agentleFS
dgilforddgilford/ai-science-toolkit/skills/handoff/SKILL.md

handoff

Skill82 starsChanged 42 days agoMITView on GitHub

Create or update a durable project handoff for the next AI agent/session. Use when ending a coding/research session, switching agents, compressing context, or preparing another Claude/ChatGPT/Codex session to continue the work.

191 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use decision-mapping, setup-ts-deep-modules and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • FabienDehopreSkill

    decision-mapping

    Turn a loose idea into a sequenced map of investigation tickets, then drive them to resolution one at a time.

    Also in 8 projects that use this

  • liufeng1976Skill

    setup-ts-deep-modules

    Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.

    Also in 42 projects that use this

  • AWSSkill

    to-questionnaire

    Turn a decision you can't fully answer into a questionnaire for someone else to fill in.

    Also in 93 projects that use this

  • GoogleSkill

    ask-matt

    Ask which skill or flow fits your situation. A router over the skills in this repo.

    Also in 87 projects that use this

  • RoentekSkill

    loop-me

    Grill me about specs for the workflows I want to build, within this workspace.

    Also in 51 projects that use this

  • LesaniSkill

    writing-beats

    Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.

    Also in 54 projects that use this

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 2 are distinctive, and 6 are very specific to this project.

  • 0

    Common

    Most of the 172 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 6

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Create or update .ai/HANDOFF.md so the next agent can resume without rereading this conversation.

    line 11
  2. Very specific:

    Read these if they exist: .ai/HANDOFF.md, CLAUDE.md, AGENTS.md.

    line 33
  3. Very specific:

    Create .ai/ if missing. Append .ai/ to .gitignore if not already present.

    line 39
  4. Distinctive:

    Use only the sections that have real content.

    line 41
  5. Distinctive:

    Ask me at most one question, only if it would materially improve the handoff.

    line 76
  6. Very specific:

    If this session produced something meant to be shared (a doc, Slack message, post, figure, published artifact) with no matching entry in …

    line 80
  7. Very specific:

    After .ai/HANDOFF.md is written, run the worklog skill to capture this session to its three targets (local .ai/ mirror, remote server …

    line 87
  8. Very specific:

    Run the evolve-claude-md skill to promote any durable session knowledge into the project's canonical agent file (CLAUDE.md, or the …

    line 95

“Common” is measured: kept by at least 6 in 10 of the 172 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Create or update .ai/HANDOFF.md so the next agent can resume without rereading this conversation.

Current repo state

git status --short 2>/dev/null || echo "(not a git repo)"
git log --oneline -12 2>/dev/null || echo "(no git log)"
git diff --stat HEAD 2>/dev/null | head -30 || echo "(no diff)"
claude --version 2>/dev/null || echo "(claude --version unavailable)"

Before writing

Read these if they exist: .ai/HANDOFF.md, CLAUDE.md, AGENTS.md. Scan recently modified files for TODO/FIXME markers. Do not invent results for commands that were not run.

Do not duplicate content already captured in durable artifacts (specs, plans, ADRs, issues, commits, diffs, CLAUDE.md). Reference them by path or URL and summarize only what the next agent needs to act. This includes review reports archived under .ai/reviews/ — point at them by path (e.g. in Next actions or Pitfalls) rather than restating findings. Redact anything sensitive — API keys, tokens, passwords, PII — even though .ai/ is gitignored.

Write .ai/HANDOFF.md

Create .ai/ if missing. Append .ai/ to .gitignore if not already present.

Use only the sections that have real content. Skip empty ones.

Objective

<!-- one sentence -->

Next actions

<!-- numbered, concrete, specific — most important section; each action must be immediately executable without a follow-up question. Where a skill or subagent is the right tool, name it in the action itself (e.g. `/grilling` before a state-changing step; the stats-reviewer subagent once the estimator is drafted); point at `/pathfinder` if the next agent won't know which to reach for. --><!-- **Good**: `Run scripts/validate.py --baseline 1991-2020 and check the NaN count in coastal cells` --><!-- **Good**: `Run /grilling on the resampling design before editing pipeline.py` --><!-- **Bad**: `Continue working on the validation` -->

State

<!-- branch, environment, anything non-obvious; files changed and why --><!-- If the session established harness-behavior facts (parser, hooks, skill loading), record the `claude --version` from the preamble — those facts are version-scoped. Register: docs/harness-behavior.md in ai-science-toolkit. -->

Decisions

<!-- non-obvious choices made this session: what, why, what was rejected -->

Scientific context

<!-- datasets, versions, paths, provenance; baselines and reference periods; counterfactual definitions; methodological assumptions; known data issues: NaNs, masking, coordinate alignment, scaling -->

Pitfalls

<!-- what already went wrong, or is likely to -->

Open questions

<!-- only if genuinely unresolved and consequential: the question, what breaks if answered differently, best current recommendation -->

Preserve useful prior content. Remove stale or superseded details. Distinguish facts, assumptions, and recommendations explicitly.

Ask me at most one question, only if it would materially improve the handoff.

Unrated shareables

If this session produced something meant to be shared (a doc, Slack message, post, figure, published artifact) with no matching entry in .ai/disclosures.jsonl, list it in one line in your reply and suggest running the disclose skill (/disclose). Don't run it from here — the rating is interactive and belongs to the artifact, not the session.

Ship a worklog entry

After .ai/HANDOFF.md is written, run the worklog skill to capture this session to its three targets (local .ai/ mirror, remote server cache, Notion weekly page). Pass it the session summary and next actions you wrote above so it does not re-derive them. worklog is best-effort by design — it must never block or fail the handoff.

Finally

Run the evolve-claude-md skill to promote any durable session knowledge into the project's canonical agent file (CLAUDE.md, or the AGENTS.md it redirects to — the skill resolves which).

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.