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 skills/openai/openai-agents-python/implementation-strategynpx skills add openai/openai-agents-python --skill implementation-strategygit clone --depth 1 https://github.com/openai/openai-agents-pythonWhat 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 | $0.00047 | $0.02577 |
| Opus 5 | $0.00023 | $0.01288 |
| Sonnet 5 | $0.00009 | $0.00515 |
| Haiku 4.5 | $0.00005 | $0.00258 |
Grade A, and why
implementation-strategy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Strategy
Workflow
- Identify the surface you are changing or reviewing: released public API, unreleased branch-local API, internal helper, persisted schema, wire protocol, CLI/config/env surface, or docs/examples only.
- Determine the latest release tag to use as the compatibility baseline from
originfirst, and only fall back to local tags when remote tags are unavailable:
Report a local-tag fallback as potentially stale.BASE_TAG="$(.agents/skills/final-release-review/scripts/find_latest_release_tag.sh origin 'v*' 2>/dev/null || git tag -l 'v*' --sort=-v:refname | head -n1)" echo "$BASE_TAG" - Record the implementation scope contract below before coding.
- Identify the nearest existing implementation pipeline and the functions, types, or modules that are the source of truth for each affected concern. Prefer adapting the required input into that pipeline over creating parallel schema, metadata, validation, naming, or execution machinery.
- Choose the smallest coherent change using the core decision rules. Add compatibility machinery only for a required supported boundary.
- Before editing each review-feedback batch, run the review gate against the complete branch diff, not only the latest revision.
- Before handoff, run the effectiveness check. If any answer is no, revise the design.
Implementation scope contract
Record these four items in the plan or working notes, and update them before widening or narrowing the implementation:
- Required behavior: The smallest user-visible scenario that must work.
- Compatibility requirements: Supported released behavior or a durable boundary that must remain usable.
- Intentionally unsupported cases: Nearby inputs or shapes to reject, including when and how rejection occurs.
- Supported alternative: An existing wrapper, override, adapter, configuration, or lower-level API; state
nonewhen absent.
If the intentionally unsupported cases cannot be stated clearly, do not start by adding a general resolver. First define a narrower behavior contract. If no adequate supported alternative exists, add one only when the task requires it; do not invent one speculatively.
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.
- yesterday First seen · 158 lines · 47 tokens per session scan A 751085add152
implementation-strategy is a skill published in the GitHub repository openai/openai-agents-python (29,075 stars, last pushed 4d ago), licensed MIT. It adds 47 tokens to every session and 2,577 once invoked, about $0.0002 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 2026-08-30.
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