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 jstoup111/ai-conductor --skill intakegit clone --depth 1 https://github.com/jstoup111/ai-conductorWrote 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/intake)<a href="https://agentmods.dev/skills/jstoup111/ai-conductor/intake"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/intake/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/jstoup111/ai-conductor/intake"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/intake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.04196 |
| Opus 5.5 | $0.00019 | $0.01678 |
| Sonnet 5.5 | $0.00010 | $0.00839 |
| Haiku 4.5 | $0.00005 | $0.00420 |
Grade A, and why
intake 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 19d 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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Authors high-quality intake issues: the write-side twin of /composer's claim side.
An intake issue decides WHAT (the problem, its impact, its evidence) and highlights
OUTCOMES (what must be observably true when it's fixed). The engineer's DECIDE phase
owns HOW. A great intake issue lets a zero-context engineer start debugging
immediately — from the evidence in the issue alone — without anchoring them to the
filer's first idea of a fix.
This skill applies whether the filer is the operator (from chat or phone) or an agent
filing on the operator's behalf (halt monitor, follow-up proposals, bugs discovered
mid-build). GitHub's intake issue form (.github/ISSUE_TEMPLATE/intake.yml) scaffolds
the same shape on web/mobile; anything filed via gh issue create must follow it by
hand — this skill is how.
The Intake Shape
Four sections. Three are required.
| Section | Required | Contents |
|---|---|---|
| Observed | yes | Evidence of the problem — verbatim artifacts, not narrative |
| Impact | yes | One line minimum: who or what hurts, how often, what it costs / unblocks |
| Desired outcome | yes | Observable behavior that must hold afterward |
| Hypotheses | no | The filer's guesses about HOW — explicitly labeled as guesses |
Practices
1. Gather Evidence First — While Context Is Warm
Collect concrete artifacts before writing a word of prose. Evidence is cheapest at the moment of observation; an hour later the logs have rotated and the repro is fuzzy.
Collect whichever of these exist:
- Exact commands + verbatim output. Copy the real invocation and the real output, trimmed to the relevant lines. Never paraphrase an error message — the exact string is what the engineer will grep for.
- Log excerpts with their source path and timestamp. e.g.
.daemon/daemon.log,monitor.log, CI run URLs. Include a few lines of surrounding context, not just the one scary line. - Precise references.
file:line, commit SHAs, PR/issue numbers, run IDs. These are evidence, and always welcome — a file path cited as proof is not a "how". - Reproduction steps. The minimal sequence a zero-context reader could run to see the problem. State what you expected vs what happened.
- Frequency and scope data. How many times, since when, how widespread — a grep count, a ledger scan, "3 of the last 5 daemon runs". Turns anecdote into signal.
- Environment facts when plausibly relevant: versions, branch, config values.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 19d ago Changed 151fe14f5be6
- 27d ago Changed · +10 lines a8072bc048e3
- 1mo ago Changed · +4 lines 49276d2ec34f
- 2mo ago First seen · 342 lines · 48 tokens per session scan A bffdc6b9c179
intake is a skill published in the GitHub repository jstoup111/ai-conductor (7 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 4,196 once invoked, about $0.0002 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 2026-08-31.
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Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.
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A workflow for carrying an engineering task from scope through implementation and reviewable delivery. It works with a repository's branch, existing changes, rules, checks, and permissions while separating completed, unverified, and next-step work.
shiplog
Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.
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