Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PostHog/skills --skill finding-deleted-feature-flagsgit clone --depth 1 https://github.com/PostHog/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/posthog/skills/finding-deleted-feature-flags)<a href="https://agentmods.dev/skills/posthog/skills/finding-deleted-feature-flags"><img src="https://agentmods.dev/badge/skills/posthog/skills/finding-deleted-feature-flags/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/posthog/skills/finding-deleted-feature-flags"><img src="https://agentmods.dev/badge/skills/posthog/skills/finding-deleted-feature-flags.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.01833 |
| Opus 5 | $0.00055 | $0.00916 |
| Sonnet 5 | $0.00022 | $0.00367 |
| Haiku 4.5 | $0.00011 | $0.00183 |
Grade A, and why
finding-deleted-feature-flags scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to finding-deleted-feature-flags — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago Changed · +8 lines d3d2f13279f4
- 13d ago First seen · 122 lines · 109 tokens per session scan A b76d6f2627cc
finding-deleted-feature-flags is a skill published in the GitHub repository PostHog/skills (61 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 1,833 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to finding-deleted-feature-flags, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…