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 naimkatiman/continuous-improvement --skill plangit clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/plan)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/plan"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/plan/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/naimkatiman/continuous-improvement/plan"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 114 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 122 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 156 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 105 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 117 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 259 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00011 | $0.05333 |
| Opus 5.5 | $0.00004 | $0.02133 |
| Sonnet 5.5 | $0.00002 | $0.01067 |
| Haiku 4.5 | $0.00001 | $0.00533 |
Grade A, and why
omc-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Use_When>
- User wants to plan before implementing -- "plan this", "plan the", "let's plan"
- User wants structured requirements gathering for a vague idea
- User wants an existing plan reviewed -- "review this plan",
--review - User wants multi-perspective consensus on a plan --
--consensus, "ralplan" - Task is broad or vague and needs scoping before any code is written </Use_When>
<Do_Not_Use_When>
- User wants autonomous end-to-end execution -- use
autopilotinstead - User wants to start coding immediately with a clear task -- use
ralphor delegate to executor - User asks a simple question that can be answered directly -- just answer it
- Task is a single focused fix with obvious scope -- use an execution skill instead of running it from this planning module </Do_Not_Use_When>
<Why_This_Exists> Jumping into code without understanding requirements leads to rework, scope creep, and missed edge cases. Plan provides structured requirements gathering, expert analysis, and quality-gated plans so that execution starts from a solid foundation. The consensus mode adds multi-perspective validation for high-stakes projects. </Why_This_Exists>
<Execution_Policy>
- Auto-detect interview vs direct mode based on request specificity
- Ask one question at a time during interviews -- never batch multiple questions
- Gather codebase facts via
exploreagent before asking the user about them - Plans must meet quality standards: 80%+ claims cite file/line, 90%+ criteria are testable
- Consensus mode runs fully automated by default; add
--interactiveto enable user prompts at draft review and final approval steps - Consensus mode uses RALPLAN-DR short mode by default; switch to deliberate mode with
--deliberateor when the request explicitly signals high risk (auth/security, data migration, destructive/irreversible changes, production incident, compliance/PII, public API breakage) - Planning/execution boundary: planning modes inspect context and produce plans/specs/proposals only. They MUST mark artifacts as
pending approvalunless the user has explicitly opted into execution in the current turn or via the structured approval UI. Before explicit execution approval, planning modes MUST NOT run mutation-oriented shell commands, edit source files, commit, push, open PRs, invoke execution skills, or delegate implementation tasks. - Goal workflow boundary: when a plan compares Claude Code
/goal, Ralph, Team, or artifact-only Ultragoal, identify exactly one primary loop authority and use the deterministic conflict policiesrefuse,adopt_existing, andartifact_onlyrather than non-deterministic warning handling./goalfacts must cite Claude Code/Anthropic sources only (Claude Code/goaldocs: https://code.claude.com/docs/en/goal; Anthropic Claude Code changelog: https://raw.githubusercontent.com/anthropics/claude-code/main/CHANGELOG.md), and plans MUST NOT claim the/goalevaluator independently runs commands or reads files; require surfaced proof evidence before any completion claim. - Goal workflow doc target: for user-facing comparisons, keep examples aligned with
docs/shared/mode-selection-guide.md#goal-oriented-workflow-selectionanddocs/REFERENCE.md#goal-workflow-ux-goal-ralph-team-ultragoal. </Execution_Policy>
Mode Selection
| 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 until agreement with RALPLAN-DR structured deliberation (short by default, --deliberate for high-risk); add --interactive for user prompts at draft and approval steps |
| Review | --review, "review this plan" |
Critic evaluation of existing plan |
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.
- 1mo ago Changed · +16 lines 465eb49b8c30
- 1mo ago First seen · 277 lines · 11 tokens per session scan A 9f7fc9a21050
omc-plan is a skill published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 5,333 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-03.
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