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Finding Deleted Feature Flags

Organization
PostHog
finding-deleted-feature-flags

Find feature flags that were soft-deleted in the active project within a recent time window. Use when the user asks "what flags were deleted in the last N days", "show me recently deleted feature flags", "who deleted flag X", "audit recent flag deletions", or anything similar. Handles the non-obvious gotcha that system.feature_flags exposes the deleted boolean but does not expose a deletion timestamp — the actual deleted-at time lives in the per-flag activity log and must be cross-referenced.

Overview

PublisherPostHog
Repositoryskills
Skill namefinding-deleted-feature-flags
Stars
77
Forks
8
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Finding Deleted Feature Flags 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/PostHog/skills/tree/main/skills/omnibus/finding-deleted-feature-flags
  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/PostHog/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/omnibus/finding-deleted-feature-flags .claude/skills/finding-deleted-feature-flags
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Deleted Feature Flags 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 Finding Deleted Feature Flags 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 Finding Deleted Feature Flags 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.

Finding recently deleted feature flags

This skill produces a list of feature flags that were soft-deleted in the active project within a user-specified time window, along with who deleted each one and when.

When to use this skill

  • The user asks "what flags got deleted last week / in the last N days?"
  • The user wants an audit of recent flag deletions (who, when, what was removed)
  • The user wants to find when a specific flag was deleted, or by whom
  • Any "recently deleted feature flags" framing

Don't use this for active stale-flag cleanup — that's cleaning-up-stale-feature-flags. This skill is for flags that have already been removed.

The gotcha that makes this non-trivial

system.feature_flags exposes deleted as a boolean but does not expose deleted_at, updated_at, or last_modified_at. There's no way to filter soft-deleted flags by deletion time in a single SQL query — trying to use those columns will return Unable to resolve field.

The actual deletion timestamp lives in the per-flag activity log, reachable only via posthog:feature-flags-activity-retrieve (one call per flag id). There is no bulk activity endpoint.

So the workflow is two-stage: SQL to enumerate candidates, then parallel activity-log lookups to find each deletion event.

Workflow

1. Clarify the window if ambiguous

"Last week" is ambiguous — it can mean rolling 7 days from now, or the previous calendar week (Mon–Sun). If the user wasn't explicit, ask, or surface both interpretations in the final report.

Always compute the cutoff in UTC and keep the user's local interpretation in your head separately.

2. Enumerate soft-deleted flags via SQL

Query system.feature_flags for deleted = true in the active project, ordered by created_at DESC:

sql
SELECT id, key, created_at
FROM system.feature_flags
WHERE team_id = <team_id> AND deleted = true
ORDER BY created_at DESC
LIMIT 100

Order by created_at DESC because deletions empirically cluster near creation — most flags get deleted within a few days of being created — so walking the most-recently-created candidates first finds recent deletions fastest. But this is a heuristic, not a guarantee: an older flag deleted recently won't be at the top of this list. Be explicit about that limitation when you report.

team_id defaults to the active project, but include it explicitly for clarity.

3. Fan out activity-log lookups in parallel

For each candidate id, call posthog:feature-flags-activity-retrieve with limit: 5, page: 1. Issue all calls in one message so they run concurrently — sequential calls are dramatically slower.

text
call feature-flags-activity-retrieve {"id": <flag_id>, "limit": 5, "page": 1}

Reasonable batch sizes:

  • "last 7 days" → top 20–25 candidates
  • "last 30 days" → top 50
  • "last 90 days" → walk the full ~100

If you sample fewer than the full set, say so in the report and offer to walk the rest as a follow-up.

4. Extract the deletion event from each response

In each response, find the entry where activity == "deleted". That entry's created_at is the actual deletion time, and user.email / user.first_name identify the deleter. These fields are reliable on every delete path.

For most flags there's exactly one delete event. If a flag has been deleted-and-restored multiple times, take the most recent activity: deleted event within the window.

5. Recover the original key and report

Feature flags are renamed to <original>:deleted:<flag_id> when soft-deleted while still referenced elsewhere (e.g. a stopped experiment) — the id-based suffix frees the original key for reuse. Don't try to recover the original from the activity log's own fields: detail.changes only carries the rename on UI/ORM deletes (and is often empty, or missing the key entry, on API/MCP/programmatic deletes), and detail.name just mirrors whatever the current key is — tombstoned or not.

Instead, strip the suffix deterministically with scripts/strip_deleted_suffix.py. Pass it the whole step 2 candidate list as JSON in one call — not one invocation per flag:

bash
echo '[{"id": 1001, "key": "example-banner:deleted:1001"}]' | python3 scripts/strip_deleted_suffix.py
# prints the same array back (pretty-printed), each object gaining an "original_key" field:
# "original_key": "example-banner"

Filter the collected deletion events to those whose created_at falls inside the requested window. Present as a table, using each row's recovered original key (not the raw tombstoned form) for the "Key" column:

| Flag ID | Key | Deleted at (UTC) | Deleted by |

State your methodology in the report (how many candidates you walked vs. how many soft-deleted flags exist total), so the user knows what was and wasn't checked.

Watch-outs

  • Borderline cases: if a deletion is within ~1 hour of the window cutoff, surface it as borderline rather than silently dropping it.
  • Don't trust created_at as a proxy for deletion time: a flag created in 2024 can still have been deleted last week. The activity log is the only authority.
  • Renamed keys are normal: a flag with key foo:deleted:12345 was the flag originally keyed foo — see step 5 for how to recover it.
  • Walking all candidates is possible but slow: ~100 parallel activity-log calls is doable. Offer it as a follow-up rather than the default for short windows.

Example interaction

User: "what flags got deleted in the last week?"

  1. Clarify if needed, or note both interpretations: "rolling 7 days ending now (UTC), in the active project"

  2. Run the SQL enumeration to get up to 100 soft-deleted candidates ordered by created_at DESC

  3. Fan out activity-log lookups in parallel across the top ~25 candidates

  4. Extract activity: deleted entries; filter to those whose created_at >= now - 7 days

  5. Recover original keys with scripts/strip_deleted_suffix.py and report. The values below are illustrative, not real project data:

    text
    Found 2 feature flags deleted in the last 7 days (rolling, ending 2026-08-14 09:30 UTC):
    
    | Flag ID | Key                     | Deleted at (UTC)     | Deleted by  |
    |---------|-------------------------|----------------------|-------------|
    | 1001    | example-banner          | 2026-08-13 15:12     | Jane D.     |
    | 1002    | example-checkout-test   | 2026-08-09 21:48     | John S.     |
    
    Methodology: walked the activity log for the 25 most-recently-created soft-deleted flags.
    The step 2 query hit its 100-row cap, so the project may have more soft-deleted flags than that.
    The other 75 returned candidates were created before late June 2026 and were not checked.
    Want me to walk the rest?

Related tools

  • posthog:execute-sql: Used in step 2 to enumerate soft-deleted candidates against system.feature_flags
  • posthog:feature-flags-activity-retrieve: Used in step 3 to find the actual deletion event for each candidate
  • posthog:feature-flag-get-definition: Useful if the user then wants to inspect what the deleted flag looked like

Scripts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Finding Deleted Feature Flags AI skill do?

Find feature flags that were soft-deleted in the active project within a recent time window. Use when the user asks "what flags were deleted in the last N days", "show me recently deleted feature flags", "who deleted flag X", "audit recent flag deletions", or anything similar. Handles the non-obvious gotcha that system.feature_flags exposes the deleted boolean but does not expose a deletion timestamp — the actual deleted-at time lives in the per-flag activity log and must be cross-referenced.

Why use Finding Deleted Feature Flags on TypingMind?

Because you install it once and use it with any model. Finding Deleted Feature Flags 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 Finding Deleted Feature Flags in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PostHog/skills/tree/main/skills/omnibus/finding-deleted-feature-flags. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Finding Deleted Feature Flags?

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 Finding Deleted Feature Flags?

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

Is the Finding Deleted Feature Flags AI skill free?

Yes. It is published on GitHub by PostHog 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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