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/archcore-ai/plugin/plannpx skills add archcore-ai/plugin --skill plangit clone --depth 1 https://github.com/archcore-ai/pluginWhat 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 | $0.00194 | $0.01868 |
| Opus 5 | $0.00097 | $0.00934 |
| Sonnet 5 | $0.00039 | $0.00374 |
| Haiku 4.5 | $0.00019 | $0.00187 |
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 2d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/archcore:plan
Plan a feature or initiative through a computed route. The conductor
(skills/_shared/delta-routing.md) derives the canon delta Δ, the gap profile
Π, the zone maturity M, and the risk flags R, then assembles the document
package; instruments produce the documents. Vision types are the primary
output. Reads cover all three categories: vision supplies intent and
resumption targets, knowledge supplies constraints, experience supplies
precedent.
When to use
- "Plan the auth redesign" → computed route — typically
capability: one spec plus one plan - "Create a roadmap for the API migration" → computed route
- "Plan a new feature for CSV export" → computed route
- "Plan the notifications platform" → computed route — typically
umbrella: prd, one spec per capability, one plan - "I need market research before we plan" → acquisition instrument (
sourcesexpert path) - "We're regulated — start the ISO requirements cascade" → iso links (
isoexpert path) - "Investigate X before we plan" / "Compare the alternatives for Y" → research instrument
Not plan:
- Recording a decision →
/archcore:document - Documenting existing code →
/archcore:document - Codifying a team standard →
/archcore:document - Checking documents against code →
/archcore:review
Route computation
Apply in this order:
| Signal | Route |
|---|---|
The user names an expert path — an alias (sdd, sources, iso, research), a route name, or a registry document type |
The named path per the expert invocation map in skills/_shared/delta-routing.md, with no computation |
| Any other request | Compute Δ, Π, M, and R per the Derivation section of skills/_shared/delta-routing.md; its route table decides the package |
| A decision surfaces at a gate | Record the adr through the decision instrument (skills/_shared/tracks/decision.md), then return to the open gate |
Technical-research boundary: market and business discovery belongs to the
acquisition instrument; a request that already proposes a specific target for
team acceptance ("should we switch to Y", "let's adopt Y") belongs to
/archcore:document's decision instrument — research is pre-decision evidence
gathering with no proposed verdict.
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.
- 2d ago First seen · 124 lines · 194 tokens per session scan A 4604def7d4fe
plan is a skill published in the GitHub repository archcore-ai/plugin (53 stars, last pushed 15d ago), licensed Apache-2.0. It adds 194 tokens to every session and 1,868 once invoked, about $0.0010 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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