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 divingsbysangam/salesforce-compound-engineering-plugin --skill sf-retunegit clone --depth 1 https://github.com/divingsbysangam/salesforce-compound-engineering-pluginWrote 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/divingsbysangam/salesforce-compound-engineering-plugin/sf-retune)<a href="https://agentmods.dev/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-retune"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-retune/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/divingsbysangam/salesforce-compound-engineering-plugin/sf-retune"><img src="https://agentmods.dev/badge/skills/divingsbysangam/salesforce-compound-engineering-plugin/sf-retune.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 | $0.00096 | $0.00896 |
| Opus 5 | $0.00048 | $0.00448 |
| Sonnet 5 | $0.00019 | $0.00179 |
| Haiku 4.5 | $0.00010 | $0.00090 |
Grade A, and why
sf-retune 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 9d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retune a Corpus for a New Model
A corpus that degrades on a new model is a measurement problem before it is a writing problem. Reading the prose and rewriting what looks wrong produces a plausible fix list and no way to know whether any item mattered.
Outcome: a corpus whose measured behavior on the target model clears a bar registered before any change, with the regression classes removed and each removal attributable.
Done: the bar is cleared, or the run reports the specific claim it could not support. A green test suite is not done: it proves nothing broke, not that behavior improved.
Non-goal: word reduction. Leanness and performance are separate programs that happen to share a corpus, and only one of them is the result. Report completion, not word count.
Boundary: this is not sf-update, which updates the installed plugin, or sf-compound-refresh, which reconciles stale repository knowledge. sf-retune measures behavior on a target model and changes the skill corpus only when the measurements support it.
Required reads
Procedure lives in sibling files, not only in this orchestrator:
- Phase 0: the measurement gate — check this first — read
references/phase-0-the-measurement-gate-check-this-first.mdbefore acting on this section. - Phase 1: mine the archive before spending a run — read
references/phase-1-mine-the-archive-before-spending-a-run.mdbefore acting on this section. - Phase 2: establish the noise floor before any claim — read
references/phase-2-establish-the-noise-floor-before-any-claim.mdbefore acting on this section. - Phase 3: audit the corpus, adversarially — read
references/phase-3-audit-the-corpus-adversarially.mdbefore acting on this section. - Phase 4: cut in surgical passes — read
references/phase-4-cut-in-surgical-passes.mdbefore acting on this section. - Phase 5: measure, then let the failure choose the next fix — read
references/phase-5-measure-then-let-the-failure-choose-the-next-fix.mdbefore acting on this section. - Phase 6: ship — read
references/phase-6-ship.mdbefore acting on this section. - Workflow shapes — read
references/workflow-shapes-2.mdbefore acting on this section.
What ships with it
15 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/baseline-mining.md 10 KB
- references/corpus-audit.md 12 KB
- references/cut-passes.md 8.8 KB
- references/halt-taxonomy.md 11 KB
- references/noise-floor.md 12 KB
- references/phase-0-the-measurement-gate-check-this-first.md 1.1 KB
- references/phase-1-mine-the-archive-before-spending-a-run.md 900 B
- references/phase-2-establish-the-noise-floor-before-any-claim.md 865 B
- references/phase-3-audit-the-corpus-adversarially.md 1.4 KB
- references/phase-4-cut-in-surgical-passes.md 1.3 KB
- references/phase-5-measure-then-let-the-failure-choose-the-next-fix.md 1.1 KB
- references/phase-6-ship.md 559 B
- references/workflow-shapes-2.md 572 B
- references/workflow-shapes.md 11 KB
- scripts/context.mjs 3.9 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.
- 9d ago First seen · 47 lines · 96 tokens per session scan A 937107ded342
sf-retune is a skill published in the GitHub repository divingsbysangam/salesforce-compound-engineering-plugin (10 stars, last pushed 5d ago), licensed MIT. It adds 96 tokens to every session and 896 once invoked, about $0.0005 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.
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