Vi executes any optional reporting spawns.

Arguments

  • --donor <name> (default cerv). Maps to ane_package/proposals/donor_profiles/<name>.json.
  • Optional baseline path (a proposal handback.json or a proposal pack folder).

Step 1 — Load the donor profile

python
from ane_package.proposals.config import load_profile
profile = load_profile("cerv")  # or the --donor value

State the implementation config back to Ane: reporting periods, RAG thresholds.

Step 2 — Load the baseline (never invent)

python
from ane_package.proposals.baseline_io import load_baseline
baseline = load_baseline(handback_path_or_dict)

If no baseline is available, ask Ane for the proposal handback or a baseline brief. Do not invent any WP, indicator, figure, or partner (factual-reliability rule).

Step 3 — Apply the awarded figures

Ask Ane for the signed grant-agreement budget, dates, and revised targets.

python
from ane_package.proposals.baseline_io import apply_awarded
baseline = apply_awarded(baseline, awarded)   # raises BaselineError on unknown WP/indicator or incoherence

If the project is not yet awarded, skip this step; the pack flags "baseline = submitted" in the Guide and README.

Step 4 — Build the pack

python
from ane_package.proposals.implementation_pack import build_implementation_pack
manifest = build_implementation_pack(profile, baseline, out_dir)

Apply mel_wiki/wiki/concepts/edit-preservation-protocol.md when target file exists — if the output folder already holds an edited pack, treat Ane's content as canonical and edit scope-bounded, do not regenerate from scratch.

Step 5 — Optional reporting cycle (only when Ane asks for a populated report)

Delegate to Vi to spawn mel-report-writer (draft the narrative from tracker status) and contribution-plausibility-analyst (judge whether indicator progress supports the outcome claims). Neither is needed for the pack to be usable.

Step 6 — Disclosure and scope boundary

  • The Word report carries the AI-disclosure colophon per mel_wiki/wiki/concepts/ai-use-in-publications.md. AI is never an author.
  • State the scope boundary: the finance system of record, the official portal/grant-management reporting, and the authorising officer's sign-off are owned by finance, the portal, and the authorising officer — not by this skill.

Output

Return the pack folder path and the manifest. Surface whether the baseline is awarded or submitted, and any [PM: insert X] the PM must still complete.

관련 작업

같은 저장소의 Skills

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Apply substantive quality and reliability grading to Ane''s MEL/SRHR system (Ann/Vi/Li/Researcher team + wiki + harness + three-repo architecture): letter grades on 9 components and 10 dimensions plus OECD-DAC adapted criteria, three concrete risks, what an external evaluator would still want, and recommendations for moving the grade, with a mandatory self-grading bias note. Use when Ane types /grade-system, /grade, /quality-grade, /rate-system, or asks how good the system is. Honest grading, no

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GitHub Stars
2
업데이트
9월 24일