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 skills/zereight/gitlab-mcp/plannpx skills add zereight/gitlab-mcp --skill plangit clone --depth 1 https://github.com/zereight/gitlab-mcpWhat 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 | $0.00033 | $0.00462 |
| Opus 5 | $0.00016 | $0.00231 |
| Sonnet 5 | $0.00007 | $0.00092 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
plan 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- plan — 100% identical, 0 lines differ
What it actually says
Plan
Creates comprehensive, actionable work plans through intelligent interaction. Auto-detects whether to interview (broad requests) or plan directly (detailed requests).
Modes
| Mode | Trigger | Behavior |
|---|---|---|
| Interview | Default for broad requests | Interactive requirements gathering |
| Direct | --direct, or detailed request |
Skip interview, generate plan directly |
| Consensus | --consensus, "ralplan" |
Planner → Architect → Critic loop |
| Review | --review |
Critic evaluation of existing plan |
Interview Mode (broad/vague requests)
- Classify request: broad triggers interview
- Ask ONE focused question at a time for preferences, scope, constraints
- Gather codebase facts via @explore BEFORE asking user
- Consult @analyst for hidden requirements
- Create plan when user signals readiness
Direct Mode (detailed requests)
- Optional brief @analyst consultation
- Generate comprehensive work plan immediately
Consensus Mode (--consensus / "ralplan")
- @planner creates initial plan with RALPLAN-DR summary (Principles, Decision Drivers, Options)
- @architect reviews for architectural soundness (sequential, NOT parallel with critic)
- @critic evaluates quality criteria (after architect completes)
- Re-review loop (max 5 iterations) if critic rejects
- Apply improvements on approval
- Final plan includes ADR (Decision, Drivers, Alternatives, Why chosen, Consequences)
Review Mode (--review)
- Read plan from
.omc/plans/ - @critic evaluates
- Return verdict: APPROVED / REVISE / REJECT
Output
Plans saved to .omc/plans/. Include:
- Requirements Summary
- Testable Acceptance Criteria
- Implementation Steps (with file references)
- Risks and Mitigations
- Verification Steps
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 · 54 lines · 33 tokens per session scan A c9bd134f1392
plan is a skill published in the GitHub repository zereight/gitlab-mcp (1,939 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 462 once invoked, about $0.0002 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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