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 ReinaMacCredy/maestro --skill maestro-improvegit clone --depth 1 https://github.com/ReinaMacCredy/maestroWrote 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/reinamaccredy/maestro/maestro-improve)<a href="https://agentmods.dev/skills/reinamaccredy/maestro/maestro-improve"><img src="https://agentmods.dev/badge/skills/reinamaccredy/maestro/maestro-improve/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/reinamaccredy/maestro/maestro-improve"><img src="https://agentmods.dev/badge/skills/reinamaccredy/maestro/maestro-improve.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 | $0.00054 | $0.02892 |
| Opus 5.5 | $0.00022 | $0.01157 |
| Sonnet 5.5 | $0.00011 | $0.00578 |
| Haiku 4.5 | $0.00005 | $0.00289 |
Grade A, and why
maestro-improve 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 8d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
maestro-improve
Use when a Lead assigns improvement work on one target to a Peer. The target is the only parameter; a separate Peer challenges the returned candidate before acceptance.
A correction that stays in a transcript is spent when that session ends. The lesson record is what survives, and doctrine is the only thing a future session actually reads. This skill is the one place the two are joined: it turns filed corrections into the smallest edit that would have prevented them.
Folds rules from pstack's reflect and correct (Lauren Tan, MIT; notice in THIRD_PARTY.md).
The parameter
The target names the doctrine a lesson corrects: a recipe section, the SLP
Workspace Pack in src/plugins/resources/SLP.md, a Hub template, a
skills/maestro-* file, or a repository's Workspace Protocol. Where each one
lives, and what an edit to it costs, is in
references/targets.md.
Intakes
Most lessons are filed at the correction, by the seat that took it. Two
intakes catch the ones nobody filed. Both end in maestro lesson file, and
neither edits doctrine. Filing is reversible, so it waits for no approval; the
edit a lesson later produces passes the replay gate and the challenge Peer
like any other.
Session harvest. Run it after a session whose corrections nobody marked.
Skip a trivial session, or one that followed its doctrine correctly. A one-off
is not a lesson. The source is the session's record, not a transcript file.
That record is maestro trace <id> for each work item the session touched,
its failed: notes, its rework marks, the decisions it recorded with
--replaces, and a short digest of the conversation written by the session
itself. Hand the record to three read-only subagents in parallel, one lens
each:
- Judgment. The durable principle behind a specific incident: a mistake and its correction, an owner preference, a decision and its reason, friction in a skill or a delegation, a manual step repeated often enough to encode.
- Tooling. The command, flag, path or quirk a future agent would otherwise re-derive, and every moment the owner supplied context the agent could have fetched itself.
- Divergent. What the other two lenses will miss: a decision right for the wrong reason, a check reported instead of run, a second-order effect on callers, a skill invoked late or not at all.
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.
- 8d ago Changed · +91 lines 9605df7bbf2c
- 27d ago Changed · +32 lines 1ce8b1b0a84e
- 1mo ago Changed · +54 tokens per session 82f7a83fe0ec
- 1mo ago Changed · +6 lines · -54 tokens per session a00237e3d162
- 1mo ago First seen · 108 lines · 54 tokens per session scan A f6f8a1a70239
maestro-improve is a skill published in the GitHub repository ReinaMacCredy/maestro (235 stars, last pushed 9d ago), licensed MIT. It adds 54 tokens to every session and 2,892 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 2026-08-30.
Other skills, from other repositories
pn-github-vertical-slices
Break a plan or PRD into tracer-bullet vertical slices and create GitHub Issues via GitHub MCP (official github/github-mcp-server). Dependencies first (AFK/HITL). Use after pn-writing-plans or pn-create-prd when work must ship as Issues.
github-copilot-upgrader
Use this to update the Github Copilot CLI/SDK.
commit
Commit staged or unstaged changes with an AI-generated commit message that matches the repository's existing commit style. Use when the user asks to 'commit', 'commit changes', 'create a commit', 'save my work', or 'check in code'.
create-pr
Creates a GitHub PR with a Linear-ticket-prefixed title and a decision-led, narrative description for Prisma 8. Use when the user wants to create a pull request, open a PR, or submit changes for review.
review-implement-phase
Implements triaged review actions, commits focused fixes, and posts Done plus resolves threads. Use when the user wants only the implementation phase of the review-framework workflow.
review-triage-phase
Produces canonical review actions from fetched review state and renders action markdown. Use when the user wants only triage/action-planning for the review-framework workflow.