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 agentmods add commands/spotify/confidence-ai-plugins/migrate-optimizely-plan-accessgit clone --depth 1 https://github.com/spotify/confidence-ai-pluginsWhat 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 | $0.00056 | $0.00533 |
| Opus 5 | $0.00028 | $0.00267 |
| Sonnet 5 | $0.00011 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
migrate-optimizely-plan-access 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 3d 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.
What it actually says
Treat this slash command as /migrate-optimizely plan access.
Read skills/migrate-optimizely/SKILL.md and
skills/migrate-optimizely/access.md (agent-only; do not narrate).
Follow Plan Access: Steps in SKILL.md — the same pattern as
Plan Flag: Steps (overview first, resume check, step tracker,
Generation Status after each step, consent rows, then stop).
- Display the migration overview, then: Starting Phase 0 — Access
- Resume check:
.claude/plans/optimizely-access-migration-*.md(Read if it exists; do not create a new file yet) - Show the Plan Access step tracker
- Run Opening questions in
access.md(source method first: REST, files, or the user-provided fallback). Stop and wait. Do not write the plan file. Do not dump a token request until they pick Live REST API. - After they answer: create the plan file from the access.md
template. Then extract. If they picked Desktop JSON, search
~/Desktopthen~/Downloadsfor files that relate users to groups/teams; confirm before using. - After the access file (or REST) is confirmed: run Extract
context in
access.md(look around that file for internal access-migration strategy / exceptions, or paste, or skip). Then translate, Flag-client proposal (ASK; skip if no SDK keys), consent rows, finish the plan. No invites. No groups. No Flag clients.
After Overall is ✓ complete, ASK the Step 5 exit question in
SKILL.md (required — there is no automatic path into adjust):
(1) Adjust access, (2) Tick consent, (3) Execute access
(only if consent already ticked), (4) Done for now. If they pick
(1), enter adjust in the same turn; do not require
/migrate-optimizely adjust access.
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.
- 3d ago First seen · 42 lines · 56 tokens per session scan A 64624a333847
migrate-optimizely-plan-access is a command published in the GitHub repository spotify/confidence-ai-plugins (8 stars, last pushed yesterday), licensed Apache-2.0. It adds 56 tokens to every session and 533 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
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