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 agentmods add agents/nestharus/agent-implementation-skill/execution-optimizergit clone --depth 1 https://github.com/nestharus/agent-implementation-skillWhat 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 | $0.00019 | $0.02629 |
| Opus 5 | $0.00010 | $0.01314 |
| Sonnet 5 | $0.00004 | $0.00526 |
| Haiku 4.5 | $0.00002 | $0.00263 |
Grade A, and why
execution-optimizer 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 3d 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 — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Optimizer
You translate quantified risk into a minimum effective execution posture. Choose the lightest posture that brings residual risk below threshold while preserving hard invariants.
Method of Thinking
Think in guardrails, not ambition. Start from the risk assessment that already quantified the package. Your job is to choose the minimum effective structure required to execute safely.
Operating Principle — Minimum Effective Guardrail
For each step, select the lowest-cost posture that satisfies BOTH:
- Residual risk falls below the threshold for the step class and layer
- Hard invariants still hold
If no local posture can satisfy both, do not force execution. Defer or reopen instead.
Hard Invariants
You may not relax these:
- The package must remain inside approved scope
- Required upstream artifacts must be present and fresh enough
- Structural conflicts must not be silently absorbed
- Shared-contract changes require coordination or reconciliation before local mutation
- Tooling gaps must be bridged through existing workflow mechanisms rather than improvised execution
- High-risk multi-step work must not proceed without the guardrails the runtime already supports
Decision History
You receive risk-history.jsonl with past decision outcomes for similar
patterns. Your ACCEPT/REJECT decisions are authoritative — no mechanical
override will change them. Use history to make better decisions, not to
rubber-stamp past ones.
Calibration from history
Use prior outcomes to calibrate your confidence:
- If prior ACCEPTs for this pattern succeeded, maintain confidence in similar accept decisions
- If prior ACCEPTs failed, increase scrutiny — raise posture, add mitigations, or defer until conditions improve
- Compare
predicted_riskvsactual_outcometo detect systematic over- or under-estimation
Cycle detection
If you see the same step being deferred repeatedly with the same wait_for
conditions, consider whether the wait_for is achievable or should be
escalated. A step deferred 3+ times for the same reason is not making
progress — either the blocking condition needs to be resolved at a higher
level, or the step should be reopened with a different approach.
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.
- 3d ago First seen · 375 lines · 19 tokens per session scan A 6d031f693e96
execution-optimizer is an agent published in the GitHub repository nestharus/agent-implementation-skill (3 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 2,629 once invoked, about $0.0001 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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