AI Video Atlas

Quality 88 / 100good

10 tools, about 3,724 tokens to load them all · tool set 2cc10211c120 · tools last changed 2026-10-12 · checked 2026-10-20 23:59 UTC

Tool changes

• 2026-10-12find_shotchangeddescription
  before

  Search AI Video Atlas's library of 107 sourced AI video shots (real prompts that creators posted, with the model used, an estimated cost and a link to the original). Use this FIRST when the user describes a shot, scene, style or job ("product ad with a rotating can", "anime fight", "lip sync dialogue", "drone over mountains"). Filters: shot_type (ads, music, shorts, characters, product, cinematic, lipsync, anime), model (e.g. kling, veo, seedance), max_cost_usd (estimated cost of the clip; when the prompt states no length we use a 5-second clip). Returns prompt excerpts; call best_prompt with a slug for the full prompt. Do not use for prices alone (use get_prices) or for tools/workflows (use get_workflow).

  after · 98% of words in common

  Search AI Video Atlas's library of 106 sourced AI video shots (real prompts that creators posted, with the model used, an estimated cost and a link to the original). Use this FIRST when the user describes a shot, scene, style or job ("product ad with a rotating can", "anime fight", "lip sync dialogue", "drone over mountains"). Filters: shot_type (ads, music, shorts, characters, product, cinematic, lipsync, anime), model (e.g. kling, veo, seedance), max_cost_usd (estimated cost of the clip; when the prompt states no length we use a 5-second clip). Returns prompt excerpts; call best_prompt with a slug for the full prompt. Do not use for prices alone (use get_prices) or for tools/workflows (use get_workflow).

• 2026-10-12fix_promptadded

  Alias of refine_shot. Fix a generated shot that came out wrong. Pass the prompt you used, the model, the mode and what went wrong in plain words ("feet sliding", "face drifted", "line cut off", "too glossy", "label morphing"), plus optional frame notes. Returns a diagnosis, specific prompt changes (from Atlas craft fixes and a per-model failure-mode library, credited), a revised prompt, and an escalation ladder: same-model fixes first (prompt, seed/variation, references, duration), then a mode change, then a model switch only as a last resort with the consistency cost stated. Adapt the revised prompt to the user's voice; no generation is run.

• 2026-10-12refine_shotadded

  Get to a usable take in as few further generations as possible when a shot came out wrong. Pass the prompt you used, the model, the mode and what went wrong in plain words ("feet sliding", "face drifted", "line cut off", "too glossy", "label morphing"), plus optional frame notes. Returns a diagnosis, specific prompt changes (from Atlas craft fixes and a per-model failure-mode library, credited), a revised prompt, and an escalation ladder: same-model fixes first (prompt, seed/variation, references, duration), then a mode change, then a model switch only as a last resort with the consistency cost stated. Adapt the revised prompt to the user's voice; no generation is run.

• 2026-10-12plan_shotschangeddescription
  before

  First review and rewrite the beat list (see brief_review): Atlas parses briefs with rules, so fix non-shots, missing subjects, placeholder lines and generic coverage before writing prompts. If the user names a model or mode, pass it in model/mode (it is kept for every shot unless the docs rule it out). Returns a GUIDANCE PACKAGE, not final prompts: you (the agent) write each full video prompt yourself from it. Per shot: model + mode decision with reasons, the per-model/per-mode prompt structure and tips, 2-3 credited proven example prompts, capabilities/limits/prices with sources, techniques and director rules (practitioner hints at low weight), required assets with image-gen guidance, an 11-section quality checklist, and a starter draft (starting point only, never paste it as-is). If the user names a model or mode, pass it in model/mode (or keep it verbatim in description): Atlas keeps it for every shot unless the vendor docs rule it out, and explains why in user_named. Write each final prompt as flowing, sectioned prose sentences (subject/action, camera/lens/movement, location/light/color, style, audio), never field labels like speaker_id: or Subject:, and without stock filler phrases. Also returns intake (questions to ask the user + stated assumptions) and package_markdown (readable). Plan a multi-shot AI video from a plain description (deterministic, built only from Atlas data; no AI generation). Output is staged: decide (model/mode/clip length/aspect/cost with reasons and capability checks), direct (shot list with per-model prompts), assets (every reference/frame with status needs-generation|user-supplied|approved), image_prompts, and steps, which include a STOP: get the user to approve assets before generating any video. Global directives (same X in every shot, vertical, no text, style) apply to all shots; questions and request wording are never planned as shots; an explicit shot count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.

  after · 98% of words in common

  Goal: a usable take on the first generation, so fewer paid generations per shot. First review and rewrite the beat list (see brief_review): Atlas parses briefs with rules, so fix non-shots, missing subjects, placeholder lines and generic coverage before writing prompts. If the user names a model or mode, pass it in model/mode (it is kept for every shot unless the docs rule it out). Returns a GUIDANCE PACKAGE, not final prompts: you (the agent) write each full video prompt yourself from it. Per shot: model + mode decision with reasons, the per-model/per-mode prompt structure and tips, 2-3 credited proven example prompts, capabilities/limits/prices with sources, techniques and director rules (practitioner hints at low weight), required assets with image-gen guidance, an 11-section quality checklist, and a starter draft (starting point only, never paste it as-is). If the user names a model or mode, pass it in model/mode (or keep it verbatim in description): Atlas keeps it for every shot unless the vendor docs rule it out, and explains why in user_named. Write each final prompt as flowing, sectioned prose sentences (subject/action, camera/lens/movement, location/light/color, style, audio), never field labels like speaker_id: or Subject:, and without stock filler phrases. Also returns intake (questions to ask the user + stated assumptions) and package_markdown (readable). Plan a multi-shot AI video from a plain description (deterministic, built only from Atlas data; no AI generation). Output is staged: decide (model/mode/clip length/aspect/cost with reasons and capability checks), direct (shot list with per-model prompts), assets (every reference/frame with status needs-generation|user-supplied|approved), image_prompts, and steps, which include a STOP: get the user to approve assets before generating any video. Global directives (same X in every shot, vertical, no text, style) apply to all shots; questions and request wording are never planned as shots; an explicit shot count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.

• 2026-10-12plan_shotschangeddescription, input schema
  before

  Plan a multi-shot AI video from a plain description (deterministic, built only from Atlas data; no AI generation). Output is staged: decide (model/mode/clip length/aspect/cost with reasons and capability checks), direct (shot list with per-model prompts), assets (every reference/frame with status needs-generation|user-supplied|approved), image_prompts, and steps, which include a STOP: get the user to approve assets before generating any video. Global directives (same X in every shot, vertical, no text, style) apply to all shots; questions and request wording are never planned as shots; an explicit shot count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot.

  after · 62% of words in common

  First review and rewrite the beat list (see brief_review): Atlas parses briefs with rules, so fix non-shots, missing subjects, placeholder lines and generic coverage before writing prompts. If the user names a model or mode, pass it in model/mode (it is kept for every shot unless the docs rule it out). Returns a GUIDANCE PACKAGE, not final prompts: you (the agent) write each full video prompt yourself from it. Per shot: model + mode decision with reasons, the per-model/per-mode prompt structure and tips, 2-3 credited proven example prompts, capabilities/limits/prices with sources, techniques and director rules (practitioner hints at low weight), required assets with image-gen guidance, an 11-section quality checklist, and a starter draft (starting point only, never paste it as-is). If the user names a model or mode, pass it in model/mode (or keep it verbatim in description): Atlas keeps it for every shot unless the vendor docs rule it out, and explains why in user_named. Write each final prompt as flowing, sectioned prose sentences (subject/action, camera/lens/movement, location/light/color, style, audio), never field labels like speaker_id: or Subject:, and without stock filler phrases. Also returns intake (questions to ask the user + stated assumptions) and package_markdown (readable). Plan a multi-shot AI video from a plain description (deterministic, built only from Atlas data; no AI generation). Output is staged: decide (model/mode/clip length/aspect/cost with reasons and capability checks), direct (shot list with per-model prompts), assets (every reference/frame with status needs-generation|user-supplied|approved), image_prompts, and steps, which include a STOP: get the user to approve assets before generating any video. Global directives (same X in every shot, vertical, no text, style) apply to all shots; questions and request wording are never planned as shots; an explicit shot count is honored; clips longer than an endpoint's documented max are split. The description is split into beats (sentences, clauses, and cues like "then", "cut to", "close-up", "wide"). For each beat it returns a shot type; a recommended model endpoint with its current vendor-stated $/s and source; the generation mode to use (t2v, i2v, reference-to-video, start/end frame, v2v, lip sync; only modes the vendor docs confirm) with a one-line reason; image prompts for any input frames, plus a shared character/product reference sheet when shots must match; an adapted prompt built from the best-matching sourced example (credited and linked), an estimated cost (5 s per shot by default), and a how-to workflow when one applies (lip sync, consistent character…). It also returns a total. budget_usd (total) and/or max_per_shot steer it to cheaper supported endpoints; each shot also offers a cheaper alternative with its cost, and the plan says if the budget can't be met. Use this for "plan / storyboard / make me a video of…" requests; use find_shot for a single shot. Before presenting, adapt the wording to the user's voice, context and brand (names, tone, product details), but keep the prices, models, modes, clip lengths and reference tags exactly as returned.

Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.