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 commands/arslan70/haytham/scoutgit clone --depth 1 https://github.com/arslan70/haythamWrote 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/commands/arslan70/haytham/scout)<a href="https://agentmods.dev/commands/arslan70/haytham/scout"><img src="https://agentmods.dev/badge/commands/arslan70/haytham/scout.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 | $0.00033 | $0.01669 |
| Opus 5 | $0.00016 | $0.00834 |
| Sonnet 5 | $0.00007 | $0.00334 |
| Haiku 4.5 | $0.00003 | $0.00167 |
Grade A, and why
scout 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 4d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Haytham: Daily Scout (autonomous)
You are running the autonomous scout pipeline headless. There is no human to ask; never stop for clarification. Deterministic decisions (score floor, dedup, winner selection) belong to scripts — you orchestrate, you do not decide.
IMPORTANT: Always read agent output from files, not from conversation history.
Hard rules
- Never restart or re-run the pipeline because a verdict is PIVOT or NO-GO. One pass per day; the verdict is the result.
- Maximum ONE deep dive per run.
- Never edit the ledger or selection files —
scout_select.pyowns them. - If a stage fails or produces nothing, stop after recording what happened; the report script ships an honest-zero report from whatever artifacts exist. Shipping nothing is the only failure mode.
Setup
RUN_DIR = $ARGUMENTS (a path relative to the working directory).
- Verify
RUN_DIR/harvest/telemetry.jsonexists. If not, writeRUN_DIR/scout-status.jsonwith{"failed_at": "setup", "reason": "no harvest"}and stop. - Read PERSONA from
config/persona.yamlif it exists; otherwise use: "Solo technical founder building with AI tooling; wants ideas testable as a 1-2 week solo MVP; avoids heavy ops, sales-led GTM, regulated domains." - Call TodoWrite once with the steps below.
Step 1 — Extract
Replay guard: if RUN_DIR/candidates/candidates.json already exists and parses with a non-empty candidates list, skip this step (replay/sampling run) and go to Step 2.
Launch the idea-scout agent. Tell it the RUN_DIR and give it PERSONA verbatim. It writes RUN_DIR/candidates/candidates.json.
Verify the file exists and parses. If it is missing or candidates is empty, write RUN_DIR/scout-status.json {"failed_at": "extract", "reason": "<what happened>"} and stop.
Step 2 — Screen
Replay guard: if RUN_DIR/screen/screening.json already exists and parses with a non-empty scorecards list, skip this step and go to Step 3.
Launch the feasibility-screener agent. Tell it the RUN_DIR and give it PERSONA verbatim. It writes RUN_DIR/screen/screening.json and screening.md.
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.
- 4d ago First seen · 90 lines · 33 tokens per session scan A f84bab44b4b7
scout is a command published in the GitHub repository arslan70/haytham (13 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,669 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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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.