task_id stringlengths 9 20 | domain stringclasses 8
values | family stringlengths 6 17 | level int64 1 3 | budget_calls int64 6 20 | example_question stringlengths 49 589 | answer_format stringlengths 8 118 | files listlengths 0 7 | deliverable stringlengths 14 23 ⌀ | style stringclasses 2
values | new_in_v4 bool 2
classes | in_training_list bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|
change-impact-L1 | software-engineering | change-impact | 1 | 8 | Which modules must be rebuilt, and in what order? | a JSON object {"rebuild": ["core", "web"], "build_order": ["core", "web"]} | [
"BUILD.md",
"change.txt",
"modules.json"
] | null | answer | false | true |
change-impact-L2 | software-engineering | change-impact | 2 | 8 | Which modules must be rebuilt, and in what order? | a JSON object {"rebuild": ["core", "web"], "build_order": ["core", "web"]} | [
"BUILD.md",
"change.txt",
"modules.json"
] | null | answer | false | true |
version-resolve-L1 | software-engineering | version-resolve | 1 | 8 | Resolve one version for each package in requirements.txt using registry.json. | a JSON object mapping every package to a version string, e.g. {"flask": "2.3.1", "axios": "none"} | [
"RESOLVER.md",
"registry.json",
"requirements.txt"
] | null | answer | false | true |
version-resolve-L2 | software-engineering | version-resolve | 2 | 8 | Resolve one version for each package in requirements.txt using registry.json. | a JSON object mapping every package to a version string, e.g. {"flask": "2.3.1", "axios": "none"} | [
"RESOLVER.md",
"advisory.txt",
"registry.json",
"requirements.txt"
] | null | answer | false | true |
module-repair-L3 | software-engineering | module-repair | 3 | 12 | utils.py has 3 bugs (one per function at most). test_utils.py fails. Fix utils.py so every function matches its docstring. Hidden tests check every function against its docstring. The visible tests do not cover every bug. Submit the complete corrected contents of utils.py as the answer string. | a string holding the whole fixed file, with newlines escaped as \n inside the JSON string | [
"test_utils.py",
"utils.py"
] | null | answer | false | true |
bom-explosion-L1 | industrial-physical-systems | bom-explosion | 1 | 8 | Explode the work order through bom.csv. How many units of each raw material are required? | a JSON object mapping each raw material id to an integer, e.g. {"RM-12": 340, "RM-40": 95} | [
"bom.csv",
"work_order.txt"
] | null | answer | false | true |
bom-explosion-L2 | industrial-physical-systems | bom-explosion | 2 | 8 | Explode the work order through bom.csv. How many units of each raw material are required to buy? | a JSON object mapping each raw material id to an integer, e.g. {"RM-12": 340, "RM-40": 95} | [
"bom.csv",
"stock.csv",
"work_order.txt"
] | null | answer | false | true |
tank-balance-L1 | industrial-physical-systems | tank-balance | 1 | 8 | What is the tank level in litres at 11:35, and at what time (HH:MM, rounded up to the whole minute) does the level first reach the alarm level? | a JSON object {"final_level_l": 7350, "first_alarm": "08:47"} | [
"flows.csv",
"tank.txt"
] | null | answer | false | true |
tank-balance-L2 | industrial-physical-systems | tank-balance | 2 | 8 | What is the tank level in litres at 10:40, and at what time (HH:MM, rounded up to the whole minute) does the level first reach the alarm level? | a JSON object {"final_level_l": 7350, "first_alarm": "08:47"} | [
"flows.csv",
"shift_log.txt",
"tank.txt"
] | null | answer | false | true |
line-oee-L2 | industrial-physical-systems | line-oee | 2 | 10 | What was the OEE of machine M58 this shift, in percent? Round to 1 decimal. | a number such as 61.2 | [
"notes.txt",
"production.csv",
"states.csv"
] | null | answer | false | false |
line-oee-L3 | industrial-physical-systems | line-oee | 3 | 12 | Which machine had the lowest OEE this shift, and what was it? Answer as the machine id, a space, and the OEE percent rounded to 1 decimal. | a string such as "M17 54.3" | [
"notes.txt",
"production.csv",
"states.csv"
] | null | answer | false | true |
beer-lambert-L1 | natural-science | beer-lambert | 1 | 10 | Using the calibration standards, find the fitted slope (AU per uM) and the original concentration of sample U3 in uM. | a JSON object {"slope": 0.012345, "concentration_uM": 41.27} (within 0.5%) | [
"notebook.txt",
"samples.csv",
"standards.csv"
] | null | answer | false | false |
beer-lambert-L2 | natural-science | beer-lambert | 2 | 10 | Using the calibration standards, find the fitted slope (AU per uM) and the original concentration of sample U5 in uM. | a JSON object {"slope": 0.012345, "concentration_uM": 41.27} (within 0.5%) | [
"notebook.txt",
"samples.csv",
"standards.csv"
] | null | answer | false | false |
stoichiometry-L1 | natural-science | stoichiometry | 1 | 8 | Which reactant limits the batch, and what mass of Na2SO4 do you expect in theory? Use atomic_masses.txt for molar masses. | a JSON object {"limiting": "<formula>", "theoretical_g": 12.34} (mass in grams, within 0.5%) | [
"atomic_masses.txt",
"batch.txt"
] | null | answer | false | false |
stoichiometry-L2 | natural-science | stoichiometry | 2 | 8 | Which reactant limits the batch, and what mass of Na2SO4 do you expect to isolate? Use atomic_masses.txt for molar masses. | a JSON object {"limiting": "<formula>", "expected_g": 12.34} (mass in grams, within 0.5%) | [
"atomic_masses.txt",
"batch.txt"
] | null | answer | false | false |
kinetics-lab-L3 | natural-science | kinetics-lab | 3 | 12 | From run.csv and protocol.txt: what is the reaction order and the half-life in minutes? Answer as the order, a space, and the half-life to 4 significant figures. | a string such as "1 23.45" | [
"protocol.txt",
"run.csv"
] | null | answer | false | false |
meeting-slot-L1 | office-white-collar | meeting-slot | 1 | 8 | Find the earliest start time for the call, in UTC (HH:MM). | a JSON object {"start_utc": "13:15"} | [
"calendars.csv",
"request.txt"
] | null | answer | false | false |
meeting-slot-L2 | office-white-collar | meeting-slot | 2 | 8 | Find the earliest start time for the call, in UTC (HH:MM). | a JSON object {"start_utc": "13:15"} | [
"calendars.csv",
"inbox.txt",
"request.txt"
] | null | answer | false | true |
invoice-match-L1 | office-white-collar | invoice-match | 1 | 8 | Which invoices can be approved under ap_policy.txt, and what is the total payable for them? | a JSON object {"approved": ["INV-12345"], "total": 1234.56} | [
"ap_policy.txt",
"invoices.csv",
"purchase_orders.csv",
"receipts.csv"
] | null | answer | false | true |
invoice-match-L2 | office-white-collar | invoice-match | 2 | 8 | Which invoices can be approved under ap_policy.txt, and what is the total payable for them? | a JSON object {"approved": ["INV-12345"], "total": 1234.56} | [
"ap_policy.txt",
"credit_notes.csv",
"invoices.csv",
"purchase_orders.csv",
"receipts.csv"
] | null | answer | false | true |
expense-policy-L3 | office-white-collar | expense-policy | 3 | 12 | Using expenses.csv and policy.md, how much should Lena Weiss be reimbursed in USD for 2026-04? Round to cents. | a number such as 812.40 | [
"expenses.csv",
"policy.md",
"rates.txt"
] | null | answer | false | true |
npv-choice-L1 | finance-economics | npv-choice | 1 | 8 | Which project has the highest net present value, and what is that NPV in USD? | a JSON object {"best_project": "P00", "npv": 123456.78} (NPV within 1 USD) | [
"memo.txt",
"projects.csv"
] | null | answer | false | false |
npv-choice-L2 | finance-economics | npv-choice | 2 | 8 | Which project has the highest net present value, and what is that NPV in USD? | a JSON object {"best_project": "P00", "npv": 123456.78} (NPV within 1 USD) | [
"memo.txt",
"projects.csv"
] | null | answer | false | true |
bond-desk-L1 | finance-economics | bond-desk | 1 | 8 | What is the price of bond BD323 per its full face value? | a JSON object {"price": 101.2345} (within 0.01 per 100 face) | [
"bonds.csv",
"conventions.txt"
] | null | answer | false | false |
bond-desk-L2 | finance-economics | bond-desk | 2 | 8 | For bond BD323, give its price per full face value and its modified duration in years. | a JSON object {"price": 101.2345, "modified_duration": 4.5678} (price within 0.01 per 100 face, duration within 0.005) | [
"bonds.csv",
"conventions.txt"
] | null | answer | false | true |
fifo-basis-L1 | finance-economics | fifo-basis | 1 | 8 | For NOVA only: what is the total realized gain (USD) and how many shares are still held? | a JSON object {"realized_gain": -123.45, "shares_held": 40} (gain within 0.02) | [
"policy.txt",
"trades.csv"
] | null | answer | false | false |
fifo-basis-L2 | finance-economics | fifo-basis | 2 | 8 | For KORA only: what is the total realized gain (USD) and how many shares are still held? | a JSON object {"realized_gain": -123.45, "shares_held": 40} (gain within 0.02) | [
"policy.txt",
"trades.csv"
] | null | answer | false | true |
loan-desk-L3 | finance-economics | loan-desk | 3 | 12 | Use loans.csv and rules.txt. With the extra payments, how much total interest is paid on loan LN-4578 until it is fully repaid? Dollars, rounded to cents. | a number such as 12345.67 | [
"loans.csv",
"rules.txt"
] | null | answer | false | true |
congruences-L1 | mathematics-or-formal-reasoning | congruences | 1 | 6 | system.txt lists a system of congruences. Give the smallest non-negative solution x and the modulus M such that the solutions are exactly x + k*M. | a JSON object {"x": 1234, "modulus": 5040} with integers | [
"system.txt"
] | null | answer | false | false |
congruences-L2 | mathematics-or-formal-reasoning | congruences | 2 | 6 | system.txt lists a system of congruences. Give the smallest non-negative solution x and the modulus M such that the solutions are exactly x + k*M, and the count it asks for. | a JSON object {"x": 1234, "modulus": 5040, "count_in_range": 7} with integers | [
"system.txt"
] | null | answer | false | false |
logic-grid-L1 | mathematics-or-formal-reasoning | logic-grid | 1 | 8 | Solve the puzzle in puzzle.txt. Give each person's city. | a JSON object mapping every person to a value, e.g. {"Goran": "Quito", ...} | [
"puzzle.txt"
] | null | answer | false | false |
logic-grid-L2 | mathematics-or-formal-reasoning | logic-grid | 2 | 8 | Solve the puzzle in puzzle.txt. Give each person's pet. | a JSON object mapping every person to a value, e.g. {"Goran": "cat", ...} | [
"puzzle.txt"
] | null | answer | false | false |
shortest-route-L1 | mathematics-or-formal-reasoning | shortest-route | 1 | 8 | Roads in roads.csv are two-way. Find the fastest route from J to G. Give the total minutes and the route as a list of nodes, start and end included. | a JSON object {"minutes": 37, "route": ["A", "D", "C"]} | [
"roads.csv"
] | null | answer | false | false |
shortest-route-L2 | mathematics-or-formal-reasoning | shortest-route | 2 | 8 | Roads in roads.csv are two-way. Find the fastest route from D to E that passes through I, avoiding closed roads (see notice.txt). Give the total minutes and the route as a list of nodes, start and end included. | a JSON object {"minutes": 37, "route": ["A", "D", "C"]} | [
"notice.txt",
"roads.csv"
] | null | answer | false | false |
exact-probability-L1 | mathematics-or-formal-reasoning | exact-probability | 1 | 6 | Answer the question in problem.txt as an exact reduced fraction. | a JSON object {"probability": "a/b"} with the fraction in lowest terms | [
"problem.txt"
] | null | answer | false | false |
exact-probability-L2 | mathematics-or-formal-reasoning | exact-probability | 2 | 6 | Answer the question in problem.txt as an exact reduced fraction. | a JSON object {"probability": "a/b"} with the fraction in lowest terms | [
"problem.txt"
] | null | answer | false | false |
exact-count-L3 | mathematics-or-formal-reasoning | exact-count | 3 | 8 | Count the 49-digit positive integers (no leading zero) whose digits sum to 292 and that never use the digit 6. Give the count modulo 1000000007. | an integer | [] | null | answer | false | false |
firewall-audit-L1 | cybersecurity | firewall-audit | 1 | 8 | Which packets in packets.csv does the live firewall allow? | a JSON object {"allowed": ["P01", "P07"]} listing every allowed packet id | [
"packets.csv",
"rules.txt"
] | null | answer | false | false |
firewall-audit-L2 | cybersecurity | firewall-audit | 2 | 8 | Which packets in packets.csv does the live firewall allow? Apply change_log.txt to rules.txt first. | a JSON object {"allowed": ["P01", "P07"]} listing every allowed packet id | [
"change_log.txt",
"packets.csv",
"rules.txt"
] | null | answer | false | false |
vuln-triage-L1 | cybersecurity | vuln-triage | 1 | 8 | Versions are semantic (major.minor.patch, compared numerically). Which hosts run at least one package version covered by an advisory? Also give how many hosts that is. | a JSON object {"hosts": ["app-03", "web-01"], "count": 2} | [
"advisories.json",
"inventory.csv"
] | null | answer | false | false |
vuln-triage-L2 | cybersecurity | vuln-triage | 2 | 8 | Versions are semantic (major.minor.patch, compared numerically). Which hosts run at least one package version covered by an advisory? Also give how many hosts that is. | a JSON object {"hosts": ["app-03", "web-01"], "count": 2} | [
"advisories.json",
"inventory.csv",
"vendor_notes.txt"
] | null | answer | false | false |
auth-hunt-L3 | cybersecurity | auth-hunt | 3 | 12 | An attacker rotated source addresses inside one /24 network. Treat all IPs in the same /24 as one source. Find the /24 with at least 5 failed passwords for one user within any 10-minute span, followed within 10 minutes of its last failure by an accepted login for that same user from the same /24. Answer in CIDR form, l... | a string | [
"auth.log"
] | null | answer | false | false |
edit-timeline-L1 | media-content-production | edit-timeline | 1 | 8 | Clips play back to back in the listed order. What is the total program length in frames, and as a timecode HH:MM:SS:FF at the timeline frame rate? | a JSON object {"frames": 4321, "timecode": "00:02:52:21"} | [
"timeline.edl"
] | null | answer | false | false |
edit-timeline-L2 | media-content-production | edit-timeline | 2 | 8 | After applying review_notes.txt: clips play back to back in the listed order. What is the total program length in frames, and as a timecode HH:MM:SS:FF at the timeline frame rate? | a JSON object {"frames": 4321, "timecode": "00:02:52:21"} | [
"review_notes.txt",
"timeline.edl"
] | null | answer | false | false |
headline-style-L1 | media-content-production | headline-style | 1 | 8 | Which headlines in headlines.tsv break the house style guide? Read every page of the guide. | a JSON object {"violations": ["H02", "H09"]} listing every breaking headline id | [
"headlines.tsv",
"style_guide_p1.txt"
] | null | answer | false | false |
headline-style-L2 | media-content-production | headline-style | 2 | 8 | Which headlines in headlines.tsv break the house style guide? Read every page of the guide. | a JSON object {"violations": ["H02", "H09"]} listing every breaking headline id | [
"headlines.tsv",
"style_guide_p1.txt",
"style_guide_p2.txt"
] | null | answer | false | false |
subtitle-qc-L3 | media-content-production | subtitle-qc | 3 | 12 | Check episode.srt against style.txt. Which cue numbers break the reading-speed rule? Answer with the cue numbers in increasing order, separated by commas (empty string if none). | a string such as "3,8,15" | [
"episode.srt",
"style.txt"
] | null | answer | false | false |
assay-reduction-L1 | natural-science | assay-reduction | 1 | 16 | Reduce the protein assay in plate.csv following protocol.md and write output/results.csv with columns sample_id,conc_mg_per_L (one row per sample, original undiluted concentration). | output/results.csv | [
"plate.csv",
"protocol.md"
] | output/results.csv | file | true | true |
assay-reduction-L2 | natural-science | assay-reduction | 2 | 20 | Reduce the protein assay in plate.csv following every step of protocol.md and write output/results.csv with columns sample_id,conc_mg_per_L,status (one row per sample). | output/results.csv | [
"plate.csv",
"protocol.md",
"qc_flags.txt"
] | output/results.csv | file | true | true |
peak-picking-L1 | natural-science | peak-picking | 1 | 16 | Build the peak table for spectrum.csv following sop.md. Write output/peaks.json as a JSON list like [{"position_cm1": 1234.5, "height": 456.7}], sorted by position. | output/peaks.json | [
"sop.md",
"spectrum.csv"
] | output/peaks.json | file | true | true |
peak-picking-L2 | natural-science | peak-picking | 2 | 20 | Build the peak table for spectrum.csv following sop.md. Write output/peaks.json as a JSON list like [{"position_nm": 1234.5, "height": 456.7}], sorted by position. | output/peaks.json | [
"sop.md",
"spectrum.csv"
] | output/peaks.json | file | true | true |
dilution-plan-L1 | natural-science | dilution-plan | 1 | 16 | Write the pipetting plan for targets.csv using the stock in reagent.txt: output/plan.csv with columns tube,stock_uL,diluent_uL (volumes in microlitres). | output/plan.csv | [
"reagent.txt",
"targets.csv"
] | output/plan.csv | file | true | true |
dilution-plan-L2 | natural-science | dilution-plan | 2 | 20 | Write the serial-dilution pipetting plan for series.csv and reagent.txt: output/plan.csv with columns tube,source,transfer_uL,diluent_uL, where source is 'stock' or the tube it is drawn from and volumes are in microlitres. | output/plan.csv | [
"reagent.txt",
"series.csv"
] | output/plan.csv | file | true | true |
pk-fit-L1 | natural-science | pk-fit | 1 | 16 | Run the non-compartmental analysis in sap.md on plasma.csv (RT-2209) and write output/pk.json with keys k_el_per_h, half_life_h, auc_0_last, auc_0_inf (ng*h/mL), cmax (ng/mL) and tmax_h. | output/pk.json | [
"plasma.csv",
"sap.md"
] | output/pk.json | file | true | true |
pk-fit-L2 | natural-science | pk-fit | 2 | 20 | Run the non-compartmental analysis in sap.md on plasma.csv (candidate A) and write output/pk.json with keys k_el_per_h, half_life_h, auc_0_last, auc_0_inf (ng*h/mL), cmax (ng/mL) and tmax_h. | output/pk.json | [
"dosing.txt",
"plasma.csv",
"sap.md"
] | output/pk.json | file | true | true |
linear-system-L1 | mathematics-or-formal-reasoning | linear-system | 1 | 16 | Solve the linear system in equations.txt for x1, x2, x3, x4. Write output/solution.json mapping each variable to its exact value as a string, e.g. {"x1": "-7/3"}. | output/solution.json | [
"equations.txt"
] | output/solution.json | file | true | true |
linear-system-L2 | mathematics-or-formal-reasoning | linear-system | 2 | 20 | Solve the linear system in equations.txt for x1, x2, x3, x4, x5, x6 (read notes.txt first). Write output/solution.json mapping each variable to its exact value as a string, e.g. {"x1": "-7/3"}. | output/solution.json | [
"equations.txt",
"notes.txt"
] | output/solution.json | file | true | true |
recurrence-id-L1 | mathematics-or-formal-reasoning | recurrence-id | 1 | 16 | sequence.txt holds the first terms of an integer sequence that satisfies a linear recurrence a_n = c_1 a_{n-1} + ... + c_k a_{n-k} with constant integer coefficients. Find the one with the smallest order k and write output/answer.json as {"coefficients": [c_1, ..., c_k], "next": [the next 5 terms]}. | output/answer.json | [
"sequence.txt"
] | output/answer.json | file | true | true |
recurrence-id-L2 | mathematics-or-formal-reasoning | recurrence-id | 2 | 20 | sequence.txt holds the first terms of an integer sequence that satisfies a recurrence a_n = c_1 a_{n-1} + ... + c_k a_{n-k} + d with constant integer coefficients and a constant integer d. Find the form with the smallest k (d may be 0) and write output/answer.json as {"coefficients": [c_1, ..., c_k], "constant": d, "te... | output/answer.json | [
"sequence.txt"
] | output/answer.json | file | true | true |
claim-check-L1 | mathematics-or-formal-reasoning | claim-check | 1 | 16 | Check each claim in claims.txt exactly (by exhaustive computation over its stated range). Write output/verdicts.json mapping each claim id to {"holds": true} or {"holds": false, "counterexample": n}, where n is the SMALLEST n in the claim's range for which it fails. | output/verdicts.json | [
"claims.txt"
] | output/verdicts.json | file | true | true |
claim-check-L2 | mathematics-or-formal-reasoning | claim-check | 2 | 20 | Check each claim in claims.txt exactly (by exhaustive computation over its stated range). Write output/verdicts.json mapping each claim id to {"holds": true} or {"holds": false, "counterexample": n}, where n is the SMALLEST n in the claim's range for which it fails. | output/verdicts.json | [
"claims.txt"
] | output/verdicts.json | file | true | true |
board-expectation-L1 | mathematics-or-formal-reasoning | board-expectation | 1 | 16 | A solo player plays the game in rules.txt. What is the exact expected number of turns until they reach home? Write output/answer.json as {"expected_turns": "p/q"} with a reduced fraction. | output/answer.json | [
"rules.txt"
] | output/answer.json | file | true | true |
board-expectation-L2 | mathematics-or-formal-reasoning | board-expectation | 2 | 20 | A solo player plays the game in rules.txt. What is the exact expected number of turns until they reach home? Write output/answer.json as {"expected_turns": "p/q"} with a reduced fraction. | output/answer.json | [
"rules.txt"
] | output/answer.json | file | true | true |
web-forensics-L1 | cybersecurity | web-forensics | 1 | 16 | access.log is an nginx combined log. One client is running a vulnerability scanner (it requests many different sensitive paths and almost all of them return 404). Write output/incident.json with keys attacker_ip, first_seen and last_seen (that client's first and last request, ISO 8601 UTC like 2026-03-01T14:05:09Z), re... | output/incident.json | [
"access.log"
] | output/incident.json | file | true | true |
web-forensics-L2 | cybersecurity | web-forensics | 2 | 20 | access.log is a merged nginx combined log (lines are NOT in time order; timestamps carry the server's UTC offset). Exactly one client attempted SQL injection (UNION SELECT, tautologies like ' OR 1=1, stacked queries, SLEEP, comment terminators). Write output/incident.json with keys attacker_ip, first_seen and last_seen... | output/incident.json | [
"access.log"
] | output/incident.json | file | true | true |
decode-chain-L1 | cybersecurity | decode-chain | 1 | 16 | Decode every blob in artefacts.txt (notes.txt has the key) and crack every hash against wordlist.txt. Write output/decoded.json as {"blobs": {"B1": "plaintext", ...}, "hashes": {"H1": {"algorithm": "md5", "password": "..."}, ...}} (algorithm one of md5, sha1, sha256, sha512). | output/decoded.json | [
"artefacts.txt",
"notes.txt",
"wordlist.txt"
] | output/decoded.json | file | true | true |
decode-chain-L2 | cybersecurity | decode-chain | 2 | 20 | Decode every blob in artefacts.txt (notes.txt has the key) and crack every hash against wordlist.txt. Write output/decoded.json as {"blobs": {"B1": "plaintext", ...}, "hashes": {"H1": {"algorithm": "md5", "password": "..."}, ...}} (algorithm one of md5, sha1, sha256, sha512). | output/decoded.json | [
"artefacts.txt",
"notes.txt",
"wordlist.txt"
] | output/decoded.json | file | true | true |
sshd-audit-L1 | cybersecurity | sshd-audit | 1 | 14 | Audit sshd_config against baseline.md. Write output/findings.json as {"failing": ["SSH-02", ...]} listing the ids of every failing rule, sorted. | output/findings.json | [
"baseline.md",
"sshd_config"
] | output/findings.json | file | true | true |
sshd-audit-L2 | cybersecurity | sshd-audit | 2 | 18 | Audit sshd_config against baseline.md. Write output/findings.json as {"failing": ["SSH-02", ...]} listing the ids of every failing rule, sorted. | output/findings.json | [
"baseline.md",
"sshd_config"
] | output/findings.json | file | true | true |
jwt-inspect-L1 | cybersecurity | jwt-inspect | 1 | 16 | Inspect every JWT in tokens.txt using the rules in verifier.md. Write output/report.json mapping each token id to {"valid": true/false, "reason": "<code>", "sub": "<sub claim>"}. | output/report.json | [
"tokens.txt",
"verifier.md"
] | output/report.json | file | true | true |
jwt-inspect-L2 | cybersecurity | jwt-inspect | 2 | 20 | Inspect every JWT in tokens.txt using the rules in verifier.md. Write output/report.json mapping each token id to {"valid": true/false, "reason": "<code>", "sub": "<sub claim>"}. | output/report.json | [
"tokens.txt",
"verifier.md"
] | output/report.json | file | true | true |
srt-retime-L1 | media-content-production | srt-retime | 1 | 14 | subs.srt is 2656 ms late against the video: every subtitle must be shown 2656 ms earlier. Write the corrected file to output/out.srt (same cues and text, standard SRT timestamps HH:MM:SS,mmm). | output/out.srt | [
"subs.srt"
] | output/out.srt | file | true | true |
srt-retime-L2 | media-content-production | srt-retime | 2 | 20 | Retime subs.srt for the new video cut using sync.txt and style.md, and write the deliverable subtitle file to output/out.srt. | output/out.srt | [
"style.md",
"subs.srt",
"sync.txt"
] | output/out.srt | file | true | true |
image-audit-L1 | media-content-production | image-audit | 1 | 16 | Audit every image under assets/ against spec.md. Write output/report.csv with columns file,format,width,height,ok,reasons: file as listed (e.g. assets/hero.jpg), format as JPEG/PNG/GIF/WEBP, displayed width and height in px, ok as true/false, and reasons as the failing reason codes joined by ';' in spec order (empty wh... | output/report.csv | [
"assets/avatar.jpg",
"assets/banner.jpg",
"assets/card.png",
"assets/gallery-1.png",
"assets/hero.jpg",
"assets/logo.jpg",
"spec.md"
] | output/report.csv | file | true | true |
image-audit-L2 | media-content-production | image-audit | 2 | 20 | Audit every image under assets/ against spec.md. Write output/report.csv with columns file,format,width,height,ok,reasons: file as listed (e.g. assets/hero.jpg), format as JPEG/PNG/GIF/WEBP, displayed width and height in px, ok as true/false, and reasons as the failing reason codes joined by ';' in spec order (empty wh... | output/report.csv | [
"assets/avatar.jpg",
"assets/banner.png",
"assets/card.jpg",
"assets/gallery-1.jpg",
"assets/hero.gif",
"assets/logo.gif",
"spec.md"
] | output/report.csv | file | true | true |
audio-audit-L1 | media-content-production | audio-audit | 1 | 16 | Measure every WAV under audio/ as spec.md defines and write output/audio_report.csv with columns file,sample_rate,channels,duration_s,peak_dbfs (one row per file, file as listed, e.g. audio/intro.wav). | output/audio_report.csv | [
"audio/intro.wav",
"audio/outro.wav",
"audio/sfx_door.wav",
"audio/sting.wav",
"audio/vo_take2.wav",
"spec.md"
] | output/audio_report.csv | file | true | true |
audio-audit-L2 | media-content-production | audio-audit | 2 | 20 | Measure every WAV under audio/ as spec.md defines and write output/audio_report.csv with columns file,sample_rate,channels,duration_s,peak_dbfs,clipped_samples,clipping (one row per file, file as listed, e.g. audio/intro.wav; clipping as true/false). | output/audio_report.csv | [
"audio/intro.wav",
"audio/outro.wav",
"audio/sfx_door.wav",
"audio/sting.wav",
"audio/vo_take2.wav",
"spec.md"
] | output/audio_report.csv | file | true | true |
markdown-html-L1 | media-content-production | markdown-html | 1 | 16 | Convert article.md to HTML following style.md exactly and write it to output/article.html. | output/article.html | [
"article.md",
"style.md"
] | output/article.html | file | true | true |
markdown-html-L2 | media-content-production | markdown-html | 2 | 20 | Convert article.md to HTML following style.md exactly and write it to output/article.html. | output/article.html | [
"article.md",
"style.md"
] | output/article.html | file | true | true |
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