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 skills add 0xhughs/director-skills --skill prompt-iteration-and-diagnosticsgit clone --depth 1 https://github.com/0xhughs/director-skillsWrote 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/0xhughs/director-skills/prompt-iteration-and-diagnostics)<a href="https://agentmods.dev/skills/0xhughs/director-skills/prompt-iteration-and-diagnostics"><img src="https://agentmods.dev/badge/skills/0xhughs/director-skills/prompt-iteration-and-diagnostics/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/skills/0xhughs/director-skills/prompt-iteration-and-diagnostics"><img src="https://agentmods.dev/badge/skills/0xhughs/director-skills/prompt-iteration-and-diagnostics.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 | $0.00034 | $0.00731 |
| Opus 5 | $0.00017 | $0.00365 |
| Sonnet 5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
prompt-iteration-and-diagnostics 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 12d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
prompt-iteration-and-diagnostics
When to use
- The user shows or describes a failed output and asks how to fix it.
- The user wants prompt diagnostics, iteration, A/B variants, failure analysis, or a revision report.
- The output has drift, bad anatomy, wrong motion, weak realism, camera chaos, text/logo errors, prompt collapse, or model transfer failure.
- The prose, dialogue, or film package has generic AI voice, weak story logic, rights/provenance gaps, or release-quality concerns.
When not to use
- The user has no output or failure description and only needs first-draft prompting.
- The issue is a tool outage or account/billing problem.
- The user asks for unsafe bypass instructions.
Required inputs
- Original prompt
- Observed output or failure description
- Target model/tool
- Desired result
Optional inputs
- Reference images
- Settings/parameters
- Seed/version
- Previous attempts
- Continuity bible
Workflow
- Restate the intended result and the actual failure.
- Classify failure: prompt ambiguity, contradiction, overload, model limitation, continuity drift, temporal overload, reference conflict, safety/policy rejection, or parameter mismatch.
- Identify the smallest change likely to improve the result.
- Apply the one-variable rule for iterative tests unless the prompt is fundamentally broken.
- Rewrite using the relevant skill: image, video, continuity, style, or model-adaptation.
- Produce a revision report with diagnosis, changed fields, unchanged anchors, expected improvement, and next test.
- When the failure is a model limitation, redesign the shot or route to a better model instead of forcing the same prompt.
Decision logic
- If output is 80 percent correct, make minimal edits.
- If identity drift appears, strengthen references and bible anchors.
- If motion fails, reduce actions and camera moves.
- If text/logos fail, choose a text-capable model or simplify typography.
- If a prompt is rejected, remove unsafe/IP-sensitive content and do not provide bypass tactics.
What ships with it
12 files 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.
- checklists/diagnostic_checklist.md 255 B
- checklists/release_quality_checklist.md 582 B
- examples/failed_output_to_revised_prompt.md 682 B
- examples/final_prompt_package_export.md 342 B
- references/failure_modes.md 870 B
- references/iteration_diagnostics.md 723 B
- references/revision_loops.md 510 B
- references/story_voice_release_qc.md 1.3 KB
- templates/failed_output_intake.md 241 B
- templates/final_production_package.md 216 B
- templates/prompt_revision_report.md 269 B
- tests/prompt_quality_checklist_tests.md 244 B
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.
- 12d ago First seen · 83 lines · 34 tokens per session scan A bbbb34552d72
prompt-iteration-and-diagnostics is a skill published in the GitHub repository 0xhughs/director-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 731 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 2026-08-31.
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