Skip to content
cccskills
FreePublished on GitHub

run-organic-social-plan-calendar

Builds a two-week schedule of empty social post slots from account audits, platform research, campaigns, seasonal dates, and past performance. It organizes timing, formats, themes, and owners so a separate drafting step can fill in the posts.

OriginalShow the original description

Step of the run-organic-social playbook: turns the audit, research and dated moments into a two-week grid of empty post slots. Run run-organic-social instead; not for direct use.

gooseworks-ai/goose-skills1,272 stars(GitHub 1,272 + cccskills 0)

Run score Not tested

See how to install

Good for

  • Planning two weeks of organic social posts
  • Placing campaign and launch posts on dated slots
  • Balancing post formats and content themes
  • Leaving flexible slots for timely trends

Files included (2)

  • SKILL.md4.5 KB
  • skill.meta.json419 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Plan the calendar

Inputs: the account audit, the voice cards, ../run-organic-social/references/calendar-craft.md, and these sibling artifacts (read each one that exists; never fake one that doesn't):

  • make-content's repurposed sets: gooseworks:file_list content/repurposed/ and content/drafts/ (unslotted social cuts are the raw material).
  • This run's platform research (social/research/<date>-<platform>.md): the patterns and examples every slot is modelled on, and the posting-time and format facts.
  • research-market's latest report (the newest research/market/<D>-read.md): its "What people are saying" section — trends worth posting on, each with a post-by date, and the themes in people's words — and buyer language. Check the section's own date and window line first: research-market runs on ask or monthly, and a narrow run may cover only part of the market. A trend past its post-by date is stale; with no post-by date, a section older than 7 days is stale: use evergreen fallbacks. No such section → no trends from it.
  • plan-campaign: gooseworks:campaign_read (list mode, then one campaign) for any campaign with an organic leg in the window.
  • analyze-performance's organic "what overperformed" read: the latest reports/performance/<date>.md.
  • The brain:
    • When brand_knowledge_* tools are listed: reuse step 1's reads of chapters 19 "this week and what is coming", 20 "our plan" and 23 "what is working" (focus, live promotions, launches, what not to promote, past results); don't open them again. gooseworks:brand_knowledge_search for the seasonal calendar and for customer questions and phrases.
    • Otherwise: gooseworks:knowledge_search for "this week's focus, live promotions, launches, what not to promote, seasonal calendar", and source_types: ["evidence"] for voice-of-customer questions and phrases.
  • The previous calendar: social/calendar/ (what went out, what's still queued).
  • The last run record (the run-memory skill (../run-memory/SKILL.md) Start, loaded by SKILL.md): format tests still open, the per-format scorecard, and recommendations still open.

Procedure

  1. Window. The next 14 days from the human's chosen start (default: next Monday). State the time zone.
  2. Capacity. Slots per week per account, from calendar-craft.md's cadence rules. If the human's ask exceeds it, show the numbers and the recommendation, and let them choose.
  3. Fixed slots first:
    • campaign and launch posts (dated)
    • seasonal and cultural moments for the brand's region. Look each date up with your web tool when you have one; otherwise list the moments and ask the human to confirm dates. Never assume a date from memory.
    • silent days
    • known trends with expiries
  4. Format-test slots: place the 1–2 tests (../run-organic-social/references/calendar-craft.md, Format tests): the missing posts of open tests carried from the last run record first, under their first ids, then new ones. Aim for 4 slots on each side (a test may span two calendars); write each in the calendar header with its record id.
  5. Series slots next: each account's 1–2 named series on their days.
  6. Fill the mix: the remaining slots by the account's bucket ratios, reweighted by the last run's per-format scorecard (more of what beat its baseline, less of what fell below), pulling make-content's cuts first (they're finished thinking), then voice-of-customer questions, then pillar ideas.
  7. Flex slots: 1–2 per week, marked "trend: swap within 48h if one lands". Each also gets an evergreen fallback draft, so the slot is never empty.
  8. Times: the account's own best slots, or labelled starting guesses inside the human's free windows. Vary them.
  9. Balance check:
    • no bucket over its share
    • product mentions within the cap (the managed account's plug-ratio cap when there is one)
    • no two consecutive posts on one account from the same bucket or sub-format (on TikTok every post is video, so vary talking head, POV, tutorial, photo-mode)
    • every video slot has a named shooter

Output

A grid (one row per slot) that the drafting step fills: id · date · time tz · platform · account · format · pillar · bucket · series · test (T1/T2 side, or —) · source · person? · trend expiry

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

<slug>

FreeSummary

The supplied material contains only placeholder text for the skill’s purpose, inputs, steps, and completion criteria. It does not provide enough information to describe a task or result reliably.

  • No use case can be identified from the supplied material
  • Purpose is unspecified
  • Inputs and expected results are unspecified
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

ad-angle-miner

FreeSummary

Research supported advertising angles using buyer context, current product facts, customer language, and observed ads. Turn the evidence into static ad, video, or copy ideas, matching video concepts to available recipes. Scores rank ideas for testing; they do not predict results.

  • Finding evidence-backed angles for a product campaign
  • Developing static ad or video concepts from customer research
  • Writing headline and primary-text options for ads
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Analyze campaign data from Google, Meta, and LinkedIn to find strong and wasteful spend. The skill checks funnel performance and test results, then recommends which campaigns to pause, scale, or test and how to shift budget across channels.

  • Finding campaigns and ads that waste budget
  • Comparing performance across Google, Meta, and LinkedIn
  • Planning specific budget shifts and follow-up tests
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Compare ad spend and signups with downstream outcomes to calculate the cost of each qualified lead. The skill checks tracking coverage, accounts for how long leads need to mature, and groups creatives into actionable categories with confidence notes.

  • Compare true qualified-lead costs across creatives
  • Find low-CPA ads that bring poor-quality leads
  • Check whether signups can be linked to downstream outcomes
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Prepare the intake for a Meta ad campaign by reviewing available brand information first, stating reasonable assumptions, and asking only questions whose answers could affect budget, claims, or legal exposure. It records stable brand facts, a campaign brief, and what was asked versus assumed.

  • Prepare a Meta ad campaign when brand facts are missing
  • Clarify budget and timing before campaign planning
  • Record approved claims and audience restrictions
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

ad-policy-check

FreeSummary

Review ad copy, image text, visuals, and people against platform and brand category rules. It suggests honest fixes and needed disclosures, then flags issues requiring human or legal review, with a separate result for each ad and platform.

  • Checking ad copy and visuals before launch
  • Reviewing creator ads for required disclosures
  • Fixing a platform disapproval and recording the issue
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

See all skills by gooseworks-ai

Report a problem with this skill