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 fgmacedo/agent-skills --skill orchestrating-large-changesgit clone --depth 1 https://github.com/fgmacedo/agent-skillsWrote 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/fgmacedo/agent-skills/orchestrating-large-changes)<a href="https://agentmods.dev/skills/fgmacedo/agent-skills/orchestrating-large-changes"><img src="https://agentmods.dev/badge/skills/fgmacedo/agent-skills/orchestrating-large-changes/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/fgmacedo/agent-skills/orchestrating-large-changes"><img src="https://agentmods.dev/badge/skills/fgmacedo/agent-skills/orchestrating-large-changes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00211 | $0.01916 |
| Opus 5.5 | $0.00084 | $0.00766 |
| Sonnet 5.5 | $0.00042 | $0.00383 |
| Haiku 4.5 | $0.00021 | $0.00192 |
Grade A, and why
orchestrating-large-changes 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 6d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrating large changes
One agent plans, delegates, integrates, accepts live and reports a change too large for one context. The user decides every risk once, after the plan; then the run is unattended and the orchestrator rules on everything else, logging each ruling. Contracts live in code: a skeleton task commits them compiling before the fan-out; prose keeps only intent.
Measured on a 33-task run: reviews paid (80% of Critical and Important findings were real bugs). The waste: full suites in every lane under parallel load (18%+ of implementer time, flaky timeouts; integration gates caught 0 regressions), 227 contract deltas folded by hand into markdown beside a compiled skeleton, 160 of 556 orchestrator turns writing ledger lines.
Done when all of these hold:
- Every acceptance item is proven by a command whose output is in the ledger.
- The integration branch holds all the work, merged and green; no task stub is left.
- Nothing the run created is left; every resource of the user is where it was; backups unchanged.
- The product is in the state the handoff defines; the report is written, the user told.
Files of a run
| File | Where | Who reads it |
|---|---|---|
plan/: criteria, core.md, tasks/<id>-<slug>.md; the handoff |
run directory | everyone; a task file, its delegates |
| Contract skeleton: types, fakes, conformance tests, fixtures, test stubs | the code, from T0 | delegates, through the API docs |
implementer.md, reviewer.md |
run directory | delegates |
ledger.md, reports/<id>.md, reviews/<id>.md, the final report |
run directory | orchestrator; fixers read their review |
The run directory sits outside every worktree and is never committed: specs, plans,
ledger and reports are ephemeral. The repository receives code, tests and the user-facing
docs the change itself needs, nothing else (no docs/superpowers/, no spec folder). Go
idioms: references/go.md.
Composing with other skills
With superpowers: brainstorming and writing-plans in the format of references/plan.md,
saved to the run directory, never their default repository path;
subagent-driven-development with the overrides in references/delegation.md;
systematic-debugging; finishing-a-development-branch after the report. Several
repositories: references/delegation-tools.md. Without subagents: the same plan inline,
one task at a time, same ledger, reviews by a second pass.
What ships with it
10 files 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.
- references/delegation-tools.md 3.2 KB
- references/delegation.md 7.1 KB
- references/go.md 1.7 KB
- references/handoff.md 2.6 KB
- references/ledger.md 4.1 KB
- references/live-acceptance.md 1.8 KB
- references/plan.md 5.4 KB
- references/report.md 1.3 KB
- references/risk-questions.md 3.2 KB
- scripts/ledger.py 17 KB runs code
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.
- 6d ago Changed · -7 lines · -11 tokens per session 33083163af78
- 7d ago Changed · -46 lines · +9 tokens per session 07139fc64e90
- 11d ago First seen · 175 lines · 213 tokens per session scan A 4e1f4f51f63d
orchestrating-large-changes is a skill published in the GitHub repository fgmacedo/agent-skills (10 stars, last pushed 6d ago), licensed MIT. It adds 211 tokens to every session and 1,916 once invoked, about $0.0008 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 2026-10-19.
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