How do I install the Implementation Pack skill?

Run npx skillmds@latest add gasserane/implementation-pack in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Implementation Pack safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Implementation Pack?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Implementation Pack free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Implementation Pack?

gasserane (@gasserane) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Implementation Pack by gasserane — Agent Skill

Name: Implementation Pack
Rating: 4.5 (4 reviews)
Author: gasserane

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 Implementation_Tracker.xlsx, a periodic technical-report scaffold, and a README. Use when Ane sets up project tracking after an award or runs '/implementation-pack --donor cerv'; donor is an argument so Gates/OSF/UN are later --donor values. Does not replace the finance system of record, portal reporting, or the authorising officer's sign-off. Distinct from /proposal (pre-award).

gasserane Updated Sep 9, 2026

File contents

/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

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)

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.

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

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.

---name: implementation-packdescription: 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 Implementation_Tracker.xlsx, a periodic technical-report scaffold, and a README. Use when Ane sets up project tracking after an award or runs '/implementation-pack --donor cerv'; donor is an argument so Gates/OSF/UN are later --donor values. Does not replace the finance system of record, portal reporting, or the authorising officer's sign-off. Distinct from /proposal (pre-award).--- # /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.

Frequently asked questions