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/ahepi/deepreason/deepreason-orchestratornpx skills add AHepi/DeepReason --skill deepreason-orchestratorgit clone --depth 1 https://github.com/AHepi/DeepReasonWrote 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/ahepi/deepreason/deepreason-orchestrator)<a href="https://agentmods.dev/skills/ahepi/deepreason/deepreason-orchestrator"><img src="https://agentmods.dev/badge/skills/ahepi/deepreason/deepreason-orchestrator.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.01107 |
| Opus 5 | $0.00023 | $0.00553 |
| Sonnet 5 | $0.00009 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
deepreason-orchestrator 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 today.
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepReason problem orchestrator
You are running a tightly bounded workflow. You do not freelance. You select ONE subskill, execute it to its exit criteria, then return here to select the next. You never blend phases.
The scope contract (read before every phase)
- One tranche = one goal. A tranche works exactly one GOAL.md
(produced by
dr-set-goal). Anything else you notice goes intoPARKED.md— never into your work. Write the parked entry for its FUTURE RUNNER, at park time, while the context is free: one line of WHAT, then a ready-to-send prompt (route, one-goal statement, evidence pointers, end state). Starting the follow-up should cost the operator a paste, not an authoring session. - Evidence over prose. Claims about DeepReason behavior are only
admissible if derived from typed records:
log.jsonl,objects/,progress.jsonl,run-status.json,REPLAY_VALIDATION.json,verify_root, or test output. Your own summary of what code "probably does" is not evidence. - No phase-skipping. You may not implement without an approved FIX.md. You may not write FIX.md without a DIAGNOSIS.md. You may not write DIAGNOSIS.md without a reproduction or record-derived trace.
- Stop conditions. Stop and report (do not improvise) when: a
command fails twice the same way; evidence contradicts the goal;
the fix requires touching frozen-record semantics (anything under
capabilities/state.pydigests,harness.pyevent application, or replay validation record formats); or the diff would exceed ~150 changed lines. Before any stop becomes a question to the operator, loaddr-ask-the-right-question: route it to the cheapest authority first, and ask only what survives the dominance test — batched, with a recommendation.
Map and environment preflight (do this before routing, every time)
Full procedure, canonical: dr-drive-harness §1 (session/environment
preflight — branch resync, deepreason importable, credential check)
and §4 (map preflight — docs/map/INDEX.md → INV-frozen-surfaces.md
→ seam document → record the resolved ids in GOAL.md). Load it if this
session has not run the harness before. Also load
pinker-write-for-readers once per session, BEFORE your first message
the operator will see (it replaced dr-explain-to-operator on
2026-09-03; CLAUDE.md Conventions state what carries over).
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.
- today Changed · +1 lines b0f863156396
- 4d ago First seen · 89 lines · 46 tokens per session scan A 5985bf37b0b4
deepreason-orchestrator is a skill published in the GitHub repository AHepi/DeepReason (141 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,107 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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