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 magnus919/agent-skills --skill constrained-optimizationgit clone --depth 1 https://github.com/magnus919/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/magnus919/agent-skills/constrained-optimization)<a href="https://agentmods.dev/skills/magnus919/agent-skills/constrained-optimization"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/constrained-optimization/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/magnus919/agent-skills/constrained-optimization"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/constrained-optimization.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 | $0.00066 | $0.00761 |
| Opus 5.5 | $0.00026 | $0.00304 |
| Sonnet 5.5 | $0.00013 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
constrained-optimization 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 1mo 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Constrained optimization
Use this methodology to turn an optimization request into a defensible decision record.
Workflow
- Frame the decision. Name the decision maker, feasible action window, variable domains (continuous, integer, categorical, binary), bounds, units, and whether each variable is controllable.
- State objectives. Write objective direction, units, aggregation, baseline, and acceptable tradeoffs. Keep multiple objectives separate until a decision maker chooses weights, lexicographic priority, epsilon constraints, or a Pareto review.
- Classify constraints. Mark each as hard (must hold), soft (preference with an explicit penalty or relaxation), or informational. Define equality/inequality tolerance, feasibility predicate, and how violations are measured. Never hide a hard violation in a penalty score.
- Establish baselines. Validate a known feasible incumbent, a simple exact or bounded baseline where available, and a heuristic baseline when exact solving is impractical. State what each baseline proves and does not prove.
- Choose the method. Match solver family to domains, smoothness, scale, and proof needs. An exact solver can establish optimality or a bound when its status and gap support it; a heuristic supplies a candidate and empirical evidence. Do not call timeout or unknown an infeasible result.
- Run a fair comparison. Fix an objective/constraint implementation, feasibility checker, compute budget, stopping rule, and reporting schema. For stochastic methods, use equal budgets and declared seeds/repeats. Keep solver configuration separate from the formulation.
- Independently validate. Recompute objective and every constraint from the returned decision using an independent implementation or checker. Report feasible-best, violation magnitude, bound/gap, status, runtime, and unresolved unknowns separately.
- Decide and monitor. Select a feasible solution or Pareto set with the tradeoff rationale, sensitivity/robustness evidence, owner, and rollback/re-optimization trigger. Record “infeasible under these constraints” only when infeasibility is proven or independently certified.
What ships with it
7 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.
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.
- 1mo ago First seen · 37 lines · 66 tokens per session scan A 29871425d561
constrained-optimization is a skill published in the GitHub repository magnus919/agent-skills (133 stars, last pushed 2d ago), licensed MIT. It adds 66 tokens to every session and 761 once invoked, about $0.0003 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 2026-09-15.
Other skills, from other repositories
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CocoReview — structured code review with six-severity findings vocabulary, progressive disclosure architecture, and universal anti-pattern baseline. Invoked via $review [file] [--complexity] [--security] [--architecture] [--language ].
spec
Enter the Spec phase of CocoBrew. Guides the developer through structured requirements capture: goal, success criteria, constraints, personas involved, data sources, and deliverables. Writes spec.md to .cocoplus/lifecycle/ and creates a git commit.
discuss
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cocoharvest
Decompose an approved plan into parallel workstreams, assign specialist personas, classify stages as HITL or AFK (CocoLens), generate flow.json stages with checkpoints and dual-file state, and create per-stage prompt files. Includes adaptive parallelism, stall detection, shell identity injection, and consecutive…
cocoscout
Relevance-ranked context loading — Tier 2 async subagent (Haiku, <5s) that fires after Tier 1 deterministic checks in UserPromptSubmit. Injects ranked context from CocoGrove, CocoContext, Environment Inspector, Prompt Studio, and CocoDream.
bloom
Run a structured four-question working-backwards dialogue before $spec — commits the developer to the outcome (beneficiary, core capability, constraints, press release) before specification begins, anchoring all downstream phases to the original intent.