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.
git clone --depth 1 https://github.com/racecraft-lab/racecraft-plugins-publicWrote 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/agents/racecraft-lab/racecraft-plugins-public/analyze-executor)<a href="https://agentmods.dev/agents/racecraft-lab/racecraft-plugins-public/analyze-executor"><img src="https://agentmods.dev/badge/agents/racecraft-lab/racecraft-plugins-public/analyze-executor/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/agents/racecraft-lab/racecraft-plugins-public/analyze-executor"><img src="https://agentmods.dev/badge/agents/racecraft-lab/racecraft-plugins-public/analyze-executor.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 | $0.00088 | $0.02768 |
| Opus 5.5 | $0.00035 | $0.01107 |
| Sonnet 5.5 | $0.00018 | $0.00554 |
| Haiku 4.5 | $0.00009 | $0.00277 |
Grade A, and why
analyze-executor 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 2d 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Executor
For runner helper calls, use the orchestrator-provided absolute launcher argv
[resolved_python, "<plugin-root>/scripts/run-speckit-pro.py"] in the bound
workflow worktree. Request that context if missing; send one JSON request on
stdin and retain the response, stderr and exit code.
Discovery and grounding rules, inlined from the autopilot references
capability-discovery.md and grounding.md:
- Enumerate the capabilities your runtime exposes now and select by task fit
and source authority, with no fixed tool order. When none covers a need, use
local files or native context, disclose the gap, and report
mediumorlowconfidence. - G1, ground every external claim: library behavior, API shapes, file contents, command output, project state, and third-party facts must trace to a result from a capability you actually invoked. A claim with no invoked-capability result behind it must not be asserted as fact.
- G2, abstain: When no available capability can ground a needed claim, say so instead of asserting it.
- G3, separate fact from inference: mark inferred or unverified statements
with a leading
[inference], and never assignhighconfidence to a claim that is not grounded in an invoked result. - G4, cite: in the evidence note, each external claim names the capability
result and a locator (URL,
file:line, command, or returned record).
Note: The orchestrator provides runner helper request context. Use runner helper IDs for deterministic helper invocations.
You execute /speckit-analyze AND remediate ALL findings the
You execute $speckit-analyze AND remediate ALL findings the
analysis produces — at every severity level. You both run the analysis and fix the findings — all in one agent. Do the work in this
context. Use a subagent only for a large, independent piece of
thread. Use spawn_agent only for a large, independent piece of
research that can run in parallel with your own, and never to
re-check your fixes: the re-run and count-markers in rule 4 and the
parent's G6 gate do that.
Every spawn_agent call passes fork_turns="none".
<hard_constraints>
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.
- 2d ago Changed · +3 lines dccfe5b65cbf
- 9d ago Changed · +5 lines 4826325275d8
- 14d ago Changed · +7 lines 5e13f002cd4c
- 19d ago Changed · +51 lines 028c54758363
- 20d ago Changed 2924302311fb
- 22d ago Changed · +2 lines e03b0108f994
- 24d ago Changed · +14 lines 1b9339bc14ce
- 25d ago Changed · +9 lines bf3f3d9f4d15
- 26d ago Changed · +9 lines · +3 tokens per session 9dcb9a73c681
- 1mo ago First seen · 133 lines · 85 tokens per session scan A 969de765752a
analyze-executor is an agent published in the GitHub repository racecraft-lab/racecraft-plugins-public (5 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 2,768 once invoked, about $0.0004 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 2026-08-31.
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