Shiplog logo

Shiplog

Organization
aeonfun
shiplog

Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.

Overview

Publisheraeonfun
Repositoryaeon
Skill nameshiplog
Stars
771
Forks
280
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aeonfun on GitHub. Read the source before you install it.

Installation

Install the Shiplog AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aeonfun/aeon/tree/main/skills/shiplog
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/shiplog .claude/skills/shiplog
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Shiplog in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Shiplog on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Shiplog is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

${var} — Optional, space-separated flags:

  • since:YYYY-MM-DD — override the window start (default: when this skill last ran).
  • days:N — window = last N days.
  • dry-run — render to stdout; write no article, no state, no notify.
  • owner/repo — narrow GitHub coverage to that one repo.
  • any other word — focus/theme filter (a product name or keyword).

Empty = everything shipped since the last run, across all configured repos.

Produce two artifacts:

  1. Digest — a themed, human-readable recap of everything that shipped + traction (the article).
  2. Shiplog post — a tight, bulleted, ready-to-post version in the operator's voice, every project @-tagged (the notification).

This is cadence-agnostic: the window is always "since the last run" (memory/state/shiplog-last.json), so the aeon.yml schedule alone decides whether this is a daily, weekly, or on-demand recap. One skill, any frequency.

Read STRATEGY.md, memory/MEMORY.md, and the last 7 days of memory/logs/ for context. Read soul/SOUL.md + soul/STYLE.md before writing any output — the shiplog post must sound like the operator, not a changelog bot. If soul/ is empty, use a clear, direct, neutral voice (drop the signature flourishes below).

Config — all derived, nothing hardcoded

operator        = gh api user --jq .login            # the authenticated operator (PR-author search)
product_handles = memory/products.md `handles:` lines (@x)        # product X accounts to read
flagship_repos  = memory/products.md `repos:` tagged (public)     # the star / north-star story
watched_repos   = memory/watched-repos.md, else products.md repos: # everything shipped across
ecosystem_scouts= memory/products.md `scouts:` line (optional)    # recap accounts to scan for features
star_state      = memory/state/shiplog-stars.json    # snapshot for week-over-week star deltas

If neither watched-repos.md nor products.md yields a repo, exit SHIPLOG_NO_REPOS (notify + log, no article). Sections whose config is absent (X handles, scouts) are skipped gracefully, not failed.

Steps

1. Compute the window — "since last run"

bash
STATE="memory/state/shiplog-last.json"
NOW=$(date -u +%Y-%m-%dT%H:%M:%SZ)
TODAY=$(date -u +%Y-%m-%d)
LAST=""
[ -f "$STATE" ] && LAST=$(jq -r '.last_run_at // empty' "$STATE" 2>/dev/null)
SINCE="${LAST:-$(date -u -d '7 days ago' +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || date -u -v-7d +%Y-%m-%dT%H:%M:%SZ)}"
SINCE_DATE="${SINCE%%T*}"
  • since:YYYY-MM-DD in ${var} → SINCE = that date at T00:00:00Z; days:N → N days ago. These override the state file.
  • Use $SINCE for ALL time filtering — never substitute "since Monday" or other drift-prone shortcuts. The window is [$SINCE, $NOW); state the span ($SINCE_DATE → $TODAY) in the output.
  • Idempotency is the state file (step 8 advances it each run, so windows never overlap). No once-per-day lock — a back-to-back re-run just yields an empty window → SHIPLOG_NOTHING_NEW. Write the digest to output/articles/shiplog-${TODAY}.md; if that name exists and there's genuinely new activity since the last run, use output/articles/shiplog-${TODAY}-2.md rather than clobbering.

2. GitHub activity (the bytes)

Cross-repo PR/commit visibility needs the global token. When GH_GLOBAL is set the workflow already exports it as gh's GH_TOKEN, so plain gh calls reach every repo it can see; don't put the token on the command line (a $SECRET expansion is refused):

bash
# gh reads GH_TOKEN from the environment (GH_GLOBAL when set, else the repo-scoped token)
OPERATOR=$(gh api user --jq .login 2>/dev/null)

Track success/failure per source in a sources map; on a single endpoint failure log fail and continue — never abort the whole skill.

a) Operator PRs across all repos in the window (grouped by repo + totals):

bash
gh search prs --author "$OPERATOR" --created ">=$SINCE_DATE" \
  --json number,title,repository,state,createdAt,url --limit 100 \
  --jq 'group_by(.repository.nameWithOwner)[] | {repo: .[0].repository.nameWithOwner, count: length,
         prs: [.[] | {date: .createdAt[0:10], state, number, title}]}'

If 100 rows come back, note the result may be truncated.

b) Flagship headline numbers — for each flagship_repos entry, count commits + merged PRs in the window (the numbers the audience cares about):

bash
for REPO in $FLAGSHIP_REPOS; do
  gh api "repos/${REPO}/commits" -X GET -f since="$SINCE" \
    --jq "\"$REPO commits: \" + ([.[] | .sha] | length | tostring)" 2>/dev/null
  gh api "repos/${REPO}/pulls" -X GET -f state=closed -f sort=updated -f direction=desc \
    --jq "\"$REPO merged PRs: \" + ([.[] | select(.merged_at != null and .merged_at > \"$SINCE\")] | length | tostring)" 2>/dev/null
done

c) The security flex — PRs the operator landed in repos they do NOT own (the "a project merged a fix from us" candidates). Filter the Step-2a result to external repos whose title matches a security keyword (security|ssrf|cve|credential|sandbox|escape|injection|vuln|redos|xss|toctou|path traversal|prototype pollution|deserial). Merged ones are the marquee story — if it's a named org (not a random fork), that's a headline bullet.

d) Star delta (north-star metric — flagships only, they're public):

bash
mkdir -p memory/state
for REPO in $FLAGSHIP_REPOS; do
  gh api "repos/${REPO}" --jq '.stargazers_count'   # current total for $REPO
done

Read the prior snapshot memory/state/shiplog-stars.json (if present): delta = current_total − last_total per repo. After computing, overwrite the snapshot with { "<repo>": {"count": N, "date": "${TODAY}"}, ... }. If no prior snapshot exists, report totals only and note "no baseline yet — deltas start next run." Do NOT fabricate a delta.

3. X activity (direct X.AI curl — primary)

XAI_API_KEY is injected into this skill's environment (declared in requires:) and is the primary way to read X. For each X source below the primary fetch is a direct curl to https://api.x.ai/v1/responses with Authorization: Bearer {XAI_API_KEY}, using Grok's x_search tool. There is no network sandbox blocking this — just make the calls.

Check the key and give the call room first:

bash
[ -n "$XAI_API_KEY" ] && echo KEY_PRESENT || echo KEY_UNSET

x_search runs a live X search and typically takes 30–120s. When you invoke the Bash tool for any curl below, set the tool's timeout to at least 180000 (180s); each curl carries --max-time 150 so it fails cleanly instead of hanging. A slow curl is not a missing key — never treat a timeout as key-unavailable. If KEY_PRESENT (it will be), Path A is required for every X source.

There are two X sources here — the operator handle ($OPERATOR_HANDLE, the operator's own X account from memory/products.md) and the product/project accounts ($PRODUCT_HANDLES). Fetch each independently into its own tmp file so one failing source can't clobber or sink another.

Path A — X.AI API (primary).

Operator posts (SRC=operator):

bash
jq -n --arg h "$OPERATOR_HANDLE" --arg sd "$SINCE_DATE" --arg td "$TODAY" '{model:"grok-4.7", input:[{role:"user", content:("Search X for posts by @" + $h + " between " + $sd + " and " + $td + ". Return each post with full text, date, type (original|reply|RT - an RT text starts with \"RT @\"), exact engagement counts (likes, retweets, replies; 0 if unknown), and the direct link https://x.com/" + $h + "/status/ID. Return chronological.")}], tools:[{type:"x_search"}]}' > /tmp/xai-shiplog-operator-payload.json
HTTP=$(./secretcurl -s -o /tmp/xai-shiplog-operator.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
  -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-shiplog-operator-payload.json)
echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-shiplog-operator.json)"

Product/project accounts (SRC=projects):

bash
jq -n --arg sd "$SINCE_DATE" --arg td "$TODAY" --arg ph "$PRODUCT_HANDLES" '{model:"grok-4.7", input:[{role:"user", content:("Search X for posts between " + $sd + " and " + $td + " from these accounts: " + $ph + ". Focus on launches, announcements, and any brag about a security fix merged into another project. For each: @handle, full text, date, exact engagement counts (likes, retweets, replies; 0 if unknown), and the direct link https://x.com/handle/status/ID. Skip retweets of others.")}], tools:[{type:"x_search"}]}' > /tmp/xai-shiplog-projects-payload.json
HTTP=$(./secretcurl -s -o /tmp/xai-shiplog-projects.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
  -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-shiplog-projects-payload.json)
echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-shiplog-projects.json)"

For each source, on HTTP=200 with a non-empty body, parse that source's file with the standard extractor and mark x_source=api:

bash
jq -r '.output[] | select(.type == "message") | .content[] | select(.type == "output_text") | .text' /tmp/xai-shiplog-<SRC>.json

From the operator text, separate original posts from RTs (RT text starts with RT @) — RTs are amplification, not ships. From the projects text, note the bangers (sort by likes/views) — one or two feed the digest's narrative section.

On a real failure, skip that source — never fabricate posts. If a source's curl returns non-200, an empty/unparseable body, or times out, record the true reason for that source and continue with whatever other sources succeeded. Reason codes: key-unset (only if the check above printed KEY_UNSET), http-<code> (non-2xx), empty (200 but no posts parsed), timeout (exceeded --max-time). Never write "XAI_API_KEY unavailable" when the key was set.

Path B — WebFetch last resort (per source, optional). Only if a source's Path A failed for one of the real reasons above: WebFetch that source's public https://x.com/<handle> profile(s) — no auth — and mark that source x_source=webfetch (lower quality; prefer posts inside the window). If every X source fails both paths, set x_source=none and write the GitHub-only shiplog (note the gap) — never abort.

4. Ecosystem + traction sweep (best-effort — skip gracefully)

  • Ecosystem mentions — only if ecosystem_scouts (scouts:) is configured. Fetch with the same Path A X.AI curl as Step 3, into its own tmp file (SRC=ecosystem):
    bash
    jq -n --arg sd "$SINCE_DATE" --arg td "$TODAY" --arg es "$ECOSYSTEM_SCOUTS" --arg ph "$PRODUCT_HANDLES" '{model:"grok-4.7", input:[{role:"user", content:("Search X between " + $sd + " and " + $td + " for posts from these recap/scout accounts: " + $es + " that mention any of these products: " + $ph + ". Return each mention with @handle, follower_count, full text, date, and the direct link https://x.com/handle/status/ID - recaps, rankings, partner shares.")}], tools:[{type:"x_search"}]}' > /tmp/xai-shiplog-ecosystem-payload.json
    HTTP=$(./secretcurl -s -o /tmp/xai-shiplog-ecosystem.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
      -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-shiplog-ecosystem-payload.json)
    echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-shiplog-ecosystem.json)"
    On HTTP=200 + non-empty, parse /tmp/xai-shiplog-ecosystem.json with the standard extractor. On a real curl failure (http-<code> / empty / timeout, or key-unset) skip this source with the true reason — never fabricate a mention. Confirm any handle is real before @-mentioning (a wrong tag in a public post is worse than none). Capture follower counts for the flex ("featured by @X (Nk)"). Skip entirely if no scouts: configured.
  • Product traction (OpenRouter / x402 / analytics) — only if a source is configured for the product. If you have an app/server id, WebFetch its page; otherwise say "no product-traction sources wired yet" and move on. Keep any number exactly as measured — don't round 79 → ~80.

5. Classify the window

ConditionStatusAction
0 PRs AND 0 flagship commits AND no notable XSHIPLOG_NOTHING_NEWNotify-optional — no article. Skip to Step 8, still advance state.
< 3 substantive ships totalSHIPLOG_LIGHTShort post (3-bullet form).
OtherwiseSHIPLOG_OKFull digest + post.

If ${var} narrows to one repo/project and nothing matched, status SHIPLOG_NO_MATCH — notify and exit (still advance state).

6. Synthesize + write the article

Output handling — no PR. This is a content skill: write the article straight to output/articles/ and let the workflow's commit step push it to main (same as the article skill). Do not create a branch or open a pull request — CLAUDE.md's "branch + PR, never push to main" rule is for source-code changes, not generated articles.

Write the digest to output/articles/shiplog-${TODAY}.md: themed "what shipped" sections, a By-the-numbers line (PRs · commits · star deltas), traction/ecosystem, and the gaps you hit. Then append the ready-to-post shiplog using this template — load soul/STYLE.md first so the register matches (if soul/ is empty, write a plain, direct post and drop the ⭐ sign-off):

<product(s)> shiplog ⭐ <span: month day → day>

shipped ~<N> PRs + <M> commits this window. the bytes:

- <punchy ship 1>: <one-line what+why>. <@handles of projects involved>
- <punchy ship 2>: ...
- <punchy ship 3>: ...
- security: fixes into other people's repos (<types>) — even got one merged into <@MarqueeOrg>'s <repo>

traction:
- <product> <total> ⭐ (+<delta> this window)
- featured by <@scout> (<followers>) "<quote>" + ranked #<rank> <list>

⭐
  • Tag every project with a handle you verified. If you couldn't confidently resolve one, leave it untagged and say which is missing.
  • Keep numbers exactly as measured. If a flex (a security merge, a milestone) landed just outside the window, keep it but flag the date.
  • Also draft two variants below the post: a tight thread (hook + one tweet per ship) and a 3-bullet short version.

7. (folded into 6)

8. Advance the state file

Unless dry-run, record this run so the next one starts where this ended:

bash
mkdir -p memory/state
HEAD_SHA=$(git rev-parse HEAD 2>/dev/null || echo "")
jq -n --arg at "$NOW" --arg sha "$HEAD_SHA" --arg win "$SINCE" \
  '{last_run_at:$at, last_commit_sha:$sha, window_start:$win}' > memory/state/shiplog-last.json

Advance on every completed run — including SHIPLOG_NOTHING_NEW / SHIPLOG_LIGHT / SHIPLOG_NO_MATCH — so the window always moves forward. The workflow auto-commits it (no git here). On dry-run, do NOT write it.

9. Notify

bash
REPO_URL=$(gh repo view --json url -q .url)
ARTICLE_URL="${REPO_URL}/blob/main/output/articles/shiplog-${TODAY}.md"

Write the ready-to-post shiplog to a temp file — /tmp/shiplog-notify.md — and send it with ./notify -f /tmp/shiplog-notify.md (use -f rather than ./notify "$(cat …)" so a long multi-line post is passed as a file instead of a giant argv). Append ${ARTICLE_URL} as the last line. For SHIPLOG_NOTHING_NEW / SHIPLOG_NO_MATCH, send a one-line status instead of the post (or stay silent on sub-daily cadences).

10. Log

Append to memory/logs/${TODAY}.md:

### shiplog
- Status: SHIPLOG_OK | SHIPLOG_LIGHT | SHIPLOG_NOTHING_NEW | SHIPLOG_NO_MATCH | SHIPLOG_NO_REPOS
- Window: ${SINCE_DATE} → ${TODAY}  (var: ${var:-none})
- PRs / flagship commits: N / M   ·   external-security PRs: K
- Stars: <repo> <total> (+d) … [or: no baseline yet]
- X source: api | webfetch | none (+ per-source skip reasons if any: operator=http-<code> | projects=timeout | ecosystem=empty …)
- Article: output/articles/shiplog-${TODAY}.md (if written)
- State advanced to: ${NOW} (unless dry-run)
- Sources: prs=ok|fail · commits=ok|fail · stars=ok|fail · x=ok|fail · ecosystem=ok|fail

Fetching & sources

  • GitHub: every call uses gh (auth handled internally) — never curl the GitHub API. For cross-repo reach set GH_GLOBAL (the workflow exports it as GH_TOKEN; never pass it inline); with only the built-in token you'll see this repo plus public repos, which still covers public flagships.
  • X: XAI_API_KEY is injected into this skill's env (it's in requires:), and the primary path for every X source is a direct curl https://api.x.ai/v1/responses with Authorization: Bearer {XAI_API_KEY} (Step 3). There is no network sandbox blocking this. Attempt the curl (--max-time 150, Bash tool timeout ≥180000) before any fallback, and on a real failure skip that source with the true reason (key-unset / http-<code> / empty / timeout) — WebFetch of the public x.com/<handle> profile is a lower-quality last resort only.
  • Never abort on a single source failure — note the gap in the digest and still write + notify.

Constraints

  • The window is always "since last run" (state file) unless ${var} overrides it — never hardcode 7 days except as the first-run default. Always advance memory/state/shiplog-last.json on a real run, even a quiet one.
  • Content, not code: write the article to output/articles/ and let the workflow commit it to main. Never open a per-run PR for the shiplog.
  • Every concrete claim traces to real data — a PR (#N), a commit, a measured number, or a fetched tweet (with its permalink). No invented activity, no fabricated star deltas.
  • RTs are amplification, not ships — narrative/ecosystem only, never "the bytes".
  • Verify a handle before @-mentioning it; an unverified tag stays untagged.
  • Voice from soul/; neutral and direct if soul/ is empty. No hype adjectives, no hashtags.
  • The notify URL is the GitHub web URL via gh repo view --json url, not the SSH remote.

Frequently asked questions

What does the Shiplog AI skill do?

Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.

Why use Shiplog on TypingMind?

Because you install it once and use it with any model. Shiplog is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Shiplog in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/shiplog. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Shiplog?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Shiplog?

As many as you like. As long as a model supports skills, you can use Shiplog with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Shiplog AI skill free?

Yes. It is published on GitHub by aeonfun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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