Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jstoup111/ai-conductornpx agentmods add skills/jstoup111/ai-conductor/planWrote 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/jstoup111/ai-conductor/plan)<a href="https://agentmods.dev/skills/jstoup111/ai-conductor/plan"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/plan.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Agent Snooping · line 194 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00049 | $0.05804 |
| Opus 5 | $0.00024 | $0.02902 |
| Sonnet 5 | $0.00010 | $0.01161 |
| Haiku 4.5 | $0.00005 | $0.00580 |
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 yesterday.
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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
The technical implementation plan (HOW) that build ships from — the bridge between the
behavioral stories (WHAT) and shipped code. Stories say what the system should do; the plan
decides how: the technical approach, which files change, the 2–5 min TDD tasks, and their
dependencies/sequencing. Any agent can execute it with zero additional context.
This is not a re-listing of the stories. It adds the engineering layer they don't carry:
architecture/approach, file-level changes, task ordering, and dependencies. Traceability runs
PRD FR-N → story → task. Every acceptance criterion maps to at least one task; negative-path
stories become explicit test tasks — not afterthoughts.
Correctness gate: a plan encodes technical assumptions (which files change, how a subsystem
behaves, what an API accepts). Apply the /verify-claims protocol before finalizing tasks —
prefer one cheap Read/grep over a guess, attach a grounded confidence % to claims you cannot
cheaply verify, and HARD-BLOCK (operator approval interactive, HALT if autonomous) on any
unconfirmed assumption that changes the technical approach or task breakdown.
Open with a short Technical Approach (a paragraph or few bullets: the design decisions,
key modules/files, and sequencing) before the task list, so build has the shape of the work
before the steps.
When the approach relies on a local implementation or test pattern, capture only the focused context an implementer needs: the relevant traits, why they fit this work, allowed variation, and search hints for finding comparable code or tests. This is semantic author guidance, not a new header or parser contract. An implementation task affected by that pattern repeats its relevant subset in its own steps, because isolated implementers do not receive the full plan. Do not anchor the guidance to line numbers or snapshots. If the local pattern does not fit, or a departure would change that task's approach, record the verified no-fit result or the authorized departure in that task before BUILD begins.
Keep this focused pattern context distinct from the exact-copy declaration: use the existing paired
**Pattern-source:** and **Rename-map:** headers only when the plan replicates a source pattern,
and preserve their existing separate semantics and grammar.
Read the Scope boundary: from .docs/track/<slug>.md as binding; preserve the confirmed narrow/comprehensive breadth outcome; do not permit a materially broader expansion beyond it unless the operator confirms before it enters the artifact.
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.
- yesterday Changed · +11 lines 9baa733401d0
- 2d ago Changed · +47 lines fb40f4f849cc
- 7d ago First seen · 436 lines · 49 tokens per session scan A d4f5ec8dc2fd
plan is a skill published in the GitHub repository jstoup111/ai-conductor (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 49 tokens to every session and 5,804 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 2026-08-31.
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…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…